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BioScientist/LICENSE

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+MIT License
+
+Copyright (c) 2025 florensiawidjaja
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

BioScientist/agent_system/engines/v1_executor_backup/biorxiv_scripts/extract_biorxiv_tasks.py

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+#!/usr/bin/env python3
+"""Script to extract common tasks, databases, and software from bioRxiv papers
+using the PaperTaskExtractor and existing metadata CSV.
+"""
+
+import argparse
+import io
+import json
+import os
+import random
+import sys
+import time
+from typing import Any
+
+import pandas as pd
+import PyPDF2
+import requests
+from tqdm import tqdm
+
+# Add the parent directory to the path so we can import the bioagentos package
+sys.path.append("../../")
+from biomni.agent.env_collection import PaperTaskExtractor
+
+
+def parse_arguments():
+    """Parse command line arguments."""
+    parser = argparse.ArgumentParser(description="Extract tasks from bioRxiv papers in a specific subject area.")
+    parser.add_argument(
+        "--subject",
+        type=str,
+        required=True,
+        help='Subject area to search for papers (e.g., "neuroscience", "bioinformatics")',
+    )
+    parser.add_argument(
+        "--limit",
+        type=int,
+        default=10,
+        help="Maximum number of papers to process (default: 10)",
+    )
+    parser.add_argument(
+        "--metadata-path",
+        type=str,
+        default="/dfs/user/kexinh/BioAgentOS/data/biorxiv_metadata.csv",
+        help="Path to bioRxiv metadata CSV file (default: data/biorxiv_metadata.csv)",
+    )
+    parser.add_argument(
+        "--output-dir",
+        type=str,
+        default=None,
+        help="Directory to save results (default: ./biorxiv_results_{subject}_{limit})",
+    )
+    parser.add_argument(
+        "--model",
+        type=str,
+        default="claude-3-haiku-20240307",
+        help="LLM model to use for extraction (default: claude-3-haiku-20240307)",
+    )
+    parser.add_argument(
+        "--chunk-size",
+        type=int,
+        default=4000,
+        help="Chunk size for text processing (default: 4000)",
+    )
+    parser.add_argument(
+        "--chunk-overlap",
+        type=int,
+        default=400,
+        help="Chunk overlap for text processing (default: 400)",
+    )
+    parser.add_argument(
+        "--max-paper-length",
+        type=int,
+        default=200000,
+        help="Maximum paper length in characters (default: 200000)",
+    )
+    parser.add_argument("--save-pdfs", action="store_true", help="Save downloaded PDFs (default: False)")
+    parser.add_argument(
+        "--random-sample",
+        action="store_true",
+        help="Randomly sample papers instead of taking the first N (default: False)",
+    )
+    return parser.parse_args()
+
+
+def load_papers_from_csv(metadata_path: str, subject: str, limit: int, random_sample: bool) -> pd.DataFrame:
+    """Load papers from bioRxiv metadata CSV file.
+
+    Args:
+        metadata_path: Path to the metadata CSV file
+        subject: Subject area to filter by
+        limit: Maximum number of papers to return
+        random_sample: Whether to randomly sample papers
+
+    Returns:
+        DataFrame of filtered papers
+
+    """
+    try:
+        # Load the metadata CSV
+        df_biorxiv = pd.read_csv(metadata_path)
+
+        # Filter published papers in the specified subject
+        if subject.lower() == "all":
+            filtered_df = df_biorxiv[df_biorxiv.published != "NA"]
+        else:
+            filtered_df = df_biorxiv[
+                (df_biorxiv.published != "NA") & (df_biorxiv.category.str.lower() == subject.lower())
+            ]
+
+        # Sample papers
+        if len(filtered_df) > limit:
+            filtered_df = filtered_df.sample(limit, random_state=42) if random_sample else filtered_df.head(limit)
+
+        print(f"Loaded {len(filtered_df)} papers in subject: {subject}")
+        return filtered_df
+
+    except Exception as e:
+        print(f"Error loading papers from CSV: {e}")
+        return pd.DataFrame()
+
+
+def download_pdf(paper_doi: str, save_path: str | None = None) -> str | None:
+    """Download PDF for a given paper DOI.
+
+    Args:
+        paper_doi: DOI of the paper
+        save_path: Path to save the PDF (if None, won't save to disk)
+
+    Returns:
+        PDF text content or None if download failed
+
+    """
+    # Construct PDF URL from DOI
+    pdf_url = f"https://www.biorxiv.org/content/{paper_doi}v1.full.pdf"
+
+    try:
+        # Add random delay to avoid rate limiting
+        time.sleep(random.uniform(1, 3))
+
+        response = requests.get(pdf_url, stream=True)
+        response.raise_for_status()
+
+        # Save PDF if requested
+        if save_path:
+            os.makedirs(os.path.dirname(save_path), exist_ok=True)
+            with open(save_path, "wb") as f:
+                for chunk in response.iter_content(chunk_size=8192):
+                    f.write(chunk)
+
+        # Extract text from PDF
+        pdf_text = extract_text_from_pdf(io.BytesIO(response.content))
+        return pdf_text
+
+    except requests.exceptions.RequestException as e:
+        print(f"Error downloading PDF for {paper_doi}: {e}")
+        return None
+
+
+def extract_text_from_pdf(pdf_file) -> str:
+    """Extract text content from a PDF file.
+
+    Args:
+        pdf_file: File-like object containing PDF data
+
+    Returns:
+        Extracted text content
+
+    """
+    try:
+        pdf_reader = PyPDF2.PdfReader(pdf_file)
+        text = ""
+        for page_num in range(len(pdf_reader.pages)):
+            page = pdf_reader.pages[page_num]
+            text += page.extract_text() + "\n\n"
+        return text
+    except Exception as e:
+        print(f"Error extracting text from PDF: {e}")
+        return ""
+
+
+def truncate_text(text: str, max_length: int) -> str:
+    """Truncate text to a maximum length while preserving complete sentences.
+
+    Args:
+        text: The text to truncate
+        max_length: Maximum length in characters
+
+    Returns:
+        Truncated text
+
+    """
+    if len(text) <= max_length:
+        return text
+
+    # Find the last sentence boundary before max_length
+    truncated = text[:max_length]
+    last_period = truncated.rfind(".")
+    last_question = truncated.rfind("?")
+    last_exclamation = truncated.rfind("!")
+
+    # Find the last sentence boundary
+    last_boundary = max(last_period, last_question, last_exclamation)
+
+    if last_boundary > 0:
+        return text[: last_boundary + 1]
+    else:
+        # If no sentence boundary found, just truncate at max_length
+        return truncated
+
+
+def process_papers(papers_df: pd.DataFrame, args) -> list[dict[str, Any]]:
+    """Process papers using PaperTaskExtractor.
+
+    Args:
+        papers_df: DataFrame of paper metadata
+        args: Command line arguments
+
+    Returns:
+        List of papers with extracted tasks, databases, and software
+
+    """
+    # Initialize the task extractor
+    extractor = PaperTaskExtractor(llm=args.model, chunk_size=args.chunk_size, chunk_overlap=args.chunk_overlap)
+
+    results = []
+
+    for _, paper in tqdm(papers_df.iterrows(), total=len(papers_df), desc="Processing papers"):
+        paper_doi = paper.get("doi")
+        if not paper_doi:
+            continue
+
+        print(f"\nProcessing paper: {paper.get('title')} (DOI: {paper_doi})")
+
+        # Create paths
+        pdf_path = None
+        if args.save_pdfs:
+            pdf_path = os.path.join(args.output_dir, "pdfs", f"{paper_doi.replace('/', '_')}.pdf")
+
+        result_path = os.path.join(args.output_dir, "results", f"{paper_doi.replace('/', '_')}.json")
+
+        # Skip if already processed
+        if os.path.exists(result_path):
+            print(f"Paper already processed, loading from {result_path}")
+            with open(result_path) as f:
+                paper_result = json.load(f)
+                results.append(paper_result)
+            continue
+
+        # Download and process PDF
+        pdf_text = download_pdf(paper_doi, pdf_path)
+        if not pdf_text:
+            print(f"Failed to download or extract text from {paper_doi}")
+            continue
+
+        # Truncate text if it exceeds max length
+        if len(pdf_text) > args.max_paper_length:
+            original_length = len(pdf_text)
+            pdf_text = truncate_text(pdf_text, args.max_paper_length)
+            print(f"Truncated paper from {original_length} to {len(pdf_text)} characters")
+
+        # Extract tasks, databases, and software
+        try:
+            _, extraction_results = extractor.go(pdf_text)
+
+            # Add paper metadata to results
+            paper_result = {
+                "metadata": {
+                    "doi": paper.get("doi"),
+                    "title": paper.get("title"),
+                    "authors": paper.get("authors", ""),
+                    "abstract": paper.get("abstract", ""),
+                    "date": paper.get("date", ""),
+                    "category": paper.get("category", ""),
+                    "text_length": len(pdf_text),
+                },
+                "extraction": extraction_results,
+            }
+
+            # Save results
+            os.makedirs(os.path.dirname(result_path), exist_ok=True)
+            with open(result_path, "w") as f:
+                json.dump(paper_result, f, indent=2)
+
+            results.append(paper_result)
+
+        except Exception as e:
+            print(f"Error processing paper {paper_doi}: {e}")
+
+    return results
+
+
+def generate_summary(results: list[dict[str, Any]], output_dir: str):
+    """Generate summary of extracted tasks, databases, and software.
+
+    Args:
+        results: List of papers with extraction results
+        output_dir: Directory to save summary
+
+    """
+    # Collect all tasks, databases, and software
+    all_tasks = []
+    all_databases = []
+    all_software = []
+
+    for paper in results:
+        extraction = paper.get("extraction", {})
+
+        # Add paper metadata to each item
+        paper_meta = paper.get("metadata", {})
+        paper_id = f"{paper_meta.get('title')} ({paper_meta.get('doi')})"
+
+        # Process tasks
+        for task in extraction.get("tasks", []):
+            task["paper"] = paper_id
+            all_tasks.append(task)
+
+        # Process databases
+        for db in extraction.get("databases", []):
+            db["paper"] = paper_id
+            all_databases.append(db)
+
+        # Process software
+        for sw in extraction.get("software", []):
+            sw["paper"] = paper_id
+            all_software.append(sw)
+
+    # Create summary dataframes
+    if all_tasks:
+        tasks_df = pd.DataFrame(all_tasks)
+        tasks_df.to_csv(os.path.join(output_dir, "tasks_summary.csv"), index=False)
+
+    if all_databases:
+        db_df = pd.DataFrame(all_databases)
+        db_df.to_csv(os.path.join(output_dir, "databases_summary.csv"), index=False)
+
+    if all_software:
+        sw_df = pd.DataFrame(all_software)
+        sw_df.to_csv(os.path.join(output_dir, "software_summary.csv"), index=False)
+
+    # Generate frequency counts
+    task_counts = {}
+    for task in all_tasks:
+        task_name = task.get("task_name")
+        if task_name:
+            task_counts[task_name] = task_counts.get(task_name, 0) + 1
+
+    db_counts = {}
+    for db in all_databases:
+        db_name = db.get("name")
+        if db_name:
+            db_counts[db_name] = db_counts.get(db_name, 0) + 1
+
+    sw_counts = {}
+    for sw in all_software:
+        sw_name = sw.get("name")
+        if sw_name:
+            sw_counts[sw_name] = sw_counts.get(sw_name, 0) + 1
+
+    # Save frequency counts
+    with open(os.path.join(output_dir, "frequency_summary.json"), "w") as f:
+        json.dump(
+            {"tasks": task_counts, "databases": db_counts, "software": sw_counts},
+            f,
+            indent=2,
+        )
+
+    print(f"Summary files saved to {output_dir}")
+
+
+def main():
+    """Main function to run the script."""
+    args = parse_arguments()
+
+    # Set default output directory if not specified
+    if args.output_dir is None:
+        # Clean subject name for directory name (replace spaces with underscores, lowercase)
+        clean_subject = args.subject.lower().replace(" ", "_").replace("/", "_")
+        args.output_dir = f"./biorxiv_results_{clean_subject}_{args.limit}"
+
+    # Create output directories
+    os.makedirs(args.output_dir, exist_ok=True)
+    os.makedirs(os.path.join(args.output_dir, "results"), exist_ok=True)
+    if args.save_pdfs:
+        os.makedirs(os.path.join(args.output_dir, "pdfs"), exist_ok=True)
+
+    # Load papers from CSV
+    print(f"Loading papers from {args.metadata_path} in subject area: {args.subject}")
+    papers_df = load_papers_from_csv(args.metadata_path, args.subject, args.limit, args.random_sample)
+
+    if len(papers_df) == 0:
+        print(f"No papers found for subject: {args.subject}")
+        return
+
+    # Process papers
+    results = process_papers(papers_df, args)
+
+    # Generate summary
+    generate_summary(results, args.output_dir)
+
+    print("Done!")
+
+
+if __name__ == "__main__":
+    main()

BioScientist/agent_system/engines/v1_executor_backup/biorxiv_scripts/generate_function.py

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+#!/usr/bin/env python3
+"""Command-line tool to generate Python functions from task descriptions using the function_generator agent."""
+
+import argparse
+import json
+import os
+
+from biomni.agent.function_generator import FunctionGenerator
+from tqdm import tqdm
+
+
+def main():
+    """Main function for the command-line tool."""
+    parser = argparse.ArgumentParser(description="Generate Python functions given task descriptions")
+    parser.add_argument(
+        "--task",
+        "-t",
+        type=str,
+        help="JSON file containing a list of task descriptions",
+    )
+    parser.add_argument(
+        "--output-dir",
+        "-o",
+        type=str,
+        default="generated_functions",
+        help="Directory to save the generated functions (default: generated_functions)",
+    )
+    parser.add_argument(
+        "--model",
+        "-m",
+        type=str,
+        default="claude-3-7-sonnet-latest",
+        help="LLM model to use (default: claude-3-7-sonnet-latest)",
+    )
+    parser.add_argument(
+        "--temperature",
+        type=float,
+        default=0.7,
+        help="Temperature setting for the LLM (default: 0.7)",
+    )
+
+    args = parser.parse_args()
+
+    with open(args.task) as f_tasks:
+        task_list = json.load(f_tasks)
+        task_descriptions = list(task_list["tasks"])
+
+    # Create the output directory if it doesn't exist
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    # Initialize the function generator agent
+    function_generator = FunctionGenerator(llm=args.model, temperature=args.temperature)
+
+    if not task_list:
+        print("No tasks found.")
+    else:
+        # tqdm shows a progress bar, file names as description
+        for _i, desc in enumerate(tqdm(task_descriptions, desc="Generating Python scripts given task descriptions"), 1):
+            generated_script_name, generated_codes = function_generator.go(desc)
+
+            # Save results
+            result_path = os.path.join(args.output_dir, generated_script_name)
+
+            os.makedirs(os.path.dirname(result_path), exist_ok=True)
+            with open(result_path, "w") as f:
+                f.write(generated_codes)
+
+        print("DONE")
+
+
+if __name__ == "__main__":
+    main()

BioScientist/agent_system/engines/v1_executor_backup/biorxiv_scripts/process_all_subjects.py

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+#!/usr/bin/env python3
+"""Script to process all bioRxiv subjects and extract computational tasks from the top 100 papers in each."""
+
+import argparse
+import os
+import subprocess
+import sys
+import time
+
+import pandas as pd
+from tqdm import tqdm
+
+# Add the parent directory to the path so we can import the bioagentos package
+sys.path.append("/dfs/user/kexinh/BioAgentOS/")
+
+# List of all bioRxiv subjects
+BIORXIV_SUBJECTS = [
+    "evolutionary biology",
+    "ecology",
+    "neuroscience",
+    "developmental biology",
+    "plant biology",
+    "microbiology",
+    "cancer biology",
+    "immunology",
+    "cell biology",
+    "biochemistry",
+    "genetics",
+    "bioinformatics",
+    "animal behavior and cognition",
+    "biophysics",
+    "genomics",
+    "systems biology",
+    "bioengineering",
+    "molecular biology",
+    "physiology",
+    "zoology",
+    "scientific communication and education",
+    "pathology",
+    "synthetic biology",
+    "paleontology",
+    "pharmacology and toxicology",
+]
+
+
+def parse_arguments():
+    """Parse command line arguments."""
+    parser = argparse.ArgumentParser(description="Process all bioRxiv subjects and extract tasks from papers.")
+    parser.add_argument(
+        "--papers-per-subject",
+        type=int,
+        default=100,
+        help="Number of papers to process per subject (default: 100)",
+    )
+    parser.add_argument(
+        "--metadata-path",
+        type=str,
+        default="/dfs/user/kexinh/BioAgentOS/data/biorxiv_metadata.csv",
+        help="Path to bioRxiv metadata CSV file (default: data/biorxiv_metadata.csv)",
+    )
+    parser.add_argument(
+        "--output-dir",
+        type=str,
+        default="./biorxiv_all_subjects",
+        help="Base directory to save results (default: ./biorxiv_all_subjects)",
+    )
+    parser.add_argument(
+        "--model",
+        type=str,
+        default="claude-3-haiku-20240307",
+        help="LLM model to use for extraction (default: claude-3-haiku-20240307)",
+    )
+    parser.add_argument(
+        "--max-paper-length",
+        type=int,
+        default=200000,
+        help="Maximum paper length in characters (default: 200000)",
+    )
+    parser.add_argument(
+        "--chunk-size",
+        type=int,
+        default=10000,
+        help="Chunk size for text processing (default: 10000)",
+    )
+    parser.add_argument(
+        "--chunk-overlap",
+        type=int,
+        default=400,
+        help="Chunk overlap for text processing (default: 400)",
+    )
+    parser.add_argument("--save-pdfs", action="store_true", help="Save downloaded PDFs (default: False)")
+    parser.add_argument(
+        "--random-sample",
+        action="store_true",
+        help="Randomly sample papers instead of taking the first N (default: False)",
+    )
+    parser.add_argument(
+        "--subjects",
+        type=str,
+        nargs="+",
+        help="Specific subjects to process (default: all subjects)",
+    )
+    parser.add_argument(
+        "--delay",
+        type=int,
+        default=60,
+        help="Delay in seconds between processing subjects (default: 60)",
+    )
+    parser.add_argument(
+        "--summary-only",
+        action="store_true",
+        help="Only generate the combined summary (default: False)",
+    )
+    return parser.parse_args()
+
+
+def check_subject_availability(metadata_path, subjects):
+    """Check which subjects have papers available in the metadata.
+
+    Args:
+        metadata_path: Path to the metadata CSV file
+        subjects: List of subjects to check
+
+    Returns:
+        List of subjects with available papers
+
+    """
+    try:
+        df_biorxiv = pd.read_csv(metadata_path)
+        available_subjects = []
+
+        for subject in subjects:
+            count = len(
+                df_biorxiv[(df_biorxiv.published != "NA") & (df_biorxiv.category.str.lower() == subject.lower())]
+            )
+            if count > 0:
+                available_subjects.append((subject, count))
+                print(f"Subject '{subject}' has {count} papers available")
+            else:
+                print(f"Subject '{subject}' has no papers available")
+
+        return available_subjects
+    except Exception as e:
+        print(f"Error checking subject availability: {e}")
+        return []
+
+
+def process_subject(subject, args):
+    """Process a single subject by calling the extract_biorxiv_tasks.py script.
+
+    Args:
+        subject: Subject to process
+        args: Command line arguments
+
+    """
+    # Clean subject name for command line
+    subject.replace(" ", "\\ ")
+
+    # Build command
+    cmd = [
+        "python",
+        "BioAgentOS/bioagentos/scripts/extract_biorxiv_tasks.py",
+        "--subject",
+        f'"{subject}"',
+        "--limit",
+        str(args.papers_per_subject),
+        "--metadata-path",
+        args.metadata_path,
+        "--model",
+        args.model,
+        "--max-paper-length",
+        str(args.max_paper_length),
+        "--chunk-size",
+        str(args.chunk_size),
+        "--chunk-overlap",
+        str(args.chunk_overlap),
+    ]
+
+    # Add optional arguments
+    if args.save_pdfs:
+        cmd.append("--save-pdfs")
+    if args.random_sample:
+        cmd.append("--random-sample")
+
+    # Convert command list to string
+    cmd_str = " ".join(cmd)
+
+    print(f"\n{'=' * 80}\nProcessing subject: {subject}\n{'=' * 80}")
+    print(f"Running command: {cmd_str}")
+
+    # Execute command
+    try:
+        subprocess.run(cmd_str, shell=True, check=True)
+        print(f"Successfully processed subject: {subject}")
+    except subprocess.CalledProcessError as e:
+        print(f"Error processing subject '{subject}': {e}")
+
+
+def combine_summaries(base_dir, subjects, papers_per_subject):
+    """Combine frequency summaries from all subjects into a single summary.
+
+    Args:
+        base_dir: Base directory containing subject results
+        subjects: List of subjects that were processed
+        papers_per_subject: Number of papers processed per subject
+
+    """
+    all_tasks = {}
+    all_databases = {}
+    all_software = {}
+
+    for subject, _ in subjects:
+        # Clean subject name for directory name
+        clean_subject = subject.lower().replace(" ", "_").replace("/", "_")
+        subject_dir = f"biorxiv_results_{clean_subject}_{papers_per_subject}"
+        summary_path = os.path.join(subject_dir, "frequency_summary.json")
+
+        try:
+            if os.path.exists(summary_path):
+                with open(summary_path) as f:
+                    summary = json.load(f)
+
+                    # Merge tasks
+                    for task, count in summary.get("tasks", {}).items():
+                        if task in all_tasks:
+                            all_tasks[task] += count
+                        else:
+                            all_tasks[task] = count
+
+                    # Merge databases
+                    for db, count in summary.get("databases", {}).items():
+                        if db in all_databases:
+                            all_databases[db] += count
+                        else:
+                            all_databases[db] = count
+
+                    # Merge software
+                    for sw, count in summary.get("software", {}).items():
+                        if sw in all_software:
+                            all_software[sw] += count
+                        else:
+                            all_software[sw] = count
+            else:
+                print(f"Warning: Summary file not found for subject '{subject}'")
+        except Exception as e:
+            print(f"Error processing summary for subject '{subject}': {e}")
+
+    # Sort by frequency (descending)
+    all_tasks = dict(sorted(all_tasks.items(), key=lambda item: item[1], reverse=True))
+    all_databases = dict(sorted(all_databases.items(), key=lambda item: item[1], reverse=True))
+    all_software = dict(sorted(all_software.items(), key=lambda item: item[1], reverse=True))
+
+    # Create combined summary
+    combined_summary = {
+        "tasks": all_tasks,
+        "databases": all_databases,
+        "software": all_software,
+    }
+
+    # Save combined summary
+    combined_path = os.path.join(base_dir, "combined_summary.json")
+    os.makedirs(base_dir, exist_ok=True)
+
+    with open(combined_path, "w") as f:
+        json.dump(combined_summary, f, indent=2)
+
+    print(f"Combined summary saved to {combined_path}")
+
+    # Create CSV files for each category
+    tasks_df = pd.DataFrame(list(all_tasks.items()), columns=["Task", "Frequency"])
+    tasks_df.to_csv(os.path.join(base_dir, "tasks_frequency.csv"), index=False)
+
+    db_df = pd.DataFrame(list(all_databases.items()), columns=["Database", "Frequency"])
+    db_df.to_csv(os.path.join(base_dir, "databases_frequency.csv"), index=False)
+
+    sw_df = pd.DataFrame(list(all_software.items()), columns=["Software", "Frequency"])
+    sw_df.to_csv(os.path.join(base_dir, "software_frequency.csv"), index=False)
+
+    print(f"CSV summaries saved to {base_dir}")
+
+
+def main():
+    """Main function to run the script."""
+    args = parse_arguments()
+
+    # Determine which subjects to process
+    subjects_to_process = args.subjects if args.subjects else BIORXIV_SUBJECTS
+
+    # Check which subjects have papers available
+    available_subjects = check_subject_availability(args.metadata_path, subjects_to_process)
+
+    if not available_subjects:
+        print("No subjects with available papers found. Exiting.")
+        return
+
+    if not args.summary_only:
+        # Process each subject
+        for subject, _count in tqdm(available_subjects, desc="Processing subjects"):
+            process_subject(subject, args)
+
+            # Add delay between subjects to avoid rate limiting
+            if args.delay > 0:
+                print(f"Waiting {args.delay} seconds before processing the next subject...")
+                time.sleep(args.delay)
+
+    # Combine summaries from all subjects
+    combine_summaries(args.output_dir, available_subjects, args.papers_per_subject)
+
+    print("All subjects processed successfully!")
+
+
+if __name__ == "__main__":
+    import json  # Import here to avoid circular import with the main script
+
+    main()

BioScientist/agent_system/engines/v1_executor_backup/know_how/__init__.py

@@ -0,0 +1,9 @@
+"""Know-How library for Biomni agent.
+
+This module provides functionality to load and manage know-how documents
+that contain best practices, protocols, and troubleshooting guides.
+"""
+
+from .loader import KnowHowLoader
+
+__all__ = ["KnowHowLoader"]

BioScientist/agent_system/engines/v1_executor_backup/know_how/loader.py

@@ -0,0 +1,348 @@
+"""Loader for know-how documents."""
+
+import glob
+import os
+from pathlib import Path
+
+
+class KnowHowLoader:
+    """Load and manage know-how documents for the agent."""
+
+    def __init__(self, know_how_dir: str | None = None):
+        """Initialize the know-how loader.
+
+        Args:
+            know_how_dir: Directory containing know-how documents.
+                         If None, uses the default know_how directory.
+
+        """
+        if know_how_dir is None:
+            # Default to the know_how directory in the package
+            current_dir = Path(__file__).parent
+            know_how_dir = str(current_dir)
+
+        self.know_how_dir = know_how_dir
+        self.documents = {}
+        self._load_documents()
+
+    def _load_documents(self):
+        """Load all markdown documents from the know-how directory."""
+        pattern = os.path.join(self.know_how_dir, "*.md")
+        md_files = glob.glob(pattern)
+
+        for filepath in md_files:
+            filename = os.path.basename(filepath)
+            filename_without_ext = os.path.splitext(filename)[0]
+
+            # Skip README, QUICK_START, and other meta documentation (all caps filenames)
+            if filename.upper() in ["README.MD", "QUICK_START.MD"] or filename_without_ext.isupper():
+                continue
+
+            # Read the document
+            with open(filepath) as f:
+                content = f.read()
+
+            # Extract title, description, and metadata from the document
+            title, description, metadata = self._extract_metadata(content, filename)
+
+            # Use short_description from metadata if available, otherwise fall back to extracted description
+            if "short_description" in metadata and metadata["short_description"]:
+                description = metadata["short_description"]
+
+            # Store document info
+            doc_id = os.path.splitext(filename)[0]
+            self.documents[doc_id] = {
+                "id": doc_id,
+                "name": title,
+                "description": description,
+                "content": content,
+                "content_without_metadata": self._strip_metadata(content),
+                "filepath": filepath,
+                "metadata": metadata,
+            }
+
+    def _extract_metadata(self, content: str, filename: str) -> tuple[str, str, dict]:
+        """Extract title, description, and metadata from markdown content.
+
+        Args:
+            content: Markdown content
+            filename: Filename (used as fallback for title)
+
+        Returns:
+            Tuple of (title, description, metadata_dict)
+
+        """
+        lines = content.split("\n")
+
+        # Extract title (first h1)
+        title = None
+        for line in lines:
+            if line.startswith("# "):
+                title = line[2:].strip()
+                break
+
+        if title is None:
+            # Fallback to filename
+            title = filename.replace("_", " ").replace(".md", "").title()
+
+        # Extract metadata section
+        metadata = {}
+        in_metadata = False
+        current_field = None
+
+        for _i, line in enumerate(lines):
+            if line.startswith("## Metadata"):
+                in_metadata = True
+                continue
+            elif in_metadata:
+                if line.startswith("##") and "Metadata" not in line:
+                    # End of metadata section
+                    break
+                elif line.startswith("**") and "**:" in line:
+                    # New metadata field (e.g., **Authors**:)
+                    field_match = line.split("**")[1]
+                    current_field = field_match.lower().replace(" ", "_")
+                    # Get the value after the colon if it exists on the same line
+                    colon_idx = line.find("**:")
+                    if colon_idx != -1:
+                        value_part = line[colon_idx + 3 :].strip()
+                        if value_part:
+                            metadata[current_field] = value_part
+                        else:
+                            metadata[current_field] = ""
+                elif current_field and line.strip() and not line.startswith("---"):
+                    # Continuation of current field
+                    if current_field not in metadata:
+                        metadata[current_field] = ""
+                    if line.startswith("- "):
+                        # List item
+                        if metadata[current_field]:
+                            metadata[current_field] += ", " + line[2:].strip()
+                        else:
+                            metadata[current_field] = line[2:].strip()
+                    elif not line.startswith("```"):
+                        # Regular text
+                        if metadata[current_field]:
+                            metadata[current_field] += " " + line.strip()
+                        else:
+                            metadata[current_field] = line.strip()
+
+        # Extract description (content under ## Overview or first paragraph)
+        description = ""
+        in_overview = False
+        overview_lines = []
+
+        for _i, line in enumerate(lines):
+            if line.startswith("## Overview"):
+                in_overview = True
+                continue
+            elif in_overview:
+                if line.startswith("##"):
+                    # End of overview section
+                    break
+                elif line.strip():
+                    overview_lines.append(line.strip())
+
+        if overview_lines:
+            description = " ".join(overview_lines)
+        else:
+            # Fallback: get first non-empty paragraph after title
+            found_title = False
+            for line in lines:
+                if line.startswith("# "):
+                    found_title = True
+                    continue
+                if found_title and line.strip() and not line.startswith("#"):
+                    description = line.strip()
+                    break
+
+        # Limit description length
+        if len(description) > 200:
+            description = description[:197] + "..."
+
+        return title, description, metadata
+
+    def _strip_metadata(self, content: str) -> str:
+        """Strip the metadata section from document content.
+
+        Args:
+            content: Full document content with metadata
+
+        Returns:
+            Content without metadata section
+
+        """
+        lines = content.split("\n")
+        result_lines = []
+        in_metadata = False
+        skip_until_separator = False
+        found_first_h1 = False
+
+        for line in lines:
+            # Track first H1 (title)
+            if line.startswith("# ") and not found_first_h1:
+                result_lines.append(line)
+                found_first_h1 = True
+                continue
+
+            # Detect metadata section start
+            if line.startswith("## Metadata"):
+                in_metadata = True
+                continue
+
+            # Skip separator lines before and after metadata
+            if line.strip() == "---":
+                if not in_metadata:
+                    # This might be the separator before metadata
+                    skip_until_separator = True
+                    continue
+                else:
+                    # This is the separator after metadata
+                    in_metadata = False
+                    skip_until_separator = False
+                    continue
+
+            # Skip lines in metadata section
+            if in_metadata or skip_until_separator:
+                # Check if we hit another H2 (end of metadata)
+                if line.startswith("##") and "Metadata" not in line:
+                    in_metadata = False
+                    skip_until_separator = False
+                    result_lines.append(line)
+                continue
+
+            # Keep all other lines
+            result_lines.append(line)
+
+        # Join and clean up extra blank lines
+        result = "\n".join(result_lines)
+
+        # Remove excessive blank lines (more than 2 consecutive)
+        while "\n\n\n\n" in result:
+            result = result.replace("\n\n\n\n", "\n\n\n")
+
+        return result.strip()
+
+    def get_all_documents(self) -> list[dict]:
+        """Get all know-how documents as a list.
+
+        Returns:
+            List of document dictionaries with keys: id, name, description, content
+
+        """
+        return list(self.documents.values())
+
+    def get_document_by_id(self, doc_id: str) -> dict | None:
+        """Get a specific know-how document by ID.
+
+        Args:
+            doc_id: Document identifier
+
+        Returns:
+            Document dictionary or None if not found
+
+        """
+        return self.documents.get(doc_id)
+
+    def get_document_summaries(self) -> list[dict]:
+        """Get summaries of all documents (without full content).
+
+        Returns:
+            List of document summaries with keys: id, name, description
+
+        """
+        return [
+            {"id": doc["id"], "name": doc["name"], "description": doc["description"]} for doc in self.documents.values()
+        ]
+
+    def add_custom_document(self, doc_id: str, name: str, description: str, content: str, metadata: dict | None = None):
+        """Add a custom know-how document programmatically.
+
+        Args:
+            doc_id: Unique identifier for the document
+            name: Document title
+            description: Brief description
+            content: Full document content
+            metadata: Optional metadata dictionary (authors, license, etc.)
+
+        """
+        if metadata is None:
+            metadata = {}
+
+        self.documents[doc_id] = {
+            "id": doc_id,
+            "name": name,
+            "description": description,
+            "content": content,
+            "filepath": None,
+            "metadata": metadata,
+        }
+
+    def get_document_metadata(self, doc_id: str) -> dict | None:
+        """Get metadata for a specific document.
+
+        Args:
+            doc_id: Document identifier
+
+        Returns:
+            Metadata dictionary or None if not found
+
+        """
+        doc = self.documents.get(doc_id)
+        return doc.get("metadata", {}) if doc else None
+
+    def print_document_info(self, doc_id: str):
+        """Print formatted information about a document including metadata.
+
+        Args:
+            doc_id: Document identifier
+
+        """
+        doc = self.documents.get(doc_id)
+        if not doc:
+            print(f"Document '{doc_id}' not found")
+            return
+
+        print("=" * 70)
+        print(f"📚 {doc['name']}")
+        print("=" * 70)
+        print(f"\nDescription: {doc['description']}")
+
+        metadata = doc.get("metadata", {})
+        if metadata:
+            print("\n" + "-" * 70)
+            print("METADATA")
+            print("-" * 70)
+
+            # Display key metadata fields
+            if "authors" in metadata:
+                print(f"Authors: {metadata['authors']}")
+            if "affiliations" in metadata:
+                print(f"Affiliations: {metadata['affiliations']}")
+            if "version" in metadata:
+                print(f"Version: {metadata['version']}")
+            if "last_updated" in metadata:
+                print(f"Last Updated: {metadata['last_updated']}")
+            if "license" in metadata:
+                print(f"License: {metadata['license']}")
+            if "commercial_use" in metadata:
+                print(f"Commercial Use: {metadata['commercial_use']}")
+            if "status" in metadata:
+                print(f"Status: {metadata['status']}")
+
+        print("=" * 70)
+
+    def remove_document(self, doc_id: str):
+        """Remove a know-how document.
+
+        Args:
+            doc_id: Document identifier
+
+        """
+        if doc_id in self.documents:
+            del self.documents[doc_id]
+
+    def reload(self):
+        """Reload all documents from disk."""
+        self.documents = {}
+        self._load_documents()

BioScientist/agent_system/engines/v1_executor_backup/know_how/resource/CRISPick_download_links.txt

@@ -0,0 +1,238 @@
+https://portals.broadinstitute.org/gppx/public/sgrna_design/api/downloads/sgRNA_design_8030_ICSASG_v2_SpyoCas9_CRISPRa_RS3seq-Chen2013_NCBI_20230421.txt.gz
+https://portals.broadinstitute.org/gppx/public/sgrna_design/api/downloads/sgRNA_design_8030_ICSASG_v2_SpyoCas9_CRISPRa_RS3seq-Chen2013_NCBI_20230421.summary.txt
+https://portals.broadinstitute.org/gppx/public/sgrna_design/api/downloads/sgRNA_design_8030_ICSASG_v2_SpyoCas9_CRISPRko_RS3seq-Chen2013+RS3target_NCBI_20230421.txt.gz
+https://portals.broadinstitute.org/gppx/public/sgrna_design/api/downloads/sgRNA_design_8030_ICSASG_v2_SpyoCas9_CRISPRko_RS3seq-Chen2013+RS3target_NCBI_20230421.summary.txt
+https://portals.broadinstitute.org/gppx/public/sgrna_design/api/downloads/sgRNA_design_10029_CHOK1GS_HDv1_SaurCas9_CRISPRa_Azimuth_2.0_Ensembl_20241114.txt.gz
+https://portals.broadinstitute.org/gppx/public/sgrna_design/api/downloads/sgRNA_design_10029_CHOK1GS_HDv1_SaurCas9_CRISPRa_Azimuth_2.0_Ensembl_20241114.summary.txt
+https://portals.broadinstitute.org/gppx/public/sgrna_design/api/downloads/sgRNA_design_10029_CHOK1GS_HDv1_SaurCas9_CRISPRi_Azimuth_2.0_Ensembl_20241114.txt.gz
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+https://portals.broadinstitute.org/gppx/public/sgrna_design/api/downloads/sgRNA_design_10029_CHOK1GS_HDv1_SaurCas9_CRISPRko_Azimuth_2.0_Ensembl_20241114.txt.gz
+https://portals.broadinstitute.org/gppx/public/sgrna_design/api/downloads/sgRNA_design_10029_CHOK1GS_HDv1_SaurCas9_CRISPRko_Azimuth_2.0_Ensembl_20241114.summary.txt
+https://portals.broadinstitute.org/gppx/public/sgrna_design/api/downloads/sgRNA_design_10029_CHOK1GS_HDv1_SpyoCas9_CRISPRa_RS3seq-Chen2013_Ensembl_20241114.txt.gz
+https://portals.broadinstitute.org/gppx/public/sgrna_design/api/downloads/sgRNA_design_10029_CHOK1GS_HDv1_SpyoCas9_CRISPRa_RS3seq-Chen2013_Ensembl_20241114.summary.txt
+https://portals.broadinstitute.org/gppx/public/sgrna_design/api/downloads/sgRNA_design_10029_CHOK1GS_HDv1_SpyoCas9_CRISPRko_RS3seq-Chen2013+RS3target_Ensembl_20241114.txt.gz
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+https://portals.broadinstitute.org/gppx/public/sgrna_design/api/downloads/sgRNA_design_74940_Otsh_v1.0_SpyoCas9_CRISPRa_RS3seq-Chen2013_NCBI_20230424.txt.gz
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+https://portals.broadinstitute.org/gppx/public/sgrna_design/api/downloads/sgRNA_design_74940_Otsh_v1.0_SpyoCas9_CRISPRko_RS3seq-Chen2013+RS3target_NCBI_20230424.txt.gz
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+https://portals.broadinstitute.org/gppx/public/sgrna_design/api/downloads/sgRNA_design_9913_ARS-UCD1.2_SpyoCas9_CRISPRa_RS3seq-Chen2013_NCBI_20230418.txt.gz
+https://portals.broadinstitute.org/gppx/public/sgrna_design/api/downloads/sgRNA_design_9913_ARS-UCD1.2_SpyoCas9_CRISPRa_RS3seq-Chen2013_NCBI_20230418.summary.txt
+https://portals.broadinstitute.org/gppx/public/sgrna_design/api/downloads/sgRNA_design_9913_ARS-UCD1.2_SpyoCas9_CRISPRko_RS3seq-Chen2013+RS3target_NCBI_20230418.txt.gz
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+https://portals.broadinstitute.org/gppx/public/sgrna_design/api/downloads/sgRNA_design_9615_ROS_Cfam_1.0_AsCas12a_CRISPRko_Seq-DeepCpf1_Ensembl_20251031.txt.gz
+https://portals.broadinstitute.org/gppx/public/sgrna_design/api/downloads/sgRNA_design_9615_ROS_Cfam_1.0_AsCas12a_CRISPRko_Seq-DeepCpf1_Ensembl_20251031.summary.txt
+https://portals.broadinstitute.org/gppx/public/sgrna_design/api/downloads/sgRNA_design_9615_ROS_Cfam_1.0_AsCas12a_CRISPRko_Seq-DeepCpf1_NCBI_20240611.txt.gz
+https://portals.broadinstitute.org/gppx/public/sgrna_design/api/downloads/sgRNA_design_9615_ROS_Cfam_1.0_AsCas12a_CRISPRko_Seq-DeepCpf1_NCBI_20240611.summary.txt
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+https://portals.broadinstitute.org/gppx/public/sgrna_design/api/downloads/sgRNA_design_9615_ROS_Cfam_1.0_enAsCas12a_CRISPRa_enPAM+GB_NCBI_20240611.txt.gz
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BioScientist/agent_system/engines/v1_executor_backup/know_how/resource/addgene_grna_sequences.csv

@@ -0,0 +1,322 @@
+Application,Cas9 Species,Depositor,Plasmid ID,PubMed ID,Target Gene,Target Sequence,Target Species
+cut,S. pyogenes,Goncalves,"<a href=""/58252/"">58252</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24870050/"">24870050</a>",AAVS1,GGGGCCACTAGGGACAGGAT,H. sapiens
+cut,S. pyogenes,Church,"<a href=""/41818/"">41818</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23287722/"">23287722</a>",AAVS1,GGGGCCACTAGGGACAGGAT,H. sapiens
+cut,S. pyogenes,Sabatini,"<a href=""/50662/"">50662</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24336569/"">24336569</a>",AAVS1,GGGGCCACTAGGGACAGGAT,H. sapiens
+cut,S. pyogenes,Sabatini,"<a href=""/70661/"">70661</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26472758/"">26472758</a>",AAVS1,GGGGCCACTAGGGACAGGAT,H. sapiens
+cut,S. pyogenes,Church,"<a href=""/41817/"">41817</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23287722/"">23287722</a>",AAVS1,GTCCCCTCCACCCCACAGTG,H. sapiens
+cut,S. pyogenes,Kanemaki,"<a href=""/72833/"">72833</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/27052166/"">27052166</a>",AAVS1,GGGGCCACTAGGGACAGGAT,H. sapiens
+cut,S. pyogenes,Corn,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26789497/"">26789497</a>",AAVS1,TGTCCCTAGTGGCCCCACTG,H. sapiens
+cut,S. pyogenes,Corn,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26789497/"">26789497</a>",AAVS1,ACAGTGGGGCCACTAGGGAC,H. sapiens
+RNA targeting,S. pyogenes,Yeo,"<a href=""/74705/"">74705</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26997482/"">26997482</a>",ACTB,UGGAGCGAGCAUCCCCCAAA,H. sapiens
+cut,S. pyogenes,Jiang,"<a href=""/62552/"">62552</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25739462/"">25739462</a>",actII-orf4,ATTACCAGGGACCGGAGTTC,S. coelicolor
+cut,S. pyogenes,Zaratiegui,"<a href=""/52225/"">52225</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25352017/"">25352017</a>",ade6+,TCTATTGTTCAGATGCCTCG,S. pombe
+cut,S. pyogenes,Zaratiegui,"<a href=""/52227/"">52227</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25352017/"">25352017</a>",ade6-L469,TCTATTGTTCAGATGCCTTG,S. pombe
+cut,S. pyogenes,Zaratiegui,"<a href=""/52226/"">52226</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25352017/"">25352017</a>",ade6-M210,TCTATTGTTCAGATGCTTCG,S. pombe
+cut,S. pyogenes,Ventura,"<a href=""/64071/"">64071</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25337876/"">25337876</a>",Alk and Eml,,M. musculus
+nick,S. pyogenes,Shaw,"<a href=""/74374/"">74374</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26816379/"">26816379</a>",AMPK alpha 1,GGCTGTCGCCATCTTTCTCC,H. sapiens
+nick,S. pyogenes,Shaw,"<a href=""/74375/"">74375</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26816379/"">26816379</a>",AMPK alpha 1,GAAGATCGGCCACTACATTC,H. sapiens
+nick,S. pyogenes,Shaw,"<a href=""/74376/"">74376</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26816379/"">26816379</a>",AMPK alpha 2,GTCAGCCATCTTCGGCGCGCG,H. sapiens
+nick,S. pyogenes,Shaw,"<a href=""/74377/"">74377</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26816379/"">26816379</a>",AMPK alpha 2,GAAGATCGGACACTACGTGC,H. sapiens
+cut,S. pyogenes,Sabatini,"<a href=""/70660/"">70660</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26472758/"">26472758</a>","Amplicon, JAK2",GAGGCATATTCTTCTCCTGG,H. sapiens
+cut,S. pyogenes,Voytas,"<a href=""/70019/"">70019</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26541286/"">26541286</a>",ANT1,TGTCGTTTATAATTTGTAGA,S. lycopersicum
+cut,S. pyogenes,Voytas,"<a href=""/70018/"">70018</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26541286/"">26541286</a>",ANT1,ACAATTTAATACACCTTTT,S. lycopersicum
+cut,S. pyogenes,Kim,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26479191/"">26479191</a>",AOC,CAAAAGACTGTCAATTCCCT,N. attenuata
+cut/nick,S. pyogenes,Yamamoto,"<a href=""/58780/"">58780</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24954249/"">24954249</a>",APC,GAAGCGGGCAAAGGGGCGAC,H. sapiens
+cut,S. pyogenes,Joung,"<a href=""/42241/"">42241</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23360964/"">23360964</a>",apoea,GGATGAGCCAAGAAGCCGCT,D. rerio
+activate,S. pyogenes,Sato,"<a href=""/64131/"">64131</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",ASCL1,TGGATGGAGAGTTTGCAAGGAGC,H. sapiens
+activate,S. pyogenes,Sato,"<a href=""/64130/"">64130</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",ASCL1,TGGGCAGCCGCTCGCTGCAGCAG,H. sapiens
+activate,S. pyogenes,Sato,"<a href=""/64129/"">64129</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",ASCL1,TGGGGCTGGGTGTCCCATTGAAA,H. sapiens
+activate,S. pyogenes,Sato,"<a href=""/64132/"">64132</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",ASCL1,TGGTGTTTATTCAGCCGGGAGTC,H. sapiens
+cut,S. pyogenes,Mashimo,"<a href=""/60968/"">60968</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24967838/"">24967838</a>",Asip,ATGACAGGAGTCTAACTCAC,R. norvegicus
+tag,S. pyogenes,Mendenhall,"<a href=""/64690/"">64690</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26355004/"">26355004</a>",ATF1,TAGGAATCAAACACTTTTATTGG,H. sapiens
+cut,S. pyogenes,Yamamoto,"<a href=""/58779/"">58779</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24954249/"">24954249</a>",ATM,TGAATTGGGATGCTGTTTTT,H. sapiens
+cut,S. pyogenes,Mello,"<a href=""/58981/"">58981</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24879462/"">24879462</a>",avr-14,GATTGGAGAGTTAGACCACG,C. elegans
+cut,S. pyogenes,Mello,"<a href=""/58982/"">58982</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24879462/"">24879462</a>",avr-15,GTTTGCAATATAAGTCACCC,C. elegans
+cut,S. pyogenes,Yamamoto,"<a href=""/58782/"">58782</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24954249/"">24954249</a>",AXIN2,TATGTTGGTGACTTGCCTCC,H. sapiens
+methylation,S. pyogenes,Zoldoš,"<a href=""/71828/"">71828</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26969735/"">26969735</a>",BACH2,AATGTAGCGATTGAGAGTGTGGG,H. sapiens
+cut,S. pyogenes,Sabatini,"<a href=""/70658/"">70658</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26472758/"">26472758</a>",BCR/ABL amplicon,GGCTCCCTTCAAGTGGGATG,H. sapiens
+cut,S. pyogenes,Corn,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26789497/"">26789497</a>",BFP,ATGGCGTGCAGTGCTTCAGC,
+cut,S. pyogenes,Kim,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26479191/"">26479191</a>",BIN2,ATCACAGTGATGCTCGTCAA,L. sativa
+cut,S. pyogenes,Hanna,"<a href=""/59724/"">59724</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25543152/"">25543152</a>",BLIMP1,CGGATGGGGTAAACGACCCG,H. sapiens
+cut,S. pyogenes,Kim,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26479191/"">26479191</a>",BRI1,TTTGAAAGATGGAAGCGCGG,A. thaliana
+cut,S. pyogenes,Kim,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26479191/"">26479191</a>",BRI1,TGAAACTAAACTGGTCCACA,A. thaliana
+interfere,S. pyogenes,Lu,"<a href=""/68892/"">68892</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26918244/"">26918244</a>",BT1754,GAAAATGGGGTGTATCCTGC,B. thetaiotaomicron
+interfere,S. pyogenes,Lu,"<a href=""/68891/"">68891</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26918244/"">26918244</a>",BT1854,ATTGAAGAACAAAAGCAGTT,B. thetaiotaomicron
+cut,S. pyogenes,Sabatini,"<a href=""/70650/"">70650</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26472758/"">26472758</a>",C16orf80,CGATGCTGTAGAGGATGGAG,H. sapiens
+cut,S. pyogenes,Sabatini,"<a href=""/70651/"">70651</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26472758/"">26472758</a>",C16orf80,TGTCTGAGAAGTAAACCCGT,H. sapiens
+cut,S. pyogenes,Sabatini,"<a href=""/70653/"">70653</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26472758/"">26472758</a>",C3orf17,GGGCCAAATGGGGTTTGTGG,H. sapiens
+cut,S. pyogenes,Sabatini,"<a href=""/70652/"">70652</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26472758/"">26472758</a>",C3orf17,GTGTGAGAATCCCTAAGGCG,H. sapiens
+cut,S. pyogenes,Sabatini,"<a href=""/70654/"">70654</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26472758/"">26472758</a>",C9orf114,CAGGCGGGCTCACCTCCGTG,H. sapiens
+cut,S. pyogenes,Sabatini,"<a href=""/70655/"">70655</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26472758/"">26472758</a>",C9orf114,GCGGCAGAGAAGGAGGACCG,H. sapiens
+cut,S. pyogenes,Church,"<a href=""/43803/"">43803</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23460208/"">23460208</a>",CAN1,GATACGTTCTCTATGGAGGA,S. cerevisiae
+cut,S. pyogenes,Bornhauser,"<a href=""/75164/"">75164</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/27194728/"">27194728</a>",Caspase-8,GCCTGGACTACATTCCGCAA,H. sapiens
+cut,S. pyogenes,Corn,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26789497/"">26789497</a>",CCR5,TGACATCAATTATTATACAT,H. sapiens
+interfere,S. pyogenes,Qi,"<a href=""/46918/"">46918</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23849981/"">23849981</a>",CD71,GGACGCGCTAGTGTGAGTGC,H. sapiens
+cut,S. pyogenes,Yamamoto,"<a href=""/58781/"">58781</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24954249/"">24954249</a>",CDH1,TGACTTGCGAGGGACGCATT,H. sapiens
+activate,S. pyogenes,Vale,"<a href=""/60905/"">60905</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25307933/"">25307933</a>",CDKN1B,GAAGCCGGGACCTGGACCAG,H. sapiens
+tag,S. pyogenes,Mendenhall,"<a href=""/64036/"">64036</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26355004/"">26355004</a>",CEBPB,CTCCGGCCACTGCTAGCGCGG,H. sapiens
+tag,S. pyogenes,Mendenhall,"<a href=""/64047/"">64047</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26355004/"">26355004</a>",CEBPB,GCCGGCAGGGGGACGCGCGCGG,H. sapiens
+cut,S. pyogenes,Ikawa,"<a href=""/50718/"">50718</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24284873/"">24284873</a>",Cent1,GGCAACGTTTGACTTCCTGA,M. musculus
+cut,S. pyogenes,Yamamoto,"<a href=""/58783/"">58783</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24954249/"">24954249</a>",CFTR,TCTGTATCTATATTCATCAT,H. sapiens
+tag,S. pyogenes,Mendenhall,"<a href=""/64940/"">64940</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26355004/"">26355004</a>",CREB1,GCCACAAATCAGATTAATTTGG,H. sapiens
+tag,S. pyogenes,Mendenhall,"<a href=""/64939/"">64939</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26355004/"">26355004</a>",CREB1,GCCACAAATCAGATTAATTTGGG,H. sapiens
+cut,S. pyogenes,Hong,"<a href=""/68060/"">68060</a>","<a href=""http://fungalbiolbiotech.biomedcentral.com/articles/10.1186/s40694-015-0015-1"">Fungal Biology and Biotechnology 2015, 2:4</a>",csr-1,GAGTGGGAGGGTCCCGTCCT,N. crassa
+cut,S. pyogenes,Jacks,"<a href=""/59912/"">59912</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25119044/"">25119044</a>",Ctnnb1,AGCTCCTTCCCTGAGTGGCA,M. musculus
+cut,S. pyogenes,Jacks,"<a href=""/59911/"">59911</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25119044/"">25119044</a>",Ctnnb1,CTGTGGTGGTGGCACCAGAA,M. musculus
+interfere,S. pyogenes,Qi,"<a href=""/46917/"">46917</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23849981/"">23849981</a>",CXCR4,GCAGGTAGCAAAGTGACGCCGA,H. sapiens
+cut,S. pyogenes,Corn,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26789497/"">26789497</a>",CXCR4,CCTCTTTGTCATCACGCTTC,H. sapiens
+activate,S. pyogenes,Lu,"<a href=""/64385/"">64385</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23977949/"">23977949</a>",CYC1m promoter,ACAGAGCACATGCATGCCAT,S. cerevisiae
+activate,S. pyogenes,Lu,"<a href=""/64387/"">64387</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23977949/"">23977949</a>",CYC1m promoter,ACTAATACTTTCAACATTTT,S. cerevisiae
+activate,S. pyogenes,Lu,"<a href=""/64382/"">64382</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23977949/"">23977949</a>",CYC1m promoter,ATATCGAATTCCTGCAGCCC,S. cerevisiae
+activate,S. pyogenes,Lu,"<a href=""/64380/"">64380</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23977949/"">23977949</a>",CYC1m promoter,ATATTCTTTCCTTATACATT,S. cerevisiae
+activate,S. pyogenes,Lu,"<a href=""/64379/"">64379</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23977949/"">23977949</a>",CYC1m promoter,CTAGATATTAAAATGTCTAA,S. cerevisiae
+activate,S. pyogenes,Lu,"<a href=""/64388/"">64388</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23977949/"">23977949</a>",CYC1m promoter,GTTGAAAGTATTAGTTAAAG,S. cerevisiae
+activate,S. pyogenes,Lu,"<a href=""/64381/"">64381</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23977949/"">23977949</a>",CYC1m promoter,TACATACAGTAGGATCCTA,S. cerevisiae
+activate,S. pyogenes,Lu,"<a href=""/64383/"">64383</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23977949/"">23977949</a>",CYC1m promoter,TATAGTAATTTATGCTGCAA,S. cerevisiae
+activate,S. pyogenes,Lu,"<a href=""/64384/"">64384</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23977949/"">23977949</a>",CYC1m promoter,TGCAAAGGTCCTAATGTATA,S. cerevisiae
+activate,S. pyogenes,Lu,"<a href=""/64386/"">64386</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23977949/"">23977949</a>",CYC1m promoter,TGTATGTACATACAGTACCC,S. cerevisiae
+activate,S. pyogenes,Lu,"<a href=""/64378/"">64378</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23977949/"">23977949</a>",CYC1m promoter,TTACTGTATGTACATACAGT,S. cerevisiae
+cut,S. pyogenes,Wente,"<a href=""/47932/"">47932</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23918387/"">23918387</a>",ddx19,GGCAACAGATTCGTGGGCCC,D. rerio
+cut,S. pyogenes,Sabatini,"<a href=""/70656/"">70656</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26472758/"">26472758</a>",DDX3Y,GCAGTTTAGCGATATTGACA,H. sapiens
+cut,S. pyogenes,Sabatini,"<a href=""/70657/"">70657</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26472758/"">26472758</a>",DDX3Y,TCTTGTTGGGGCTAAAACCA,H. sapiens
+cut,S. pyogenes,Church,"<a href=""/41822/"">41822</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23287722/"">23287722</a>",DNMT3A,GAGATGATCGCCCCTTCTTC,H. sapiens
+cut,S. pyogenes,Church,"<a href=""/41821/"">41821</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23287722/"">23287722</a>",DNMT3A,GCATGATGCGCGGCCCAAGG,H. sapiens
+cut,S. pyogenes,Church,"<a href=""/41823/"">41823</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23287722/"">23287722</a>",DNMT3B,GAATTACTCACGCCCCAAGG,H. sapiens
+cut,VRER Cas9 mutant,Fire,"<a href=""/71516/"">71516</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26680661/"">26680661</a>",dpy-10,CGCTACCATAGGCACCACG,C. elegans
+cut,S. pyogenes,Katic,"<a href=""/65630/"">65630</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26044730/"">26044730</a>",dpy-10,GCTACCATAGGCACCACGAG,C.elegans
+cut,S. pyogenes,Fire,"<a href=""/59933/"">59933</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25161212/"">25161212</a>",dpy-10,GCTACCATAGGCACCACGAG,C. elegans
+cut,VQR Cas9 variant,Fire,"<a href=""/71479/"">71479</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26680661/"">26680661</a>",dpy-10,TCCGCTACCATAGGCACCA,C. elegans
+cut,S. pyogenes,Seydoux,"<a href=""/70047/"">70047</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26187122/"">26187122</a>",dpy‐10,GCTACCATAGGCACCACGAG,C. elegans
+cut,S. pyogenes,Joung,"<a href=""/42242/"">42242</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23360964/"">23360964</a>",drd3,GGAAACTACAGCCCAGCGTC,D. rerio
+cut,S. pyogenes,Kim,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26479191/"">26479191</a>",DWD1,TGCATCGTCCAAGCGCACAG,O. sativa
+cut,S. pyogenes,Kim,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26479191/"">26479191</a>",DWD1,CTACGACGTCAGGTTCTACC,O. sativa
+cut,S. pyogenes,Christiaen,"<a href=""/59990/"">59990</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25336740/"">25336740</a>",Ebf,GCTGAGGGTTGGACAACAGG,C. intestinalis
+dCas9-FokI,S. pyogenes,Joung,"<a href=""/60071/"">60071</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24770325/"">24770325</a>",EGFP,"multiple, see article",A. victoria
+cut,S. pyogenes,Zhang,"<a href=""/51764/"">51764</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24336571/"">24336571</a>",EGFP,GAAGTTCGAGGGCGACACCC,A. victoria
+cut,S. pyogenes,Zhang,"<a href=""/51761/"">51761</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24336571/"">24336571</a>",EGFP,GAGCTGGACGGCGACGTAAA,A. victoria
+cut,S. pyogenes,Joung,"<a href=""/47512/"">47512</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23792628/"">23792628</a>",EGFP,GATGCCGTTCTTCTGCTTGT,A. victoria
+cut,S. pyogenes,Zhang,"<a href=""/51763/"">51763</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24336571/"">24336571</a>",EGFP,GGAGCGCACCATCTTCTTCA,A. victoria
+cut,S. pyogenes,Zhang,"<a href=""/51762/"">51762</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24336571/"">24336571</a>",EGFP,GGCCACAAGTTCAGCGTGTC,A. victoria
+cut,S. pyogenes,Del Bene,"<a href=""/61051/"">61051</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24179142/"">24179142</a>",EGFP,GGCGAGGGCGATGCCACCTA,A. victoria
+cut,S. pyogenes,Chen,"<a href=""/46760/"">46760</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23918387/"">23918387</a>",EGFP,GGGCACGGGCAGCTTGCCGG,A. victoria
+cut,S. pyogenes,Joung,"<a href=""/47511/"">47511</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23792628/"">23792628</a>",EGFP,GGGCACGGGCAGCTTGCCGG,A. victoria
+cut,S. pyogenes,Zhang,"<a href=""/51760/"">51760</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24336571/"">24336571</a>",EGFP,GGGCGAGGAGCTGTTCACCG,A. victoria
+cut,S. pyogenes,Zhang,"<a href=""/51765/"">51765</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24336571/"">24336571</a>",EGFP,GGTGAACCGCATCGAGCTGA,A. victoria
+cut,S. pyogenes,Joung,"<a href=""/47513/"">47513</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23792628/"">23792628</a>",EGFP,GGTGGTGCAGATGAACTTCA,A. victoria
+cut,S. pyogenes,Goncalves,"<a href=""/58255/"">58255</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24870050/"">24870050</a>",EGFP,TGAAGAAGATGGTGCGCTC,A. victoria
+cut,S. pyogenes,Moffat,"<a href=""/74187/"">74187</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26627737/"">26627737</a>",EIF3D,TGTAGGTTGCCTCCATGGCC,H. sapiens
+cut,S. pyogenes,Moffat,"<a href=""/74188/"">74188</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26627737/"">26627737</a>",EIF3D,AGACGACCCTGTCATCCGCA,H. sapiens
+cut,S. pyogenes,Zhang,"<a href=""/42337/"">42337</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23287718/"">23287718</a>",Emx1,,H. sapiens
+cut,S. pyogenes,Joung,"<a href=""/47508/"">47508</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23792628/"">23792628</a>",EMX1,GAGTCCGAGCAGAAGAAGAA,H. sapiens
+cut,S. pyogenes,Corn,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26789497/"">26789497</a>",EMX1,CGATGTCACCTCCAATGACT,H. sapiens
+cut,S. pyogenes,Chen,"<a href=""/71288/"">71288</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26193878/"">26193878</a>","ETC2, TRY, and CPC",,A. thaliana
+cut,S. pyogenes,Bornhauser,"<a href=""/75166/"">75166</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/27194728/"">27194728</a>",FADD,ACTCCCGCACACGCTCTGTC,H. sapiens
+cut,S. pyogenes,Joung,"<a href=""/47510/"">47510</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23792628/"">23792628</a>",FANCF,GGAATCCCTTCTGCAGCACC,H. sapiens
+cut,S. pyogenes,Seydoux,"<a href=""/65597/"">65597</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25249454/"">25249454</a>",fbf-2,GTAGTCACGGCGATGATTA,C. elegans
+cut,S. pyogenes,Seydoux,"<a href=""/65591/"">65591</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25249454/"">25249454</a>",fbf-2,TAATCATCGCCGTGACTAC,C. elegans
+cut,S. pyogenes,Joung,"<a href=""/42244/"">42244</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23360964/"">23360964</a>",fh,GGAGCGAGCGGAGCGGTACA,D. rerio
+cut,S. pyogenes,Joung,"<a href=""/42243/"">42243</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23360964/"">23360964</a>",fh,GGAGCGGTACATGGCGACCG,D. rerio
+tag,S. pyogenes,Mendenhall,"<a href=""/64254/"">64254</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26355004/"">26355004</a>",GABPA,GAAAAGGATAATTGAGCCCCAGG,H. sapiens
+tag,S. pyogenes,Mendenhall,"<a href=""/64255/"">64255</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26355004/"">26355004</a>",GABPA,TTTGGAGTCTCAGAATGTCCTGG,H. sapiens
+activate,S. pyogenes,Qi,"<a href=""/46916/"">46916</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23849981/"">23849981</a>",GAL4 UAS,GAACGACTAGTTAGGCGTGTA,
+activate,S. pyogenes,Qi,"<a href=""/46915/"">46915</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23849981/"">23849981</a>",GAL4 UAS,GTTGGAGCACTGTCCTCCGAACGT,
+activate,S. pyogenes,Sato,"<a href=""/64158/"">64158</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",GAL4UAS,TGGGGACAGTACTCCGCTCGAGT,
+activate,S. pyogenes,Sato,"<a href=""/64157/"">64157</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",GAL4UAS,TGGGTCTTCGGAGGACAGTACTC,
+activate,S. pyogenes,Sato,"<a href=""/64159/"">64159</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",GAL4UAS,TGGTCCGTCTAGAAACTCGGTAC,
+cut,S. pyogenes,Du,"<a href=""/65566/"">65566</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25849248/"">25849248</a>",gfap,GTGCGCAACACATAGCACCA,D. rerio
+interfere,S. pyogenes,Qi,"<a href=""/46914/"">46914</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23849981/"">23849981</a>",GFP,GAATAGCTCAGAGGCCGAGG,A. victoria
+cut,S. pyogenes,Church,"<a href=""/41820/"">41820</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23287722/"">23287722</a>",GFP,GGAGCGCACCATCTTCTTCA,A. victoria
+cut,S. pyogenes,Church,"<a href=""/41819/"">41819</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23287722/"">23287722</a>",GFP,GTGAACCGCATCGAGCTGAA,A. victoria
+cut,S. pyogenes,"Guigo, Johnson","<a href=""/72619/"">72619</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26493208/"">26493208</a>",GFP,gRNA1: GAGCTGGACGGCGACGTAAA; gRNA2: CAGAACACCCCCATCGGCGA,Synthetic
+interfere,S. pyogenes,Hudson,"<a href=""/73221/"">73221</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26689101/"">26689101</a>",GFPmut3b,ACCATCTAATTCAACAAGAATT,A. victoria
+interfere,S. pyogenes,Hudson,"<a href=""/73224/"">73224</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26689101/"">26689101</a>",GFPmut3b,ACCATCTAATTCAACAAGAATT,synthetic
+CRISPR-display,S. pyogenes,Rinn,"<a href=""/68422/"">68422</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26030444/"">26030444</a>",GLuc,GATCTAGATACGACTCACTAT,synthetic
+cut,S. pyogenes,Ye,"<a href=""/61833/"">61833</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24424410/"">24424410</a>",gp78,GCCCAGCCTCCGCACCTACA,H. sapiens
+cut,S. pyogenes,Joung,"<a href=""/42245/"">42245</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23360964/"">23360964</a>",gsk3b,GGGACCTGACCGGCCGCAGG,D. rerio
+cut,S. pyogenes,Jin,"<a href=""/64333/"">64333</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25281382/"">25281382</a>",his3,ATTGCGATCTCTTTAAAGGG,S. cerevisiae
+cut,S. pyogenes,Yan,"<a href=""/69539/"">69539</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26130722/"">26130722</a>",HPRT,GACTGTAAGTGAATTACTT,H. sapiens
+cut,S. pyogenes,Yamamoto,"<a href=""/58778/"">58778</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24954249/"">24954249</a>",HPRT1,CTGATAAAATCTACAGTCAT,H. sapiens
+cut,S. pyogenes,Yamamoto,"<a href=""/58771/"">58771</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24954249/"">24954249</a>",HPRT1,TTATGCTGAGGATTTGGAAA,H. sapiens
+activate,S. pyogenes,Sato,"<a href=""/64142/"">64142</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",IL1RN,TGGACGCAGATAAGAACCAGTT,H. sapiens
+activate,S. pyogenes,Sato,"<a href=""/64151/"">64151</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",IL1RN,TGGCATCAAGTCAGCCATCAGC,H. sapiens
+activate,S. pyogenes,Sato,"<a href=""/64152/"">64152</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",IL1RN,TGGGAGTCACCCTCCTGGAAAC,H. sapiens
+activate,S. pyogenes,Sato,"<a href=""/64140/"">64140</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",IL1RN,TGGTGTACTCTCTGAGGTGCTC,H. sapiens
+cut,S. pyogenes,Xue,"<a href=""/66582/"">66582</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26018130/"">26018130</a>",inverted GFP,GAGCGGCCGCTCGAGTCTAG,A. victoria
+cut,S. pyogenes,Xue,"<a href=""/66583/"">66583</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26018130/"">26018130</a>",inverted GFP,GATATCGAATTCCTGCAGCC,A. victoria
+cut,S. pyogenes,Xue,"<a href=""/66581/"">66581</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26018130/"">26018130</a>",inverted GFP,GTATCGATACCGTCGACCTCG,A. victoria
+cut,S. pyogenes,Xue,"<a href=""/66584/"">66584</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26018130/"">26018130</a>",inverted GFP,GTTGATCCATAACTTCGTAT,A. victoria
+purify,S. pyogenes,Fujii,"<a href=""/61079/"">61079</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23942116/"">23942116</a>",IRF1,CCGGGGGCGCTGGGCTGTCCCGG,H. sapiens
+purify,S. pyogenes,Fujii,"<a href=""/61080/"">61080</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25051498/"">25051498</a>",IRF1,GCGCTTACACTTTAGGAGACACTC,H. sapiens
+cut,S. pyogenes,Sabatini,"<a href=""/70679/"">70679</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26472758/"">26472758</a>",JAK2 amplicon&nbsp;,GGTTTAATGGAAGAGAAGGG,H. sapiens
+cut,S. pyogenes,Seydoux,"<a href=""/66085/"">66085</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25249454/"">25249454</a>",K08F4.2,AATCACTCCCTGTTTGTGT,C. elegans
+cut,S. pyogenes,Seydoux,"<a href=""/66095/"">66095</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25249454/"">25249454</a>",K08F4.2,CACGAGGTGGTATGCGCAG,C. elegans
+cut,S. pyogenes,Seydoux,"<a href=""/66092/"">66092</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25249454/"">25249454</a>",K08F4.2,CGCAGCGGTTTCCAAAATG,C. elegans
+cut,S. pyogenes,Seydoux,"<a href=""/66087/"">66087</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25249454/"">25249454</a>",K08F4.2,GCCTTAACCCAGAATAAGA,C. elegans
+cut,S. pyogenes,Seydoux,"<a href=""/66089/"">66089</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25249454/"">25249454</a>",K08F4.2,TATTAAATGCAGATAACCT,C. elegans
+cut,S. pyogenes,Wolfe,"<a href=""/69238/"">69238</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26480473/"">26480473</a>",KANK3,GCATGGGTGATGTCAATGCC,H. sapiens
+cut,S. pyogenes,Mashimo,"<a href=""/60969/"">60969</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24967838/"">24967838</a>",Kit-1,CATCTGTGCGGCCGTTGGCT,R. norvegicus
+cut,S. pyogenes,Mashimo,"<a href=""/60970/"">60970</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24967838/"">24967838</a>",Kit-2,CTAACGTTCCAGCGCTCGTT,R. norvegicus
+cut,S. pyogenes,Mashimo,"<a href=""/60971/"">60971</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24967838/"">24967838</a>",Kit-2,GTCAAGATGTCATCTTACGG,R. norvegicus
+cut,S. pyogenes,Calarco,"<a href=""/46170/"">46170</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23817069/"">23817069</a>",klp-12,GATCCACAAGTTACAATTGG,C. elegans
+cut,S. pyogenes,Zhang,"<a href=""/60224/"">60224</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25263330/"">25263330</a>","Kras, p53, and Lkb1","multiple, see article",M. musculus
+cut,S. pyogenes,Zhang,"<a href=""/60225/"">60225</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25263330/"">25263330</a>",LacZ,TGCGAATACGCCCACGCGAT,E. coli
+cut,S. pyogenes,Moffat,"<a href=""/74179/"">74179</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26627737/"">26627737</a>",LacZ,CCCGAATCTCTATCGTGCGG,E.coli
+cut,S. pyogenes,Patron,"<a href=""/68256/"">68256</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26616834/"">26616834</a>",lC.GA4a,GTTGAGAGGGGAGCCGGTGA,B. oleracea
+cut,S. pyogenes,Patron,"<a href=""/68255/"">68255</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26616834/"">26616834</a>",lC.GA4a,GTTTTCACTTGCGGCCGGAG,B. oleracea
+cut,S. pyogenes,Jin,"<a href=""/64332/"">64332</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25281382/"">25281382</a>",leu2,AATCACAGCCGAAGCCATTA,S. cerevisiae
+cut,S. pyogenes,Winslow,"<a href=""/66894/"">66894</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26178787/"">26178787</a>",Lkb1,GTGGTGGGCCGCAGTCACAA,M. musculus
+cut,S. pyogenes,Xue,"<a href=""/66586/"">66586</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26018130/"">26018130</a>",Lox-STOP-Lox,GCGTATAGCATACATTATACG,synthetic
+cut,S. pyogenes,Xue,"<a href=""/66585/"">66585</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26018130/"">26018130</a>",Lox-STOP-Lox,GCGTATAGTACACATTATACG,synthetic
+cut,S. pyogenes,Wolfe,"<a href=""/69237/"">69237</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26480473/"">26480473</a>",LRTM2,GCTGGCGGAAGACAGAGTGC,H. sapiens
+cut,S. pyogenes,Moffat,"<a href=""/74190/"">74190</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26627737/"">26627737</a>",Luciferase,ACAACTTTACCGACCGCGCC,
+cut,S. pyogenes,Cate,"<a href=""/60847/"">60847</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25139909/"">25139909</a>",LYP1,CATAATAACGTCCAATAAAT,S. cerevisiae
+cut,S. pyogenes,"Guigo, Johnson","<a href=""/72620/"">72620</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26493208/"">26493208</a>",Malat1,gRNA1: GAACCGGTGGGGCTGCGTCA; gRNA2: GGCAGGAGAGGCCAGTTGCG,H. sapiens
+cut,S. pyogenes,"Guigo, Johnson","<a href=""/72621/"">72621</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26493208/"">26493208</a>",Malat1,gRNA1: GCAACTTCCATTTTCAGTCT; gRNA2: GGAAGCCTCAGCTCGCCTGA,H. sapiens
+cut,S. pyogenes,"Guigo, Johnson","<a href=""/72622/"">72622</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26493208/"">26493208</a>",Malat1,gRNA1:GCTGGGGCTCAGTTGCGTAA; gRNA2:AGGTTTCTAAAACATGACGG,H. sapiens
+cut,S. pyogenes,"Guigo, Johnson","<a href=""/72623/"">72623</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26493208/"">26493208</a>",Malat1,gRNA1:GTTGAGATGAAGCTTCTTCA; gRNA2:TCAACCGTCCCTGCAAGGCT,H. sapiens
+cut,S. pyogenes,"Guigo, Johnson","<a href=""/72624/"">72624</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26493208/"">26493208</a>",Malat1,gRNA1: GAAACCTCGTGTAGCTATCA; gRNA2: AATGTGAAGGACTTTCGTAA,H. sapiens
+nick,S. pyogenes,Hanna,"<a href=""/61514/"">61514</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25569111/"">25569111</a>",Mettl14,GCCGCTCCCGGATCTCCTGC,M. musculus
+nick,S. pyogenes,Hanna,"<a href=""/61515/"">61515</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25569111/"">25569111</a>",Mettl14,GCGGCAGCTCCTAGCTCAGC,M. musculus
+cut,S. pyogenes,Zhao,"<a href=""/69801/"">69801</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26293209/"">26293209</a>",mitf,CTTTCGGATATAATCCACGG,S. scrofa&nbsp;
+cut,S. pyogenes,Chen,"<a href=""/47931/"">47931</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23918387/"">23918387</a>",mitfa,GGTCTCTCGCAGGATGTTGC,D. rerio
+cut,S. pyogenes,Bleris,"<a href=""/62715/"">62715</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25527740/"">25527740</a>",mKate,ATGAGAATCAAGGCGGTCGA,synthetic
+cut,S. pyogenes,Bornhauser,"<a href=""/75167/"">75167</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/27194728/"">27194728</a>",MLKL,ATCCCCGTGGATTCTGCTAA,H. sapiens
+cut,S. pyogenes,Katsanis,"<a href=""/72890/"">72890</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26429889/"">26429889</a>",mmp21,CGGAGCTGATCACTGACA,D. rerio
+visualize,S. pyogenes,Qi,"<a href=""/51025/"">51025</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24360272/"">24360272</a>",MUC4,GTGGCGTGACCTGTGGATGCTG,H. sapiens
+activate,S. pyogenes,Sato,"<a href=""/64138/"">64138</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",MYOD1,TGGCCTCCCTCCCTGCCCGGTAG,H. sapiens
+activate,S. pyogenes,Sato,"<a href=""/64139/"">64139</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",MYOD1,TGGGAGGTTTGGAAAGGGCGTGC,H. sapiens
+activate,S. pyogenes,Sato,"<a href=""/64136/"">64136</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",MYOD1,TGGGCCTGGGCTCCGGGGCGTTT,H. sapiens
+activate,S. pyogenes,Sato,"<a href=""/64137/"">64137</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",MYOD1,TGGGGGCCCCTGCGGCCACCCCG,H. sapiens
+activate,S. pyogenes,Sato,"<a href=""/64156/"">64156</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",NANOG,TGGCATATTCCTGATTTAAAAGT,H. sapiens
+activate,S. pyogenes,Sato,"<a href=""/64153/"">64153</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",NANOG,TGGCGCCAGGAGGGGTGGGTCTA,H. sapiens
+activate,S. pyogenes,Sato,"<a href=""/64154/"">64154</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",NANOG,TGGGCCTTGGTGAGACTGGTAGA,H. sapiens
+activate,S. pyogenes,Sato,"<a href=""/64155/"">64155</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",NANOG,TGGTGTCTTCAGGTTCTGTTGCT,H. sapiens
+interfere,S. pyogenes,Lu,"<a href=""/68897/"">68897</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26918244/"">26918244</a>",NanoLuc,AGCTTACGCCACCCTGTTCC,synthetic
+interfere,S. pyogenes,Lu,"<a href=""/68898/"">68898</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26918244/"">26918244</a>",NanoLuc,GACAGAACGATGCGCTGAAT,synthetic
+interfere,S. pyogenes,Lu,"<a href=""/68895/"">68895</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26918244/"">26918244</a>",NanoLuc,TCACGCTCACACCCAGGTTC,synthetic
+interfere,S. pyogenes,Lu,"<a href=""/68896/"">68896</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26918244/"">26918244</a>",NanoLuc,TTGATCCAAATTATAACCCG,synthetic
+cut,S. pyogenes,Lu,"<a href=""/61270/"">61270</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25240928/"">25240928</a>",NDM-1,GGGCAGTCGCTTCCAACGGTTTGATCGTCA,
+cut,S. pyogenes,Cepko,"<a href=""/69992/"">69992</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25155555/"">25155555</a>",near LoxP sites,CGAAGTTATATTAAGGGTTC,synthetic
+interfere,S. pyogenes,Lu,"<a href=""/68893/"">68893</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26918244/"">26918244</a>",negative control,CTGGAATGAATTGGCCTATG,
+cut,S. pyogenes,Winslow,"<a href=""/66895/"">66895</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26178787/"">26178787</a>",negative control,GCGAGGTATTCGGCTCCGCG,M. musculus
+cut,S. pyogenes,Christiaen,"<a href=""/60006/"">60006</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25336740/"">25336740</a>",negative control,GCTTTGCTACGATCTACATT,C. intestinalis
+cut,S. pyogenes,Bleris,"<a href=""/64955/"">64955</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25527740/"">25527740</a>",negative control,GTCAAGGCACTCTTGCCTA,synthetic
+,S. pyogenes,Wolfe,"<a href=""/50927/"">50927</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24346702/"">24346702</a>",negative control,GTTCCGCGTTACATAACTTA,H. sapiens
+cut,S. pyogenes,Winslow,"<a href=""/67594/"">67594</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26178787/"">26178787</a>",Neomycin,TCATGGCTGATGCAATGCGG,
+cut,S. pyogenes,Zhang,"<a href=""/60227/"">60227</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25263330/"">25263330</a>",NeuN,TCCGGTTCAGGGACCCCGAC,M. musculus
+activate,S. pyogenes,Sato,"<a href=""/64161/"">64161</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",Neurog2,GTCTCTATCACTGATAGGGA,H. sapiens
+activate,S. pyogenes,Sato,"<a href=""/64160/"">64160</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",Neurog2,GTGAATGATGATAATACGAT,H. sapiens
+methylation,S. pyogenes,Zoldoš,"<a href=""/71830/"">71830</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26969735/"">26969735</a>",Non-targeting,GTAGGCGCGCCGCTCTCTAC,H. sapiens
+cut,S. pyogenes,Huangfu,"<a href=""/66989/"">66989</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26028531/"">26028531</a>",OCT4,CTCCCATGCATTCAAACTG,H. sapiens
+activate,S. pyogenes,Otonkoski,"<a href=""/69537/"">69537</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26352799/"">26352799</a>",OCT4,"multiple, see article",H. sapiens
+cut,N. meningitidis,Thomson,"<a href=""/47870/"">47870</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23940360/"">23940360</a>",OCT4,GTTGTAGCTCCCTTTCTCATTTCG,H. sapiens
+interfere,S. pyogenes,Wolfe,"<a href=""/50922/"">50922</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24346702/"">24346702</a>",Oct4A (POU5F1),GGGGCGCCAGTTGTGTCTCC,H. sapiens
+interfere,S. pyogenes,Wolfe,"<a href=""/50921/"">50921</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24346702/"">24346702</a>",Oct4A (POU5F1),GTGGGACTGGGGAGGGAGAG,H. sapiens
+cut,S. pyogenes,Kim,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26479191/"">26479191</a>",P450,CATATAGTTGGGTCATGGCA,O. sativa
+cut,S. pyogenes,Sherwood,"<a href=""/71483/"">71483</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26527385/"">26527385</a>",Pal1,GCTCTGTGACTAGTCACAGAG,synthetic
+cut,S. pyogenes,Sherwood,"<a href=""/71484/"">71484</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26527385/"">26527385</a>",Pal7,GGCTTAGTACTAGTACTAAGC,synthetic
+cut,S. pyogenes,Zhang,"<a href=""/68466/"">68466</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26145478/"">26145478</a>",PAX6,GATACTGGAGAAGCAGGGGC,H. sapiens
+cut,S. pyogenes,Zhang,"<a href=""/68465/"">68465</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26145478/"">26145478</a>",PAX6,GTAGATTTTGTATGCACTGC,H. sapiens
+interfere,S. pyogenes,Lu,"<a href=""/68900/"">68900</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26918244/"">26918244</a>",PcfiA,AAACAAAACCTCATCAGGCA,
+interfere,S. pyogenes,Lu,"<a href=""/68899/"">68899</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26918244/"">26918244</a>",PcfiA,GAAGCTCACTCCTTAGCACG,
+interfere,S. pyogenes,Lu,"<a href=""/74068/"">74068</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26829286/"">26829286</a>",pck,AGGGCGAGGCGCCGACCAAGAATA,C. glutamicum
+interfere,S. pyogenes,Lu,"<a href=""/74069/"">74069</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26829286/"">26829286</a>",pck,TCCAGTTCAGCAGTTCCTTATTCT,C. glutamicum
+cut,S. pyogenes,Kamoun,"<a href=""/46966/"">46966</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23929340/"">23929340</a>",PDS,GCCGTTAATTTGAGAGTCCA,N. benthamiana
+cut,S. pyogenes,Kamoun,"<a href=""/49772/"">49772</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24112467/"">24112467</a>",PDS1,"multiple, see article",N. benthamiana
+cut,S. pyogenes,Sheen,"<a href=""/52255/"">52255</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23929339/"">23929339</a>",PDS3,GGACTTTTGCCAGCCATGGT,A. thaliana
+interfere,S. pyogenes,Lu,"<a href=""/74066/"">74066</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26829286/"">26829286</a>",pgi,TGACCGATCATTACTCAAACTTCC,C. glutamicum
+interfere,S. pyogenes,Lu,"<a href=""/74067/"">74067</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26829286/"">26829286</a>",pgi,TTGCCTGGAAGTTTGAGTAATGAT,C. glutamicum
+activate,S. pyogenes,Gersbach,"<a href=""/60719/"">60719</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25664691/"">25664691</a>",pGL3-Basic-8x-gRNA-eGFP reporter,AAAGGTCGAGAAACTGCAAA,synthetic
+cut,S. pyogenes,Ward,"<a href=""/61252/"">61252</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25491644/"">25491644</a>",pha-1,ATGAATAACTTGATGAACAT,C. elegans
+cut,S. pyogenes,Kim,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26479191/"">26479191</a>",PHYB,CACTAGGAGCAACACCCAA,A. thaliana
+cut,S. pyogenes,Wolfe,"<a href=""/69240/"">69240</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26480473/"">26480473</a>",PLXNB2,GGGCCCAACCTAGGGCATGG,H. sapiens
+cut,S. pyogenes,Patron,"<a href=""/68253/"">68253</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26616834/"">26616834</a>",PM19,GCTCTCCACTCTGGGCTCTT,H. vulgare
+cut,S. pyogenes,Patron,"<a href=""/68254/"">68254</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26616834/"">26616834</a>",PM19,GGCTGGCGTTGGTCGTAACA,H. vulgare
+cut,S. pyogenes,Shaw,"<a href=""/79004/"">79004</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26944679/"">26944679</a>",Prkaa1,TTATTGTCACAGGCATATGG,M. musculus
+cut,S. pyogenes,Shaw,"<a href=""/79005/"">79005</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26944679/"">26944679</a>",Prkaa2,ACAGGCATATGGTTGTCCAT,M. musculus
+cut,S. pyogenes,Schmitt-Ulms,"<a href=""/61857/"">61857</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25490046/"">25490046</a>",Prnp,GGTGGAACACCGGTGGAAGC,M. musculus
+cut,S. pyogenes,Schmitt-Ulms,"<a href=""/61856/"">61856</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25490046/"">25490046</a>",Prnp,TTGGCCCCATCCACCGCCAT,M. musculus
+cut,N. meningitidis,Church,"<a href=""/48651/"">48651</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24076762/"">24076762</a>",Protospacer A,TACCATCTCAAGCTTGTTGA,Synthetic
+cut,S. thermophilus,Church,"<a href=""/48653/"">48653</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24076762/"">24076762</a>",Protospacer A,TACCATCTCAAGCTTGTTGA,Synthetic
+cut,T. denticola,Church,"<a href=""/48655/"">48655</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24076762/"">24076762</a>",Protospacer A,TACCATCTCAAGCTTGTTGA,Synthetic
+cut,N. meningitidis,Church,"<a href=""/48652/"">48652</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24076762/"">24076762</a>",Protospacer B,ACTTTAAAAGTATTCGCCAT,Synthetic
+cut,S. thermophilus,Church,"<a href=""/48654/"">48654</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24076762/"">24076762</a>",Protospacer B,ACTTTAAAAGTATTCGCCAT,Synthetic
+cut,T. denticola,Church,"<a href=""/48656/"">48656</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24076762/"">24076762</a>",Protospacer B,ACTTTAAAAGTATTCGCCAT,Synthetic
+cut,S. pyogenes,Moffat,"<a href=""/74184/"">74184</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26627737/"">26627737</a>",PSMB2,ATGTTCTTGTCGCCTCCGAC,H. sapiens
+cut,S. pyogenes,Moffat,"<a href=""/74185/"">74185</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26627737/"">26627737</a>",PSMB2,AATATTGTCCAGATGAAGGA,H. sapiens
+cut,S. pyogenes,Moffat,"<a href=""/74180/"">74180</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26627737/"">26627737</a>",PSMD1,TGTGCGCTACGGAGCTGCAA,H. sapiens
+cut,S. pyogenes,Moffat,"<a href=""/74181/"">74181</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26627737/"">26627737</a>",PSMD1,ACCAGAGCCACAATAAGCCA,H. sapiens
+cut,S. pyogenes,Jacks,"<a href=""/59909/"">59909</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25119044/"">25119044</a>",Pten,AGATCGTTAGCAGAAACAAA,M. musculus
+cut,S. pyogenes,Xue,"<a href=""/66588/"">66588</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26018130/"">26018130</a>",Pten,GCTGTAGTAATATCTGCTAT,M. musculus
+cut,S. pyogenes,Xue,"<a href=""/66587/"">66587</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26018130/"">26018130</a>",Pten,GTTTCATAGCGGCCACGAAGT,M. musculus
+interfere,S. pyogenes,Lu,"<a href=""/74070/"">74070</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26829286/"">26829286</a>",pyk,AGATTGTATGTACCCTAGGCCCAG,C. glutamicum
+interfere,S. pyogenes,Lu,"<a href=""/74072/"">74072</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26829286/"">26829286</a>",pyk,ATTCCATCTGCACTAGCCACCGCT,C. glutamicum
+tag,S. pyogenes,Mendenhall,"<a href=""/64057/"">64057</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26355004/"">26355004</a>",RAD21,CCAAGGTTCCATATTATATAAGG,H. sapiens
+cut,S. pyogenes,Fire,"<a href=""/59927/"">59927</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25161212/"">25161212</a>",rde-1(D718),TGCCATTAACTATGTATGT,C. elegans
+cut,S. pyogenes,Fire,"<a href=""/59928/"">59928</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25161212/"">25161212</a>",rde-1(D801),GATATTGTAGTCTATCGAGA,C. elegans
+cut,S. pyogenes,Fire,"<a href=""/59929/"">59929</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25161212/"">25161212</a>",rde-1(H974),GATAAATGAGCATAATGAAC,C. elegans
+interfere,S. pyogenes,Lu,"<a href=""/74075/"">74075</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26829286/"">26829286</a>",rfp,AAGTTCGTATGGAAGGTTCCGTTAA,
+cut,S. pyogenes,Joung,"<a href=""/42246/"">42246</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23360964/"">23360964</a>",rgs4,GGAGAAGGTGAAGGACACTG,D. rerio
+cut,S. pyogenes,Bornhauser,"<a href=""/75165/"">75165</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/27194728/"">27194728</a>",RIP3,TACACGAGTGATGGTTCGGT,H. sapiens
+cut,S. pyogenes,Bornhauser,"<a href=""/75163/"">75163</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/27194728/"">27194728</a>",RIPK1,GCTCTGCTGGGAAGCGAATC,H. sapiens
+cut,S. pyogenes,Joung,"<a href=""/47509/"">47509</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23792628/"">23792628</a>",RNF2,GTCATCTTAGTCATTACCTG,H. sapiens
+cut,S. pyogenes,Fire,"<a href=""/59930/"">59930</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25161212/"">25161212</a>",rol-6(su1006),GTGAGACGTCAACAATATGG,C. elegans
+cut,S. pyogenes,Kuhn,"<a href=""/64216/"">64216</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25803306/"">25803306</a>",Rosa26,ACTCCAGTCTTTCTAGAAGA,M.musculus
+cut,S. pyogenes,Marraffini,"<a href=""/44505/"">44505</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23360965/"">23360965</a>",rpsL,AAAAAACCGAACTCCGCGCTGCGTAAAGTA,E. coli
+interfere,S. pyogenes,Voigt,"<a href=""/62285/"">62285</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25422271/"">25422271</a>",scramble,AACCCCTGATTGTATCCGCA,synthetic
+cut,S. pyogenes,Chen,"<a href=""/47930/"">47930</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23918387/"">23918387</a>",slc24a5 (golden),GGTCTCTCGCAGGATGTTGC,D. rerio
+cut,S. pyogenes,Hanna,"<a href=""/59725/"">59725</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25543152/"">25543152</a>",SOX17,GCCCAGCCCGGCCATCACCG,H. sapiens
+activate,S. pyogenes,Wolfe,"<a href=""/50925/"">50925</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24346702/"">24346702</a>",Sox17,GCTCCGGCTAGTTTTCCCGG,H. sapiens
+activate,S. pyogenes,Wolfe,"<a href=""/50924/"">50924</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24346702/"">24346702</a>",Sox17,GGAGGGGCAAGGGGCGGGCG,H. sapiens
+activate,S. pyogenes,Wolfe,"<a href=""/50926/"">50926</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24346702/"">24346702</a>",Sox17,GGGCAAGTACGTCGATTCCA,H. sapiens
+activate,S. pyogenes,Wolfe,"<a href=""/50923/"">50923</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24346702/"">24346702</a>",Sox17,GGGCGTGGGCCTAACGACGC,H. sapiens
+cut,S. pyogenes,Fire,"<a href=""/59935/"">59935</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25161212/"">25161212</a>",sqt-1,GGAAGGACATAGTTGTCAT,C. elegans
+cut,S. pyogenes,Fire,"<a href=""/60212/"">60212</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25161212/"">25161212</a>",sqt-1,TGTGGAGTTGGGGTAGCGT,C. elegans
+activate/interfere,S. pyogenes,Qi,"<a href=""/62338/"">62338</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25533786/"">25533786</a>",sv40 promoter,CATACTTCTGCCTGCTGGGGAGCCTG,synthetic
+scaffold,S. pyogenes,Qi &amp; Lim,"<a href=""/62340/"">62340</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25533786/"">25533786</a>",SV40.NT1,GAATAGCTCAGAGGCCGAGG,H. sapiens
+cut,S. pyogenes,Seydoux,"<a href=""/66100/"">66100</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25249454/"">25249454</a>",swan-2,ACAAATTGATATCCAATCA,C. elegans
+cut,S. pyogenes,Seydoux,"<a href=""/66099/"">66099</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25249454/"">25249454</a>",swan-2,TGAAGAAAGTTATACTCGA,C. elegans
+cut,S. pyogenes,Hanna,"<a href=""/59726/"">59726</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25543152/"">25543152</a>",T (BRACHYURY),CACGCGCAGTTCGCGCTCTG,H. sapiens
+cut,S. pyogenes,"Guigo, Johnson","<a href=""/72627/"">72627</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26493208/"">26493208</a>",TCRC,gRNA1:GGGGAGCGGGAAAGCGGTCG; gRNA2:AACTGACCTTCAGGCCCGTA,H. sapiens
+cut,N. meningitidis,Thomson,"<a href=""/47869/"">47869</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23940360/"">23940360</a>",tdtomato,CGAAATGAGAAAGGGAGCTACAAC,Synthetic
+scaffold,S. pyogenes,Qi &amp; Lim,"<a href=""/62320/"">62320</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25533786/"">25533786</a>",TEF,TTGATATTTAAGTTAATAAA,S. cerevisiae
+interfere,S. pyogenes,Weissman,"<a href=""/46922/"">46922</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23849981/"">23849981</a>",TEF1,TTGATATTTAAGTTAATAAA,S. cerevisiae
+visualize,S. pyogenes,Qi,"<a href=""/51024/"">51024</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24360272/"">24360272</a>",telomeres,GTTAGGGTTAGGGTTAGGGTTA,H. sapiens
+scaffold,S. pyogenes,Qi &amp; Lim,"<a href=""/62313/"">62313</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25533786/"">25533786</a>",TET,ACTTTTCTCTATCACTGATA,S. cerevisiae
+interfere,S. pyogenes,Weissman,"<a href=""/46923/"">46923</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23849981/"">23849981</a>",TET promoter,TATCAGTGATAGAGAAAAGT,
+activate/interfere,S. pyogenes,Qi,"<a href=""/62327/"">62327</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25533786/"">25533786</a>",Tet3G,GTACGTTCTCTATCACTGATA,synthetic
+cut,S. pyogenes,Shaw,"<a href=""/79006/"">79006</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26944679/"">26944679</a>",Tfeb,AGCACTGTTGCCGGCCGAGG,M. musculus
+RNA targeting,S. pyogenes,Yeo,"<a href=""/74709/"">74709</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26997482/"">26997482</a>",TFRC,GCACUGACCAGAUAAGAAUG,H. sapiens
+cut,S. pyogenes,"Guigo, Johnson","<a href=""/72626/"">72626</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26493208/"">26493208</a>",TFRC,"gRNA1:GCAGCCTCAGAAATACAAAA, gRNA2CGGGATATCGGGTGGCGGCT",H. sapiens
+cut,S. pyogenes,Wolfe,"<a href=""/69239/"">69239</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26480473/"">26480473</a>",TGM2,CCTGGACCCAACGCCCCAGG,H. sapiens
+cut,S. pyogenes,Sibley,"<a href=""/59855/"">59855</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25480939/"">25480939</a>",TGME49_290860,ATTGGTCTGACAGCGATGTG,T. gondii
+cut,S. pyogenes,Joung,"<a href=""/42247/"">42247</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23360964/"">23360964</a>",th,GGATGCGCGTAAGGAGCGCG,D. rerio
+cut,S. pyogenes,Du,"<a href=""/65565/"">65565</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25849248/"">25849248</a>",th,GGGTTGTTACACTCATAAAG,D. rerio
+cut,S. pyogenes,Joung,"<a href=""/42248/"">42248</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23360964/"">23360964</a>",tia1l,GGTATGTCGGGAACCTCTCC,D. rerio
+cut,S. pyogenes,Joung,"<a href=""/42249/"">42249</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23360964/"">23360964</a>",tph1a,GGGAAAACACAACCGCAGCC,D. rerio
+scaffold,S. pyogenes,Qi &amp; Lim,"<a href=""/62325/"">62325</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25533786/"">25533786</a>",TRE3G,GTACGTTCTCTATCACTGATA,H. sapiens
+cut,S. pyogenes,Jin,"<a href=""/64331/"">64331</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25281382/"">25281382</a>",trp1,GTCAATTGTTCTCTTTCTAT,S. cerevisiae
+cut,S. pyogenes,Jacks,"<a href=""/59910/"">59910</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25119044/"">25119044</a>",Trp53,CCTCGAGCTCCCTCTGAGCC,M. musculus
+cut,S. pyogenes,Wolfe,"<a href=""/69234/"">69234</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26480473/"">26480473</a>",TS2,GACCCCCTCCACCCCGCCTC,H. sapiens
+cut,S. pyogenes,Wolfe,"<a href=""/69235/"">69235</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26480473/"">26480473</a>",TS3,GGTGAGTGAGTGTGTGCGTG,H. sapiens
+cut,S. pyogenes,Wolfe,"<a href=""/69236/"">69236</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26480473/"">26480473</a>",TS4,GAGTCCGAGCAGAAGAAGAA,H. sapiens
+cut,S. pyogenes,Goldstein,"<a href=""/47550/"">47550</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23995389/"">23995389</a>",ttTi5605 Mos1 insertion site,ATATCAGTCTGTTTCGTAA,C. elegans
+cut,S. pyogenes,Chen,"<a href=""/46761/"">46761</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23918387/"">23918387</a>",tyr,CCCCAGAAGTCCTCCAGTCC,D. rerio
+cut,S. pyogenes,Chen,"<a href=""/64250/"">64250</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25855067/"">25855067</a>",tyr,GGACTGGAGGACTTCTGGGG,D. rerio
+cut,S. pyogenes,Mashimo,"<a href=""/60966/"">60966</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24967838/"">24967838</a>",Tyr (albino),TTTCCAGGATTATGTAATAG,R. norvegicus
+cut,S. pyogenes,Mashimo,"<a href=""/60967/"">60967</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24967838/"">24967838</a>",Tyr (wild type),TTTCCAGGATTACGTAATAG,R. norvegicus
+cut,S. pyogenes,"Guigo, Johnson","<a href=""/72628/"">72628</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26493208/"">26493208</a>",UCA1,gRNA1:GGTTTCCTTTTAGATGACGG; gRNA2:TCTGAAAAGAGAGTCAGCGA,H. sapiens
+cut,S. pyogenes,Fire,"<a href=""/59932/"">59932</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25161212/"">25161212</a>",unc-109(n499),GGAACTCGTGTCAAAACAAC,C. elegans
+cut,S. pyogenes,Calarco,"<a href=""/46169/"">46169</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23817069/"">23817069</a>",unc-119,GAATTTTCTGAAATTAAAGA,C. elegans
+cut,S. pyogenes,Mello,"<a href=""/58202/"">58202</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24879462/"">24879462</a>",unc-22,GAACCCGTTGCCGAATACAC,C. elegans
+cut,S. pyogenes,Fire,"<a href=""/59931/"">59931</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25161212/"">25161212</a>",unc-58(e665),TCCACGCACATGGTCACTA,C. elegans
+cut,S. pyogenes,Sibley,"<a href=""/54467/"">54467</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24825012/"">24825012</a>",UPRT,GGCGTCTCGATTGTGAGAGC,Toxoplasma gondii
+cut,S. pyogenes,Jin,"<a href=""/64330/"">64330</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25281382/"">25281382</a>",ura3,GAGTAAAAAATTGTACTTGG,S. cerevisiae
+cut,S. pyogenes,Ye,"<a href=""/61812/"">61812</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24424410/"">24424410</a>",USP13,GCCGCCCGGCATGCCGAACA,H. sapiens
+cut,S. pyogenes,Joung,"<a href=""/47506/"">47506</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23792628/"">23792628</a>",VEGF,GACCCCCTCCACCCCGCCTC,H. sapiens
+cut,S. pyogenes,Joung,"<a href=""/47505/"">47505</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23792628/"">23792628</a>",VEGF,GGGTGGGGGGAGTTTGCTCC,H. sapiens
+cut,S. pyogenes,Joung,"<a href=""/47507/"">47507</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23792628/"">23792628</a>",VEGF,GGTGAGTGAGTGTGTGCGTG,H. sapiens
+interfere,S. pyogenes,Voigt,"<a href=""/62286/"">62286</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25422271/"">25422271</a>",VR,TGCGCTCGGTCGTTCGGCTG,synthetic
+nick,S. pyogenes,Yang,"<a href=""/73228/"">73228</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/27213844/"">27213844</a>",xylR,CGAGTTAGACATAATAGTGA,C. beijerinckii
+cut,S. pyogenes,Ward,"<a href=""/61250/"">61250</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25491644/"">25491644</a>",Y61A9LA.1,GGATGGATGTGTAGTCAATT,C. elegans
+cut,S. pyogenes,Liu,"<a href=""/49331/"">49331</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24326186/"">24326186</a>",yellow,GGTTTTGGACACTGGAACCG,D. melanogaster

BioScientist/agent_system/engines/v1_executor_backup/know_how/sgRNA_design_guide.md

@@ -0,0 +1,419 @@
+# sgRNA Design Guide: Three-Tiered Approach
+
+---
+
+## Metadata
+
+**Short Description**: Comprehensive guide for finding or designing sgRNAs using validated sequences, CRISPick datasets, or de novo design tools.
+
+**Authors**: Biomni Team
+
+**Version**: 1.0
+
+**Last Updated**: November 2025
+
+**License**: CC BY 4.0
+
+**Commercial Use**: ✅ Allowed
+
+## Citations and Acknowledgments
+
+### If you use validated sgRNAs from our database (Option 1):
+- **Database Source**: Addgene (https://www.addgene.org)
+- **Citation**: Always cite the original publication associated with each sgRNA using the PubMed ID provided in the database
+- **Acknowledgment**: "Validated sgRNA sequences obtained from Addgene (https://www.addgene.org/crispr/reference/grna-sequence/)"
+
+### If you use CRISPick designs (Option 2):
+- **Acknowledgment Statement**: "Guide designs provided by the CRISPick web tool of the GPP at the Broad Institute"
+- **Citation for Cas9 designs (SpCas9, SaCas9)**: Sanson KR, et al. Optimized libraries for CRISPR-Cas9 genetic screens with multiple modalities. Nat Commun. 2018;9(1):5416. PMID: 30575746
+- **Citation for Cas12a designs (AsCas12a, enAsCas12a)**: DeWeirdt PC, et al. Optimization of AsCas12a for combinatorial genetic screens in human cells. Nat Biotechnol. 2021;39(1):94-104. PMID: 32661438
+  - Note: This paper describes enAsCas12a optimization; specify which variant you used in your methods
+
+---
+
+## Overview
+
+This guide provides a three-tiered approach to sgRNA design, prioritizing validated sequences before moving to computational predictions. Always start with Option 1 and proceed to subsequent options only if needed.
+
+
+
+## Option 1: Search Validated sgRNA Sequences (Recommended First)
+
+### 1.1 Search for Validated sgRNAs
+
+**IMPORTANT**: You MUST complete BOTH Method 1 AND Method 2 before proceeding to Option 2. Do not skip Method 2 even if Method 1 finds no results.
+
+#### Method 1: Search Our Database (Fastest)
+
+We maintain a curated database of 300+ validated sgRNA sequences from Addgene with experimental evidence.
+
+**Location**: `biomni/know_how/resource/addgene_grna_sequences.csv`
+
+**Search the database**:
+```python
+import pandas as pd
+
+# Load the database
+df = pd.read_csv('addgene_grna_sequences.csv')
+
+# Search for your gene
+gene_name = "TP53"
+results = df[df['Target_Gene'].str.upper() == gene_name.upper()]
+
+# Filter by species and application
+results_filtered = results[
+    (results['Target_Species'] == 'H. sapiens') &
+    (results['Application'] == 'cut')  # or 'activate', 'RNA targeting'
+]
+
+# Display results with references
+print(results_filtered[['Target_Gene', 'Target_Sequence',
+                        'Plasmid_ID', 'PubMed_ID', 'Depositor']])
+```
+
+**Database columns**:
+- `Target_Gene`: Gene symbol
+- `Target_Species`: Organism (H. sapiens, M. musculus, etc.)
+- `Target_Sequence`: 20bp sgRNA sequence (5' to 3')
+- `Application`: cut (knockout), activate (CRISPRa), RNA targeting (CRISPRi)
+- `Cas9_Species`: S. pyogenes, S. aureus, etc.
+- `Plasmid_ID`: Addgene plasmid number
+- `Plasmid_URL`: Direct link to plasmid page
+- `PubMed_ID`: **Publication reference** (cite this in your work)
+- `PubMed_URL`: Direct link to paper
+- `Depositor`: Research lab that contributed the sequence
+
+#### Method 2: Advanced Web Search (REQUIRED - Do Not Skip)
+
+**CRITICAL**: Even if Method 1 found no results, you MUST perform this literature search before moving to Option 2. Many validated sgRNAs are published in literature but not in the Addgene database.
+
+Use `advanced_web_search_claude` from `biomni.tool.literature` to find validated sgRNAs from literature and databases:
+
+```python
+from biomni.tool.literature import advanced_web_search_claude
+
+# Example usage
+results = advanced_web_search_claude("sgRNA TP53 validated H. sapiens experimental")
+```
+
+**Search queries to try (use multiple):**
+```
+"sgRNA" OR "guide RNA" "[GENE_NAME]" validated experimental
+"CRISPR knockout" "[GENE_NAME]" sgRNA sequence validated
+"[GENE_NAME]" sgRNA "cutting efficiency" OR "on-target"
+"[GENE_NAME]" "guide sequence" CRISPR validated
+```
+
+**Example for TP53:**
+```
+"sgRNA" "TP53" validated "H. sapiens" experimental
+"CRISPR knockout" "TP53" guide sequence validated
+```
+
+**What to search for in results:**
+- Published papers with validated sgRNA sequences
+- Supplementary materials containing sgRNA sequences
+- Other CRISPR databases (e.g., GenScript, Horizon Discovery)
+- Laboratory protocols with specific sgRNA sequences
+
+**IMPORTANT**: Spend adequate time searching literature. Look through at least the first 10-15 search results and check supplementary materials of relevant papers.
+
+#### What to Do with Results:
+
+✅ **If you find matching sgRNAs (from either method):**
+1. **Record the sgRNA sequence** (20bp target sequence)
+2. **Note the reference**:
+   - From database: Use `PubMed_ID` to cite the original paper
+   - From web search: Record the publication DOI/PubMed ID
+3. **Record validation details**: Cell line, cutting efficiency, any reported off-targets
+
+
+**Example result format:**
+```
+Gene: TP53
+sgRNA sequence: GAGGTTGTGAGGCGCTGCCC
+Species: H. sapiens (human)
+Application: Knockout (cut)
+Reference: PubMed ID 24336569 (Ran et al., 2013)
+Validation: Tested in HEK293T cells, 85% cutting efficiency
+```
+
+❌ **If no matches found in BOTH Method 1 AND Method 2:**
+Only then proceed to **Option 2: Download CRISPick Dataset**
+
+
+
+## Option 2: Download Pre-Computed sgRNAs from CRISPick
+
+### When to Use This Option?
+- No validated sgRNAs found in Addgene
+- Need comprehensive coverage of a gene
+- Want multiple sgRNA options with predicted scores
+- Working with genome-scale screening
+
+### 2.1 Overview of CRISPick
+
+**CRISPick** (from Broad Institute GPP) provides pre-computed sgRNA designs for entire genomes with 238 available datasets covering:
+- Human (GRCh38, GRCh37)
+- Mouse (GRCm38, GRCm39)
+- Dog, Cow, Monkey, and other model organisms
+- Multiple Cas enzymes (SpCas9, SaCas9, AsCas12a, enAsCas12a)
+  - **IMPORTANT**: AsCas12a and enAsCas12a are DIFFERENT enzymes
+  - **AsCas12a**: Wild-type Acidaminococcus sp. Cas12a
+  - **enAsCas12a**: Enhanced variant with improved activity
+  - **Critical**: sgRNAs designed for enAsCas12a may NOT work with wild-type AsCas12a
+  - Always match the dataset to your specific Cas12a variant
+- Different applications (knockout, activation, inhibition)
+
+### 2.2 Find the Download Link
+
+**All 238 download links are available in**: `biomni/know_how/resource/CRISPick_download_links.txt`
+
+#### Step 1: Understand File Naming Convention
+
+Files are named: `sgRNA_design_{TAXID}_{GENOME}_{CAS}_{APPLICATION}_{ALGORITHM}_{SOURCE}_{DATE}.txt.gz`
+
+**Common datasets:**
+
+| Organism | Genome | Cas9 | Application | Search Pattern |
+|-|--||-|-|
+| Human | GRCh38 | SpCas9 | Knockout | `9606_GRCh38_SpyoCas9_CRISPRko` |
+| Human | GRCh38 | SpCas9 | Activation | `9606_GRCh38_SpyoCas9_CRISPRa` |
+| Human | GRCh38 | SaCas9 | Knockout | `9606_GRCh38_SaurCas9_CRISPRko` |
+| Mouse | GRCm38 | SpCas9 | Knockout | `10090_GRCm38_SpyoCas9_CRISPRko` |
+| Mouse | GRCm38 | SpCas9 | Activation | `10090_GRCm38_SpyoCas9_CRISPRa` |
+
+**Key components:**
+- **TAXID**: `9606` (Human), `10090` (Mouse), `9615` (Dog), `9913` (Cow)
+- **CAS**:
+  - `SpyoCas9` (SpCas9, NGG PAM)
+  - `SaurCas9` (SaCas9, NNGRRT PAM)
+  - `AsCas12a` (Wild-type Cas12a, TTTV PAM)
+  - `enAsCas12a` (Enhanced Cas12a, TTTV PAM)
+  - **WARNING**: Do NOT use AsCas12a datasets if you have enAsCas12a, and vice versa
+- **APPLICATION**: `CRISPRko` (knockout), `CRISPRa` (activation), `CRISPRi` (inhibition)
+
+#### Step 2: Find Your Download URL
+
+```bash
+# Search the download links file
+grep "9606_GRCh38_SpyoCas9_CRISPRko" biomni/know_how/resource/CRISPick_download_links.txt
+
+# Or for mouse
+grep "10090_GRCm38_SpyoCas9_CRISPRko" biomni/know_how/resource/CRISPick_download_links.txt
+```
+
+#### Step 3: Download and Extract
+
+```bash
+# Copy the URL from download_links.txt, then:
+wget [PASTE_URL_HERE]
+
+# Extract the file
+gunzip sgRNA_design_*.txt.gz
+```
+
+**File sizes**: Knockout (300-700 MB), Activation (50-100 MB), Summary files (1-3 MB)
+
+### 2.4 Understanding the File Format
+
+The `.txt` file is tab-delimited. Column names differ between knockout and activation/inhibition datasets.
+
+**Essential Columns (All files):**
+- **sgRNA Sequence**: The 20bp guide RNA sequence (5' to 3')
+- **Target Gene Symbol**: Gene name (e.g., "TP53", "BRCA1")
+- **Combined Rank**: Overall ranking (lower = better) - **Use this by default**
+- **On-Target Rank**: Ranking by efficiency only (lower = better)
+- **Off-Target Rank**: Ranking by specificity only (lower = better)
+- **PAM Sequence**: The PAM sequence (e.g., "AGG", "TGG")
+
+**Knockout-specific columns:**
+- **sgRNA Cut Position (1-based)**: Genomic coordinate of cut site
+- **Exon Number**: Which exon is targeted
+- **Target Cut %**: Percentage of protein affected
+
+**Activation/Inhibition-specific columns:**
+- **sgRNA 'Cut' Position**: Position relative to gene
+- **sgRNA 'Cut' Site TSS Offset**: Distance from transcription start site (bp)
+- **DHS Score**: DNase hypersensitivity score (for CRISPRa)
+
+### 2.5 Extract and Select sgRNAs
+
+#### Step 1: Load Data and Filter for Your Gene
+
+```python
+import pandas as pd
+
+# Load the dataset
+df = pd.read_csv('sgRNA_design_9606_GRCh38_SpyoCas9_CRISPRko_*.txt',
+                 sep='\t', low_memory=False)
+
+# Filter for your gene
+gene_name = "TP53"
+gene_sgrnas = df[df['Target Gene Symbol'] == gene_name].copy()
+
+print(f"Found {len(gene_sgrnas)} sgRNAs for {gene_name}")
+```
+
+#### Step 2: Select Top sgRNAs
+
+**Default: Use Combined Rank (balances efficiency and specificity)**
+```python
+# Sort by Combined Rank (lower is better)
+top_sgrnas = gene_sgrnas.nsmallest(10, 'Combined Rank')
+
+print(top_sgrnas[['sgRNA Sequence', 'Combined Rank',
+                   'Exon Number', 'sgRNA Cut Position (1-based)']])
+```
+
+**Option A: Prioritize On-Target Efficiency**
+```python
+# Sort by On-Target Rank (for maximum cutting efficiency)
+efficient_sgrnas = gene_sgrnas.nsmallest(10, 'On-Target Rank')
+```
+
+**Option B: Prioritize Off-Target Specificity**
+```python
+# Sort by Off-Target Rank (for maximum specificity)
+specific_sgrnas = gene_sgrnas.nsmallest(10, 'Off-Target Rank')
+```
+
+#### Step 3: Filter by Custom Criteria (Optional)
+
+**Filter by Exon Number:**
+```python
+# Target specific exon (e.g., exon 5)
+exon5_sgrnas = gene_sgrnas[gene_sgrnas['Exon Number'] == 5]
+top_exon5 = exon5_sgrnas.nsmallest(5, 'Combined Rank')
+```
+
+**Filter by Genomic Position:**
+```python
+# Target specific genomic range
+position_filtered = gene_sgrnas[
+    (gene_sgrnas['sgRNA Cut Position (1-based)'] >= 7572000) &
+    (gene_sgrnas['sgRNA Cut Position (1-based)'] <= 7575000)
+]
+```
+
+**Target Early Exons for Knockout:**
+```python
+# Get sgRNAs from first 3 exons
+early_exons = gene_sgrnas[gene_sgrnas['Exon Number'] <= 3]
+top_early = early_exons.nsmallest(10, 'Combined Rank')
+```
+
+**Filter by Target Cut Percentage:**
+```python
+# Target sgRNAs that affect significant portion of protein
+high_impact = gene_sgrnas[gene_sgrnas['Target Cut %'] <= 50]  # Cut in first 50%
+top_high_impact = high_impact.nsmallest(10, 'Combined Rank')
+```
+
+#### Step 4: Select Multiple sgRNAs for Validation
+
+```python
+# Get top 4 sgRNAs from different exons for redundancy
+final_selection = gene_sgrnas.sort_values('Combined Rank').groupby('Exon Number').head(1).head(4)
+
+# Save results
+final_selection.to_csv(f'{gene_name}_selected_sgRNAs.csv', index=False)
+
+print("\nSelected sgRNAs:")
+print(final_selection[['sgRNA Sequence', 'Exon Number', 'Combined Rank']])
+```
+
+### 2.6 What to Do with Results
+
+✅ **Once you have selected sgRNAs:**
+1. Choose **3-4 sgRNAs** (use Combined_Rank by default)
+
+❌ **If dataset doesn't cover your gene or organism:**
+Proceed to **Option 3: De Novo sgRNA Design**
+
+
+
+## Option 3: General sgRNA Design Guidelines (Last Resort)
+
+### When to Use This Option?
+- Gene not covered in CRISPick datasets
+- Non-model organism
+- Custom design requirements
+
+### General Design Rules
+
+#### Essential Requirements:
+1. **Length**:
+   - 20 bp for SpCas9 and SaCas9
+   - 23-25 bp for Cas12a variants (AsCas12a, enAsCas12a)
+2. **PAM sequence**:
+   - **SpCas9**: Requires **NGG** immediately after target (3' end)
+   - **SaCas9**: Requires **NNGRRT** immediately after target (3' end)
+   - **AsCas12a** (wild-type): Requires **TTTV** before target (5' end)
+   - **enAsCas12a** (enhanced): Requires **TTTV** before target (5' end)
+   - **CRITICAL**: Guides designed for enAsCas12a may not work with wild-type AsCas12a due to different activity profiles
+3. **GC content**: 40-60% is optimal
+4. **Avoid**:
+   - TTTT sequences (terminates transcription)
+   - Long runs of same nucleotide (>4 repeats)
+
+#### Target Location:
+- **For knockout**: Target early exons (first 50% of gene)
+- **For activation**: Target -200 to +1 bp from transcription start site (TSS)
+- **For inhibition**: Target -50 to +300 bp from TSS
+
+#### Best Practices:
+- **Test 3-4 different sgRNAs** per target gene
+- Select sgRNAs with high efficiency scores (>0.5) and minimal off-targets
+- Validate experimentally with Sanger sequencing
+
+
+
+## Quick Start Examples
+
+### Example 1: Knockout TP53 in Human Cells
+
+**Step 1**: Check Addgene
+```python
+df = pd.read_csv('addgene_grna_sequences.csv')
+tp53_results = df[(df['Target_Gene'] == 'TP53') &
+                  (df['Target_Species'] == 'H. sapiens') &
+                  (df['Application'] == 'cut')]
+# Result: Found 0 entries → Proceed to Option 2
+```
+
+**Step 2**: Download CRISPick dataset
+```bash
+# Download human GRCh38 SpCas9 knockout dataset
+wget https://portals.broadinstitute.org/gppx/public/sgrna_design/api/downloads/\
+sgRNA_design_9606_GRCh38_SpyoCas9_CRISPRko_RS3seq-Chen2013+RS3target_NCBI_20241104.txt.gz
+
+gunzip sgRNA_design_9606_GRCh38_SpyoCas9_CRISPRko_*.txt.gz
+```
+
+**Step 3**: Extract TP53 sgRNAs
+```python
+df = pd.read_csv('sgRNA_design_9606_GRCh38_SpyoCas9_CRISPRko_*.txt', sep='\t')
+tp53 = df[df['Gene_Symbol'] == 'TP53']
+top_sgrnas = tp53[
+    (tp53['sgRNA_score'] > 0.6) &
+    (tp53['Off_target_stringency'] > 0.5)
+].sort_values('sgRNA_score', ascending=False).head(4)
+
+print(top_sgrnas[['sgRNA_sequence', 'sgRNA_score', 'Exon_ID']])
+```
+
+### Example 2: Activate OCT4 in Human iPSCs
+
+**Step 1**: Check Addgene
+```python
+oct4_results = df[(df['Target_Gene'] == 'OCT4') &
+                  (df['Application'] == 'activate')]
+# Found 1 validated sgRNA!
+print(oct4_results['Target_Sequence'].values[0])
+# Use this sequence ✅
+```
+
+
+
+**Remember**: Always start with validated sequences (Option 1), then move to pre-computed designs (Option 2), and only use de novo design (Option 3) when necessary. Testing 3-4 sgRNAs per gene is standard practice regardless of prediction scores.

BioScientist/agent_system/engines/v1_executor_backup/know_how/single_cell_annotation.md

@@ -0,0 +1,223 @@
+# Single Cell RNA-seq Cell Type Annotation
+
+---
+
+## Metadata
+
+**Short Description**: Best practices for annotating cell types in single-cell RNA-seq data using marker-based, automated, and reference-based approaches.
+
+**Authors**: Distilled from "Single-cell best practices" by Luecken, M.D. et al.
+
+**Affiliations**: Helmholtz Munich, Wellcome Sanger Institute, Harvard Medical School, and contributors
+
+**Version**: 1.0
+
+**Last Updated**: January 2025
+
+**License**: CC BY 4.0
+
+**Commercial Use**: ✅ Allowed
+
+**Source**: https://www.sc-best-practices.org/cellular_structure/annotation.html
+
+**Citation**: Luecken, M.D., Theis, F.J. et al. (2023). Current best practices in single-cell RNA-seq analysis: a tutorial. Molecular Systems Biology.
+
+---
+
+## Overview
+
+Cell type annotation is the process of assigning cell type labels to clusters or individual cells in single-cell RNA-seq data. This guide covers three main approaches and their practical implementation.
+
+## Three Annotation Approaches
+
+### 1. Manual Marker-Based Annotation
+Identify cell types by examining expression of known marker genes in each cluster.
+
+**Tools**: Scanpy, Seurat
+**Best for**: Small datasets, novel cell types, high confidence needs
+
+### 2. Automated Annotation
+Use pre-trained classifiers to automatically assign cell type labels.
+
+**Tools**: CellTypist, scAnnotate
+**Best for**: Standard tissues, quick preliminary annotation, large datasets
+
+### 3. Reference-Based Label Transfer
+Transfer labels from annotated reference datasets to your query data.
+
+**Tools**: scArches, scANVI, Azimuth, SingleR
+**Best for**: Well-characterized tissues, integration with public data
+
+## Recommended Workflow
+
+### Step 1: Quality Control First
+- **Remove low-quality cells before annotation**
+- Filter doublets (expected doublet rate: 0.8% per 1000 cells)
+- Check for ambient RNA contamination
+- Verify cluster quality and resolution
+
+### Step 2: Initial Marker-Based Assessment
+
+```python
+# Scanpy example
+import scanpy as sc
+
+# Calculate marker genes for clusters
+sc.tl.rank_genes_groups(adata, 'leiden', method='wilcoxon')
+
+# Visualize top markers
+sc.pl.rank_genes_groups(adata, n_genes=25, sharey=False)
+
+# Plot known markers
+markers = {
+    'T cells': ['CD3D', 'CD3E', 'CD4', 'CD8A'],
+    'B cells': ['CD19', 'MS4A1', 'CD79A'],
+    'Monocytes': ['CD14', 'FCGR3A', 'LYZ'],
+    'NK cells': ['NCAM1', 'NKG7', 'GNLY']
+}
+
+sc.pl.dotplot(adata, markers, groupby='leiden')
+```
+
+### Step 3: Use Automated Tools for Validation
+
+```python
+# CellTypist example (fast, accurate for immune cells)
+import celltypist
+from celltypist import models
+
+# Download immune cell model
+model = models.Model.load(model='Immune_All_Low.pkl')
+
+# Predict cell types
+predictions = celltypist.annotate(adata, model=model, majority_voting=True)
+adata = predictions.to_adata()
+```
+
+### Step 4: Reference-Based Refinement
+
+```python
+# scArches example for label transfer
+import scarches as sca
+
+# Load pre-trained reference model
+model = sca.models.SCANVI.load_query_data(
+    adata=adata,  # Your query data
+    reference_model="path/to/reference_model"
+)
+
+# Transfer labels
+model.train(max_epochs=100)
+adata.obs['transferred_labels'] = model.predict()
+```
+
+## Best Practices
+
+### Do's:
+1. **Always combine multiple approaches** - Use marker-based validation even with automated tools
+2. **Check cluster purity** - Ensure clusters represent single cell types
+3. **Validate with multiple marker sets** - Don't rely on single markers
+4. **Consider biological context** - Tissue type, disease state, developmental stage
+5. **Document confidence levels** - Note uncertain annotations
+6. **Use hierarchical annotation** - Broad categories first, then subtypes
+
+### Don'ts:
+1. **Don't over-cluster** - Too fine resolution creates artificial distinctions
+2. **Don't ignore batch effects** - Correct before annotation
+3. **Don't trust automation blindly** - Always validate predictions
+4. **Don't mix cell states with cell types** - Activated vs. resting cells are states, not types
+5. **Don't annotate low-quality cells** - Remove them first
+
+## Common Pitfalls
+
+### 1. Doublet Clusters
+**Problem**: Clusters with markers from multiple cell types
+**Solution**: Use doublet detection tools (Scrublet, DoubletFinder) before annotation
+
+### 2. Ambient RNA
+**Problem**: Background markers in all cells
+**Solution**: Use SoupX or CellBender for decontamination
+
+### 3. Over-interpretation of Small Clusters
+**Problem**: Rare clusters may be technical artifacts
+**Solution**: Require minimum cell count (e.g., >25 cells), validate with independent data
+
+### 4. Reference Mismatch
+**Problem**: Reference from different tissue/species/condition
+**Solution**: Use tissue-matched references, check marker overlap before transfer
+
+## Tool Selection Guide
+
+| Scenario | Recommended Tool | Why |
+|----------|------------------|-----|
+| Immune cells (human) | CellTypist | Pre-trained on large immune atlases |
+| Mouse tissues | scArches + Mouse Cell Atlas | Comprehensive mouse reference |
+| Novel cell types | Manual + Scanpy/Seurat | Need domain expertise |
+| Large datasets (>100k cells) | CellTypist | Fast, scalable |
+| Cross-species | Manual markers | Limited reference transfer |
+| Developmental data | scArches | Handles continuous states |
+
+## Key Marker Genes by Cell Type
+
+### Blood/Immune:
+- **T cells**: CD3D, CD3E (all T cells); CD4, CD8A (subtypes)
+- **B cells**: CD19, MS4A1 (CD20), CD79A
+- **Monocytes/Macrophages**: CD14, CD68, LYZ
+- **NK cells**: NCAM1 (CD56), NKG7, KLRD1
+- **Dendritic cells**: FCER1A, CD1C
+
+### Epithelial:
+- **General epithelial**: EPCAM, KRT18, KRT19
+- **Lung AT1**: AGER, PDPN
+- **Lung AT2**: SFTPC, SFTPA1
+- **Intestinal**: VIL1, MUC2
+
+### Stromal:
+- **Fibroblasts**: COL1A1, DCN, LUM
+- **Endothelial**: PECAM1 (CD31), VWF, CDH5
+- **Smooth muscle**: ACTA2, MYH11, TAGLN
+
+## Validation Checklist
+
+- [ ] Cluster purity: >80% cells with same label per cluster
+- [ ] Marker consistency: Top DE genes match expected markers
+- [ ] Biological plausibility: Expected proportions for tissue type
+- [ ] Cross-method agreement: Manual and automated annotations align
+- [ ] Reference quality: >70% cells successfully transferred
+- [ ] Doublet check: No clusters with multi-lineage markers
+- [ ] Documentation: Record confidence levels and uncertain calls
+
+## Resources
+
+### Tools:
+- **Scanpy**: https://scanpy.readthedocs.io/
+- **CellTypist**: https://www.celltypist.org/
+- **scArches**: https://scarches.readthedocs.io/
+- **Seurat**: https://satijalab.org/seurat/
+
+### References:
+- **PanglaoDB**: Database of marker genes
+- **CellMarker**: Curated cell marker database
+- **Human Cell Atlas**: Reference datasets
+
+### Pre-trained Models:
+- **CellTypist models**: 30+ tissue-specific models
+- **Azimuth references**: PBMC, lung, kidney, etc.
+- **scArches models**: Multiple tissue references
+
+## Troubleshooting
+
+**Issue**: All clusters look similar
+→ Increase clustering resolution, check if data is normalized
+
+**Issue**: Too many small clusters
+→ Decrease resolution, merge similar clusters based on markers
+
+**Issue**: Automated tool gives inconsistent results
+→ Check input normalization, try multiple tools, fall back to manual
+
+**Issue**: Can't find clear markers for cluster
+→ May be transitional state, doublet, or low-quality cells
+
+**Issue**: Reference transfer fails
+→ Check batch correction, ensure overlapping gene sets, verify tissue match

BioScientist/agent_system/engines/v1_executor_backup/model/retriever.py

@@ -0,0 +1,203 @@
+import contextlib
+import re
+
+from langchain_core.messages import HumanMessage
+from langchain_openai import ChatOpenAI
+
+
+class ToolRetriever:
+    """Retrieve tools from the tool registry."""
+
+    def __init__(self):
+        pass
+
+    def prompt_based_retrieval(self, query: str, resources: dict, llm=None) -> dict:
+        """Use a prompt-based approach to retrieve the most relevant resources for a query.
+
+        Args:
+            query: The user's query
+            resources: A dictionary with keys 'tools', 'data_lake', 'libraries', and 'know_how',
+                      each containing a list of available resources
+            llm: Optional LLM instance to use for retrieval (if None, will create a new one)
+
+        Returns:
+            A dictionary with the same keys, but containing only the most relevant resources
+
+        """
+        # Build prompt sections for available resources
+        prompt_sections = []
+        prompt_sections.append(f"""
+You are an expert biomedical research assistant. Your task is to select the relevant resources to help answer a user's query.
+
+USER QUERY: {query}
+
+Below are the available resources. For each category, select items that are directly or indirectly relevant to answering the query.
+Be generous in your selection - include resources that might be useful for the task, even if they're not explicitly mentioned in the query.
+It's better to include slightly more resources than to miss potentially useful ones.
+
+AVAILABLE TOOLS:
+{self._format_resources_for_prompt(resources.get("tools", []))}
+
+AVAILABLE DATA LAKE ITEMS:
+{self._format_resources_for_prompt(resources.get("data_lake", []))}
+
+AVAILABLE SOFTWARE LIBRARIES:
+{self._format_resources_for_prompt(resources.get("libraries", []))}""")
+
+        # Add know-how section if available
+        if "know_how" in resources and resources["know_how"]:
+            prompt_sections.append(f"""
+AVAILABLE KNOW-HOW DOCUMENTS (Best Practices & Protocols):
+{self._format_resources_for_prompt(resources.get("know_how", []))}""")
+
+        # Build response format based on available categories
+        response_format = """
+For each category, respond with ONLY the indices of the relevant items in the following format:
+TOOLS: [list of indices]
+DATA_LAKE: [list of indices]
+LIBRARIES: [list of indices]"""
+
+        if "know_how" in resources and resources["know_how"]:
+            response_format += "\nKNOW_HOW: [list of indices]"
+
+        response_format += """
+
+For example:
+TOOLS: [0, 3, 5, 7, 9]
+DATA_LAKE: [1, 2, 4]
+LIBRARIES: [0, 2, 4, 5, 8]"""
+
+        if "know_how" in resources and resources["know_how"]:
+            response_format += "\nKNOW_HOW: [0, 1]"
+
+        response_format += """
+
+If a category has no relevant items, use an empty list, e.g., DATA_LAKE: []
+
+IMPORTANT GUIDELINES:
+1. Be generous but not excessive - aim to include all potentially relevant resources
+2. ALWAYS prioritize database tools for general queries - include as many database tools as possible
+3. Include all literature search tools
+4. For wet lab sequence type of queries, ALWAYS include molecular biology tools
+5. For data lake items, include datasets that could provide useful information
+6. For libraries, include those that provide functions needed for analysis
+7. For know-how documents, include those that provide relevant protocols, best practices, or troubleshooting guidance
+8. Don't exclude resources just because they're not explicitly mentioned in the query
+9. When in doubt about a database tool or molecular biology tool, include it rather than exclude it
+"""
+
+        prompt = "\n".join(prompt_sections) + response_format
+
+        # Use the provided LLM or create a new one
+        if llm is None:
+            llm = ChatOpenAI(model="gpt-4o")
+
+        # Invoke the LLM
+        if hasattr(llm, "invoke"):
+            # For LangChain-style LLMs
+            response = llm.invoke([HumanMessage(content=prompt)])
+            response_content = response.content
+        else:
+            # For other LLM interfaces
+            response_content = str(llm(prompt))
+
+        # Parse the response to extract the selected indices
+        selected_indices = self._parse_llm_response(response_content)
+
+        # Get the selected resources
+        selected_resources = {
+            "tools": [
+                resources["tools"][i] for i in selected_indices.get("tools", []) if i < len(resources.get("tools", []))
+            ],
+            "data_lake": [
+                resources["data_lake"][i]
+                for i in selected_indices.get("data_lake", [])
+                if i < len(resources.get("data_lake", []))
+            ],
+            "libraries": [
+                resources["libraries"][i]
+                for i in selected_indices.get("libraries", [])
+                if i < len(resources.get("libraries", []))
+            ],
+        }
+
+        # Add know-how if present
+        if "know_how" in resources and resources["know_how"]:
+            selected_resources["know_how"] = [
+                resources["know_how"][i]
+                for i in selected_indices.get("know_how", [])
+                if i < len(resources.get("know_how", []))
+            ]
+
+        return selected_resources
+
+    def _format_resources_for_prompt(self, resources: list) -> str:
+        """Format resources for inclusion in the prompt."""
+        formatted = []
+        for i, resource in enumerate(resources):
+            if isinstance(resource, dict):
+                # Handle dictionary format (from tool registry or data lake/libraries with descriptions)
+                name = resource.get("name", f"Resource {i}")
+                description = resource.get("description", "")
+                formatted.append(f"{i}. {name}: {description}")
+            elif isinstance(resource, str):
+                # Handle string format (simple strings)
+                formatted.append(f"{i}. {resource}")
+            else:
+                # Try to extract name and description from tool objects
+                name = getattr(resource, "name", str(resource))
+                desc = getattr(resource, "description", "")
+                formatted.append(f"{i}. {name}: {desc}")
+
+        return "\n".join(formatted) if formatted else "None available"
+
+    def _parse_llm_response(self, response) -> dict:
+        """Parse the LLM response to extract the selected indices.
+
+        Accepts either a plain string or a Responses API-style list of content blocks.
+        """
+        # Normalize response to string if it's a list of content blocks (Responses API)
+        if isinstance(response, list):
+            parts = []
+            for item in response:
+                # LangChain Responses API returns list of dicts like {"type": "text", "text": "..."}
+                if isinstance(item, dict):
+                    if item.get("type") == "text" and "text" in item:
+                        parts.append(str(item.get("text", "")))
+                    # If it's a tool_call or other block, ignore for this simple parsing
+                elif isinstance(item, str):
+                    parts.append(item)
+            response = "\n".join([p for p in parts if p])
+        elif not isinstance(response, str):
+            response = str(response)
+        selected_indices = {"tools": [], "data_lake": [], "libraries": [], "know_how": []}
+
+        # Extract indices for each category
+        tools_match = re.search(r"TOOLS:\s*\[(.*?)\]", response, re.IGNORECASE)
+        if tools_match and tools_match.group(1).strip():
+            with contextlib.suppress(ValueError):
+                selected_indices["tools"] = [int(idx.strip()) for idx in tools_match.group(1).split(",") if idx.strip()]
+
+        data_lake_match = re.search(r"DATA_LAKE:\s*\[(.*?)\]", response, re.IGNORECASE)
+        if data_lake_match and data_lake_match.group(1).strip():
+            with contextlib.suppress(ValueError):
+                selected_indices["data_lake"] = [
+                    int(idx.strip()) for idx in data_lake_match.group(1).split(",") if idx.strip()
+                ]
+
+        libraries_match = re.search(r"LIBRARIES:\s*\[(.*?)\]", response, re.IGNORECASE)
+        if libraries_match and libraries_match.group(1).strip():
+            with contextlib.suppress(ValueError):
+                selected_indices["libraries"] = [
+                    int(idx.strip()) for idx in libraries_match.group(1).split(",") if idx.strip()
+                ]
+
+        # Extract know-how indices
+        know_how_match = re.search(r"KNOW[-_]HOW:\s*\[(.*?)\]", response, re.IGNORECASE)
+        if know_how_match and know_how_match.group(1).strip():
+            with contextlib.suppress(ValueError):
+                selected_indices["know_how"] = [
+                    int(idx.strip()) for idx in know_how_match.group(1).split(",") if idx.strip()
+                ]
+
+        return selected_indices

BioScientist/agent_system/engines/v1_executor_backup/tool/bioengineering.py

@@ -0,0 +1,1387 @@
+def analyze_cell_migration_metrics(
+    image_sequence_path,
+    pixel_size_um=1.0,
+    time_interval_min=1.0,
+    min_track_length=10,
+    output_dir="./",
+):
+    """Analyze cell migration metrics from time-lapse microscopy images.
+
+    Parameters
+    ----------
+    image_sequence_path : str
+        Path to the directory containing time-lapse images or path to a multi-frame TIFF file
+    pixel_size_um : float
+        Conversion factor from pixels to micrometers (default: 1.0)
+    time_interval_min : float
+        Time interval between consecutive frames in minutes (default: 1.0)
+    min_track_length : int
+        Minimum number of frames a cell must be tracked to be included in analysis (default: 10)
+    output_dir : str
+        Directory to save output files (default: "./")
+
+    Returns
+    -------
+    str
+        Research log summarizing the cell migration analysis process and results
+
+    """
+    import os
+
+    import matplotlib.pyplot as plt
+    import numpy as np
+    import pandas as pd
+    import trackpy as tp
+    from skimage import io
+
+    # Create output directory if it doesn't exist
+    os.makedirs(output_dir, exist_ok=True)
+
+    # Load image sequence
+    if os.path.isdir(image_sequence_path):
+        # Load from directory of images
+        image_files = sorted(
+            [f for f in os.listdir(image_sequence_path) if f.endswith((".tif", ".tiff", ".png", ".jpg", ".jpeg"))]
+        )
+        frames = [io.imread(os.path.join(image_sequence_path, f)) for f in image_files]
+    else:
+        # Load multi-frame TIFF
+        frames = io.imread(image_sequence_path)
+        if frames.ndim == 3:  # Check if it's a time series
+            pass
+        else:
+            return "Error: Input is not a valid time-lapse image sequence"
+
+    # Step 1: Detect cells in each frame
+    features_list = []
+    for i, frame in enumerate(frames):
+        # Detect cells (particles) in the frame
+        features = tp.locate(frame, diameter=15, minmass=100)
+        if features is not None and not features.empty:
+            features["frame"] = i
+            features_list.append(features)
+
+    if not features_list:
+        return "Error: No cells detected in the image sequence"
+
+    # Combine all features
+    all_features = pd.concat(features_list)
+
+    # Save raw detections to output directory
+    raw_detections_file = os.path.join(output_dir, "raw_cell_detections.csv")
+    all_features.to_csv(raw_detections_file, index=False)
+
+    # Step 2: Link features into cell trajectories
+    trajectories = tp.link_df(all_features, search_range=10, memory=3)
+
+    # Save linked trajectories before filtering
+    # Reset index to ensure frame is a column, not an index level
+    trajectories_reset = trajectories.reset_index(drop=True)
+    all_trajectories_file = os.path.join(output_dir, "all_trajectories.csv")
+    trajectories_reset.to_csv(all_trajectories_file, index=False)
+
+    # Step 3: Filter trajectories to get only the ones that appear in enough frames
+    trajectories = tp.filter_stubs(trajectories, threshold=min_track_length)
+
+    if trajectories.empty:
+        return "No complete cell tracks found. Try adjusting parameters."
+
+    # Save filtered trajectories
+    filtered_trajectories_file = os.path.join(output_dir, "filtered_trajectories.csv")
+    # Reset index again to be safe
+    trajectories = trajectories.reset_index(drop=True)
+    trajectories.to_csv(filtered_trajectories_file, index=False)
+
+    # Step 4: Calculate migration metrics for each cell
+    cell_ids = trajectories["particle"].unique()
+    metrics = []
+
+    for cell_id in cell_ids:
+        cell_track = trajectories[trajectories["particle"] == cell_id].sort_values("frame")
+
+        # Convert pixel positions to micrometers
+        cell_track["x_um"] = cell_track["x"] * pixel_size_um
+        cell_track["y_um"] = cell_track["y"] * pixel_size_um
+
+        # Calculate displacements between consecutive frames
+        dx = np.diff(cell_track["x_um"])
+        dy = np.diff(cell_track["y_um"])
+
+        # Calculate step distances
+        step_distances = np.sqrt(dx**2 + dy**2)
+
+        # Calculate total path length
+        path_length = np.sum(step_distances)
+
+        # Calculate net displacement (straight-line distance from start to end)
+        start_x, start_y = cell_track.iloc[0][["x_um", "y_um"]]
+        end_x, end_y = cell_track.iloc[-1][["x_um", "y_um"]]
+        net_displacement = np.sqrt((end_x - start_x) ** 2 + (end_y - start_y) ** 2)
+
+        # Calculate directionality ratio (net displacement / path length)
+        directionality = net_displacement / path_length if path_length > 0 else 0
+
+        # Calculate speed (μm/min)
+        time_tracked = (cell_track["frame"].max() - cell_track["frame"].min()) * time_interval_min
+        speed = path_length / time_tracked if time_tracked > 0 else 0
+
+        metrics.append(
+            {
+                "cell_id": cell_id,
+                "frames_tracked": len(cell_track),
+                "speed_um_per_min": speed,
+                "directionality": directionality,
+                "displacement_um": net_displacement,
+                "path_length_um": path_length,
+            }
+        )
+
+    # Convert metrics to DataFrame
+    metrics_df = pd.DataFrame(metrics)
+
+    # Save metrics to CSV
+    metrics_file = os.path.join(output_dir, "cell_migration_metrics.csv")
+    metrics_df.to_csv(metrics_file, index=False)
+
+    # Calculate summary statistics
+    summary = {
+        "num_cells_tracked": len(metrics_df),
+        "avg_speed": metrics_df["speed_um_per_min"].mean(),
+        "std_speed": metrics_df["speed_um_per_min"].std(),
+        "avg_directionality": metrics_df["directionality"].mean(),
+        "std_directionality": metrics_df["directionality"].std(),
+        "avg_displacement": metrics_df["displacement_um"].mean(),
+        "std_displacement": metrics_df["displacement_um"].std(),
+    }
+
+    # Save summary statistics
+    summary_file = os.path.join(output_dir, "migration_summary.csv")
+    pd.DataFrame([summary]).to_csv(summary_file, index=False)
+
+    # Save trajectories visualization
+    fig, ax = plt.figure(figsize=(8, 8)), plt.gca()
+    tp.plot_traj(trajectories, ax=ax)
+    plt.title("Cell Migration Trajectories")
+    plt.xlabel("x position (pixels)")
+    plt.ylabel("y position (pixels)")
+
+    trajectories_file = os.path.join(output_dir, "cell_trajectories.png")
+    plt.savefig(trajectories_file)
+    plt.close()
+
+    # Create a rose plot to show migration directionality
+    fig, ax = plt.subplots(subplot_kw={"projection": "polar"}, figsize=(8, 8))
+
+    # Calculate angles for each cell (end position relative to start)
+    angles = []
+    for cell_id in cell_ids:
+        cell_track = trajectories[trajectories["particle"] == cell_id].sort_values("frame")
+        start_x, start_y = cell_track.iloc[0][["x", "y"]]
+        end_x, end_y = cell_track.iloc[-1][["x", "y"]]
+        dx, dy = end_x - start_x, end_y - start_y
+        angle = np.arctan2(dy, dx)
+        angles.append(angle)
+
+    # Plot the histogram
+    bins = np.linspace(-np.pi, np.pi, 16)
+    ax.hist(angles, bins=bins)
+    ax.set_title("Cell Migration Directionality")
+
+    # Save the rose plot
+    rose_plot_file = os.path.join(output_dir, "rose_plot.png")
+    plt.savefig(rose_plot_file)
+    plt.close()
+
+    # Create a displacement plot
+    plt.figure(figsize=(10, 6))
+    plt.bar(range(len(metrics_df)), metrics_df["displacement_um"])
+    plt.xlabel("Cell ID")
+    plt.ylabel("Displacement (μm)")
+    plt.title("Cell Displacements")
+
+    # Save the displacement plot
+    displacement_plot_file = os.path.join(output_dir, "track_displacement_plot.png")
+    plt.savefig(displacement_plot_file)
+    plt.close()
+
+    # Generate research log
+    log = f"""
+Cell Migration Analysis Research Log:
+
+1. Analyzed time-lapse sequence with {len(frames)} frames
+2. Detected and tracked {len(cell_ids)} cells that persisted for at least {min_track_length} frames
+3. Calculated key migration metrics:
+   - Average speed: {summary["avg_speed"]:.2f} ± {summary["std_speed"]:.2f} μm/min
+   - Average directionality ratio: {summary["avg_directionality"]:.2f} ± {summary["std_directionality"]:.2f}
+   - Average displacement: {summary["avg_displacement"]:.2f} ± {summary["std_displacement"]:.2f} μm
+
+4. Files saved:
+   - Raw cell detections: {raw_detections_file}
+   - All cell trajectories: {all_trajectories_file}
+   - Filtered trajectories: {filtered_trajectories_file}
+   - Detailed cell metrics: {metrics_file}
+   - Summary statistics: {summary_file}
+   - Cell trajectories visualization: {trajectories_file}
+   - Direction rose plot: {rose_plot_file}
+   - Cell displacement plot: {displacement_plot_file}
+
+Note: Analysis used pixel size of {pixel_size_um} μm and time interval of {time_interval_min} min between frames.
+    """
+
+    return log.strip()
+
+
+def perform_crispr_cas9_genome_editing(guide_rna_sequences, target_genomic_loci, cell_tissue_type):
+    """Simulates CRISPR-Cas9 genome editing process including guide RNA design, delivery, and analysis.
+
+    Parameters
+    ----------
+    guide_rna_sequences : list of str
+        List of guide RNA sequences (20 nucleotides each) targeting the genomic region of interest
+
+    target_genomic_loci : str
+        Target genomic sequence to be edited (should be longer than guide RNA and contain the target sites)
+
+    cell_tissue_type : str
+        Type of cell or tissue being edited (affects delivery efficiency and editing outcomes)
+
+    Returns
+    -------
+    str
+        Research log detailing the CRISPR-Cas9 editing process, including steps taken and results
+
+    """
+    import os
+    import random
+    from datetime import datetime
+
+    # Initialize research log
+    log = "CRISPR-Cas9 Genome Editing Research Log\n"
+    log += f"Date: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n"
+    log += f"Cell/Tissue Type: {cell_tissue_type}\n\n"
+
+    # Step 1: Validate guide RNA sequences
+    log += "STEP 1: Guide RNA Validation\n"
+    valid_guides = []
+
+    for i, guide in enumerate(guide_rna_sequences):
+        if len(guide) != 20:
+            log += f"  Guide {i + 1}: INVALID - Guide RNA must be 20 nucleotides (current length: {len(guide)})\n"
+            continue
+
+        if not all(n in "ATGC" for n in guide.upper()):
+            log += f"  Guide {i + 1}: INVALID - Guide RNA contains invalid nucleotides\n"
+            continue
+
+        # Calculate GC content (affects guide efficiency)
+        gc_content = (guide.upper().count("G") + guide.upper().count("C")) / len(guide) * 100
+        efficiency_score = 0
+
+        if 40 <= gc_content <= 60:
+            efficiency_score += 1
+            gc_quality = "Optimal"
+        else:
+            gc_quality = "Suboptimal"
+
+        log += f"  Guide {i + 1}: VALID - {guide} (GC content: {gc_content:.1f}% - {gc_quality})\n"
+        valid_guides.append((guide, efficiency_score))
+
+    if not valid_guides:
+        log += "\nNo valid guide RNAs found. Genome editing cannot proceed.\n"
+        return log
+
+    # Step 2: Target site identification
+    log += "\nSTEP 2: Target Site Identification\n"
+
+    target_seq = target_genomic_loci.upper()
+    target_matches = []
+
+    for i, (guide, score) in enumerate(valid_guides):
+        # Find guide RNA target in genomic sequence (including PAM site NGG)
+        guide.upper() + "NGG"
+
+        # Check if guide sequence is in target (simplified)
+        if guide.upper() in target_seq:
+            position = target_seq.find(guide.upper())
+            # Check if there's a PAM sequence (NGG) after the guide
+            if position + len(guide) + 2 <= len(target_seq):
+                potential_pam = target_seq[position + len(guide) : position + len(guide) + 3]
+                if potential_pam[1:3] == "GG":
+                    pam_quality = "Found"
+                    score += 2
+                else:
+                    pam_quality = "Not found"
+            else:
+                pam_quality = "Out of bounds"
+
+            log += f"  Guide {i + 1}: Found at position {position} (PAM: {pam_quality})\n"
+            target_matches.append((guide, position, score))
+        else:
+            log += f"  Guide {i + 1}: No match found in target sequence\n"
+
+    if not target_matches:
+        log += "\nNo matching target sites found. Genome editing cannot proceed.\n"
+        return log
+
+    # Step 3: Simulate CRISPR-Cas9 delivery
+    log += "\nSTEP 3: CRISPR-Cas9 Delivery Simulation\n"
+
+    # Cell-specific delivery efficiencies (simplified model)
+    delivery_efficiencies = {
+        "hek293": 0.85,
+        "hela": 0.75,
+        "ipsc": 0.60,
+        "primary_neuron": 0.40,
+        "hematopoietic_stem_cell": 0.55,
+        "mouse_embryo": 0.70,
+        "plant_cell": 0.30,
+    }
+
+    # Get delivery efficiency based on cell type (default to 0.5 if unknown)
+    cell_type_key = cell_tissue_type.lower().replace(" ", "_")
+    delivery_efficiency = delivery_efficiencies.get(cell_type_key, 0.5)
+
+    log += f"  Delivery method: Lipofection for {cell_tissue_type}\n"
+    log += f"  Estimated delivery efficiency: {delivery_efficiency * 100:.1f}%\n"
+
+    # Step 4: Simulate genome editing
+    log += "\nSTEP 4: Genome Editing Simulation\n"
+
+    # Select best guide based on score
+    best_guide, best_position, best_score = sorted(target_matches, key=lambda x: x[2], reverse=True)[0]
+
+    log += f"  Selected guide RNA: {best_guide} (highest efficiency score)\n"
+    log += f"  Target position: {best_position} to {best_position + len(best_guide) - 1}\n"
+
+    # Simulate editing outcome
+    edit_success_rate = delivery_efficiency * (0.5 + (best_score * 0.1))  # Between 50-90% based on guide quality
+
+    # Cut site (typically 3 bases upstream of PAM)
+    cut_position = best_position + len(best_guide) - 3
+    log += f"  Predicted cut site: Between positions {cut_position} and {cut_position + 1}\n"
+
+    # Simulate editing outcomes
+    indel_size = random.randint(1, 5)  # Random indel size between 1-5 bp
+
+    # Create modified sequence (simulate a deletion for simplicity)
+    modified_sequence = target_seq[:cut_position] + target_seq[cut_position + indel_size :]
+
+    log += f"  Simulated edit: {indel_size}bp deletion at cut site\n"
+    log += f"  Predicted editing efficiency: {edit_success_rate * 100:.1f}%\n"
+
+    # Step 5: Analysis of editing outcomes
+    log += "\nSTEP 5: Editing Outcome Analysis\n"
+
+    # Calculate basic stats
+    log += f"  Original sequence length: {len(target_seq)} bp\n"
+    log += f"  Modified sequence length: {len(modified_sequence)} bp\n"
+
+    # Save sequences to files
+    os.makedirs("crispr_results", exist_ok=True)
+
+    original_file = "crispr_results/original_sequence.txt"
+    with open(original_file, "w") as f:
+        f.write(f">Original_Sequence\n{target_seq}\n")
+
+    modified_file = "crispr_results/modified_sequence.txt"
+    with open(modified_file, "w") as f:
+        f.write(f">Modified_Sequence\n{modified_sequence}\n")
+
+    log += f"  Original sequence saved to: {original_file}\n"
+    log += f"  Modified sequence saved to: {modified_file}\n"
+
+    # Summary
+    log += "\nSUMMARY:\n"
+    log += f"  CRISPR-Cas9 editing successfully simulated for {cell_tissue_type}\n"
+    log += f"  {indel_size}bp deletion introduced at position {cut_position}\n"
+    log += f"  Expected success rate in cell population: {edit_success_rate * 100:.1f}%\n"
+
+    return log
+
+
+def analyze_calcium_imaging_data(image_stack_path, output_dir="./"):
+    """Analyze calcium imaging data to quantify neuronal activity metrics.
+
+    This function processes fluorescence microscopy images of GCaMP-labeled neurons
+    to extract quantitative metrics of neuronal activity, including cell counts,
+    event rates, decay times, and signal-to-noise ratios.
+
+    Parameters
+    ----------
+    image_stack_path : str
+        Path to the time-series stack of fluorescence microscopy images (TIFF format)
+    output_dir : str, optional
+        Directory to save output files (default: "./")
+
+    Returns
+    -------
+    str
+        Research log summarizing the analysis steps and results
+
+    """
+    import os
+
+    import numpy as np
+    import pandas as pd
+    from scipy import ndimage, signal
+    from scipy.optimize import curve_fit
+    from skimage import feature, filters, io, measure, segmentation
+
+    # Create output directory if it doesn't exist
+    os.makedirs(output_dir, exist_ok=True)
+
+    # Step 1: Load the image stack
+    log = "CALCIUM IMAGING ANALYSIS LOG\n"
+    log += "===========================\n\n"
+    log += f"Loading image stack from: {image_stack_path}\n"
+
+    try:
+        image_stack = io.imread(image_stack_path)
+        num_frames, height, width = image_stack.shape
+        log += f"Successfully loaded {num_frames} frames of size {height}x{width}\n\n"
+    except Exception as e:
+        return f"Error loading image stack: {str(e)}"
+
+    # Step 2: Calculate mean image for segmentation
+    log += "Step 1: Preprocessing and neuron segmentation\n"
+    mean_image = np.mean(image_stack, axis=0)
+
+    # Apply Gaussian filter to reduce noise
+    smooth_mean = filters.gaussian(mean_image, sigma=2)
+
+    # Step 3: Segment neurons using watershed
+    # Find local maxima (potential cell centers)
+    distance = ndimage.distance_transform_edt(smooth_mean)
+    # Create a mask for local maxima instead of using indices=False parameter
+    coordinates = feature.peak_local_max(distance, min_distance=10)
+
+    # Handle case where no local maxima are found
+    if len(coordinates) == 0:
+        log += "No local maxima detected. Using simple thresholding instead.\n"
+        # Create a simple binary mask using thresholding as fallback
+        binary_mask = smooth_mean > filters.threshold_otsu(smooth_mean)
+        markers = measure.label(binary_mask)
+    else:
+        local_max = np.zeros_like(distance, dtype=bool)
+        for coord in coordinates:
+            local_max[coord[0], coord[1]] = True
+        markers = measure.label(local_max)
+
+    # Watershed segmentation
+    segmented = segmentation.watershed(-smooth_mean, markers, mask=smooth_mean > filters.threshold_otsu(smooth_mean))
+
+    # Get region properties
+    regions = measure.regionprops(segmented)
+    cell_count = len(regions)
+    log += f"Detected {cell_count} neurons in the field of view\n\n"
+
+    # Step 4: Extract time-series data for each neuron
+    log += "Step 2: Extracting fluorescence time-series for each neuron\n"
+    time_series_data = []
+
+    for _i, region in enumerate(regions):
+        mask = segmented == region.label
+        cell_time_series = []
+
+        for frame in range(num_frames):
+            intensity = np.mean(image_stack[frame][mask])
+            cell_time_series.append(intensity)
+
+        time_series_data.append(cell_time_series)
+
+    time_series_array = np.array(time_series_data)
+
+    # Step 5: Detect calcium events and calculate metrics
+    log += "Step 3: Calculating neuronal activity metrics\n"
+
+    # Function to fit exponential decay
+    def exp_decay(x, a, tau, c):
+        return a * np.exp(-x / tau) + c
+
+    event_rates = []
+    decay_times = []
+    snr_values = []
+
+    for i, ts in enumerate(time_series_data):
+        # Normalize time series
+        baseline = np.percentile(ts, 20)
+        ts_norm = [(x - baseline) / baseline for x in ts]
+
+        # Simple event detection (threshold crossing)
+        threshold = np.std(ts_norm) * 2
+        events = []
+        in_event = False
+
+        for j, val in enumerate(ts_norm):
+            if not in_event and val > threshold:
+                events.append(j)
+                in_event = True
+            elif in_event and val < threshold:
+                in_event = False
+
+        # Calculate event rate (events per minute, assuming 10 Hz acquisition)
+        acquisition_rate = 10  # Hz (assumption)
+        recording_time_minutes = num_frames / acquisition_rate / 60
+        event_rate = len(events) / recording_time_minutes
+        event_rates.append(event_rate)
+
+        # Calculate decay times for detected events
+        cell_decay_times = []
+        for event_start in events:
+            if event_start + 30 < len(ts_norm):  # Ensure enough frames after event
+                event_window = ts_norm[event_start : event_start + 30]
+                peak_idx = np.argmax(event_window)
+                decay_segment = event_window[peak_idx:]
+
+                try:
+                    # Fit exponential decay
+                    x_data = np.arange(len(decay_segment))
+                    popt, _ = curve_fit(
+                        exp_decay,
+                        x_data,
+                        decay_segment,
+                        p0=[decay_segment[0], 5, decay_segment[-1]],
+                        bounds=([0, 0, 0], [np.inf, np.inf, np.inf]),
+                    )
+                    tau = popt[1]  # Decay time constant
+                    cell_decay_times.append(tau / acquisition_rate)  # Convert to seconds
+                except Exception:
+                    # Skip if curve fitting fails
+                    pass
+
+        if cell_decay_times:
+            decay_times.append(np.mean(cell_decay_times))
+        else:
+            decay_times.append(np.nan)
+
+        # Calculate signal-to-noise ratio
+        signal = np.mean([ts_norm[e] for e in events]) if events else 0  # noqa: F811
+        noise = np.std([ts_norm[i] for i in range(len(ts_norm)) if all(abs(i - e) > 5 for e in events)])
+        snr = signal / noise if noise > 0 else 0
+        snr_values.append(snr)
+
+    # Step 6: Compile and save results
+    cell_metrics = pd.DataFrame(
+        {
+            "Cell_ID": range(1, cell_count + 1),
+            "Event_Rate_per_min": event_rates,
+            "Decay_Time_sec": decay_times,
+            "SNR": snr_values,
+        }
+    )
+
+    metrics_file = os.path.join(output_dir, "neuron_activity_metrics.csv")
+    cell_metrics.to_csv(metrics_file, index=False)
+
+    # Save time series data
+    time_series_file = os.path.join(output_dir, "neuron_time_series.csv")
+    time_series_df = pd.DataFrame(time_series_array.T)
+    time_series_df.columns = [f"Cell_{i + 1}" for i in range(cell_count)]
+    time_series_df.to_csv(time_series_file, index=False)
+
+    # Step 7: Summarize results
+    log += f"Cell count: {cell_count}\n"
+    log += f"Average event rate: {np.nanmean(event_rates):.2f} events/min\n"
+    log += f"Average decay time: {np.nanmean(decay_times):.2f} seconds\n"
+    log += f"Average SNR: {np.nanmean(snr_values):.2f}\n\n"
+
+    log += "Step 4: Results saved to files\n"
+    log += f"Neuron activity metrics saved to: {metrics_file}\n"
+    log += f"Time series data saved to: {time_series_file}\n"
+
+    return log
+
+
+def analyze_in_vitro_drug_release_kinetics(
+    time_points,
+    concentration_data,
+    drug_name="Drug",
+    total_drug_loaded=None,
+    output_dir="./",
+):
+    """Analyzes in vitro drug release kinetics from biomaterial formulations.
+
+    Parameters
+    ----------
+    time_points : list or numpy.ndarray
+        Time points at which drug concentrations were measured (in hours)
+    concentration_data : list or numpy.ndarray
+        Measured drug concentration at each time point
+    drug_name : str, optional
+        Name of the drug being analyzed (default: "Drug")
+    total_drug_loaded : float, optional
+        Total amount of drug initially loaded in the formulation.
+        If None, the maximum concentration is used as 100% (default: None)
+    output_dir : str, optional
+        Directory to save output files (default: "./")
+
+    Returns
+    -------
+    str
+        Research log summarizing the analysis steps, results, and saved file locations
+
+    """
+    import os
+    from datetime import datetime
+
+    import matplotlib.pyplot as plt
+    import numpy as np
+    import pandas as pd
+    from scipy.optimize import curve_fit
+
+    # Ensure output directory exists
+    os.makedirs(output_dir, exist_ok=True)
+
+    # Convert inputs to numpy arrays
+    time_points = np.array(time_points)
+    concentration_data = np.array(concentration_data)
+
+    # Calculate cumulative release percentage
+    if total_drug_loaded is None:
+        total_drug_loaded = np.max(concentration_data)
+
+    cumulative_release = (concentration_data / total_drug_loaded) * 100
+
+    # Create a DataFrame for easier manipulation
+    release_df = pd.DataFrame(
+        {
+            "Time (hours)": time_points,
+            "Concentration": concentration_data,
+            "Cumulative Release (%)": cumulative_release,
+        }
+    )
+
+    # Calculate release rate (simple approximation using differences)
+    release_df["Release Rate"] = np.gradient(release_df["Cumulative Release (%)"], release_df["Time (hours)"])
+
+    # Define kinetic models
+    def zero_order(t, k):
+        return k * t
+
+    def first_order(t, k):
+        return 100 * (1 - np.exp(-k * t))
+
+    def higuchi(t, k):
+        return k * np.sqrt(t)
+
+    def korsmeyer_peppas(t, k, n):
+        return 100 * (k * t) ** n
+
+    # Fit data to different kinetic models
+    models = {}
+    r2_values = {}
+
+    # Zero-order kinetics
+    try:
+        params, _ = curve_fit(zero_order, time_points, cumulative_release)
+        y_pred = zero_order(time_points, *params)
+        ss_total = np.sum((cumulative_release - np.mean(cumulative_release)) ** 2)
+        ss_residual = np.sum((cumulative_release - y_pred) ** 2)
+        r2 = 1 - (ss_residual / ss_total)
+        models["Zero-order"] = {
+            "params": params,
+            "equation": f"Release = {params[0]:.4f} * t",
+            "pred": y_pred,
+        }
+        r2_values["Zero-order"] = r2
+    except Exception:
+        models["Zero-order"] = {
+            "params": None,
+            "equation": "Fitting failed",
+            "pred": None,
+        }
+        r2_values["Zero-order"] = 0
+
+    # First-order kinetics
+    try:
+        params, _ = curve_fit(first_order, time_points, cumulative_release, bounds=(0, [1]))
+        y_pred = first_order(time_points, *params)
+        ss_total = np.sum((cumulative_release - np.mean(cumulative_release)) ** 2)
+        ss_residual = np.sum((cumulative_release - y_pred) ** 2)
+        r2 = 1 - (ss_residual / ss_total)
+        models["First-order"] = {
+            "params": params,
+            "equation": f"Release = 100 * (1 - exp(-{params[0]:.4f} * t))",
+            "pred": y_pred,
+        }
+        r2_values["First-order"] = r2
+    except Exception:
+        models["First-order"] = {
+            "params": None,
+            "equation": "Fitting failed",
+            "pred": None,
+        }
+        r2_values["First-order"] = 0
+
+    # Higuchi model
+    try:
+        params, _ = curve_fit(higuchi, time_points, cumulative_release)
+        y_pred = higuchi(time_points, *params)
+        ss_total = np.sum((cumulative_release - np.mean(cumulative_release)) ** 2)
+        ss_residual = np.sum((cumulative_release - y_pred) ** 2)
+        r2 = 1 - (ss_residual / ss_total)
+        models["Higuchi"] = {
+            "params": params,
+            "equation": f"Release = {params[0]:.4f} * sqrt(t)",
+            "pred": y_pred,
+        }
+        r2_values["Higuchi"] = r2
+    except Exception:
+        models["Higuchi"] = {"params": None, "equation": "Fitting failed", "pred": None}
+        r2_values["Higuchi"] = 0
+
+    # Korsmeyer-Peppas model
+    try:
+        # Only use the first 60% of release data for Korsmeyer-Peppas model
+        mask = cumulative_release <= 60
+        if sum(mask) >= 3:  # Need at least 3 points for fitting
+            params, _ = curve_fit(
+                korsmeyer_peppas,
+                time_points[mask],
+                cumulative_release[mask],
+                bounds=([0, 0], [1, 1]),
+            )
+            y_pred = korsmeyer_peppas(time_points, *params)
+            ss_total = np.sum((cumulative_release - np.mean(cumulative_release)) ** 2)
+            ss_residual = np.sum((cumulative_release - y_pred) ** 2)
+            r2 = 1 - (ss_residual / ss_total)
+            models["Korsmeyer-Peppas"] = {
+                "params": params,
+                "equation": f"Release = 100 * ({params[0]:.4f} * t)^{params[1]:.4f}",
+                "pred": y_pred,
+            }
+            r2_values["Korsmeyer-Peppas"] = r2
+        else:
+            models["Korsmeyer-Peppas"] = {
+                "params": None,
+                "equation": "Insufficient data points",
+                "pred": None,
+            }
+            r2_values["Korsmeyer-Peppas"] = 0
+    except Exception:
+        models["Korsmeyer-Peppas"] = {
+            "params": None,
+            "equation": "Fitting failed",
+            "pred": None,
+        }
+        r2_values["Korsmeyer-Peppas"] = 0
+
+    # Determine best model based on R² value
+    best_model = max(r2_values, key=r2_values.get)
+
+    # Calculate half-life (time to 50% release)
+    try:
+        # Use best model to calculate half-life
+        if best_model == "Zero-order":
+            k = models[best_model]["params"][0]
+            half_life = 50 / k if k > 0 else float("inf")
+        elif best_model == "First-order":
+            k = models[best_model]["params"][0]
+            half_life = -np.log(0.5) / k if k > 0 else float("inf")
+        elif best_model == "Higuchi":
+            k = models[best_model]["params"][0]
+            half_life = (50 / k) ** 2 if k > 0 else float("inf")
+        elif best_model == "Korsmeyer-Peppas":
+            k, n = models[best_model]["params"]
+            half_life = (0.5 ** (1 / n)) / k if k > 0 else float("inf")
+        else:
+            # Interpolate from data if model fitting failed
+            from scipy.interpolate import interp1d
+
+            if np.max(cumulative_release) >= 50:
+                f = interp1d(cumulative_release, time_points)
+                half_life = float(f(50))
+            else:
+                half_life = "Not reached"
+    except Exception:
+        half_life = "Could not calculate"
+
+    # Create plots
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+
+    # 1. Cumulative release plot with model fits
+    plt.figure(figsize=(10, 6))
+    plt.plot(time_points, cumulative_release, "o-", label="Experimental data")
+
+    for model_name, model_data in models.items():
+        if model_data["pred"] is not None:
+            plt.plot(
+                time_points,
+                model_data["pred"],
+                "--",
+                label=f"{model_name} (R² = {r2_values[model_name]:.4f})",
+            )
+
+    plt.xlabel("Time (hours)")
+    plt.ylabel("Cumulative Release (%)")
+    plt.title(f"In Vitro Release Profile of {drug_name}")
+    plt.legend()
+    plt.grid(True, linestyle="--", alpha=0.7)
+    cumulative_plot_path = os.path.join(output_dir, f"cumulative_release_{timestamp}.png")
+    plt.savefig(cumulative_plot_path, dpi=300, bbox_inches="tight")
+    plt.close()
+
+    # 2. Release rate plot
+    plt.figure(figsize=(10, 6))
+    plt.plot(time_points, release_df["Release Rate"], "o-")
+    plt.xlabel("Time (hours)")
+    plt.ylabel("Release Rate (%/hour)")
+    plt.title(f"Release Rate of {drug_name}")
+    plt.grid(True, linestyle="--", alpha=0.7)
+    rate_plot_path = os.path.join(output_dir, f"release_rate_{timestamp}.png")
+    plt.savefig(rate_plot_path, dpi=300, bbox_inches="tight")
+    plt.close()
+
+    # Save data to CSV
+    csv_path = os.path.join(output_dir, f"drug_release_data_{timestamp}.csv")
+    release_df.to_csv(csv_path, index=False)
+
+    # Generate research log
+    log = f"""
+# In Vitro Drug Release Kinetics Analysis for {drug_name}
+
+## Analysis Summary
+- **Date/Time:** {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}
+- **Drug Analyzed:** {drug_name}
+- **Time Range:** {min(time_points)} to {max(time_points)} hours
+- **Number of Data Points:** {len(time_points)}
+- **Maximum Release Achieved:** {max(cumulative_release):.2f}%
+
+## Kinetic Models Analysis
+The release data was fitted to four standard kinetic models:
+
+1. **Zero-order Model:** {models["Zero-order"]["equation"]} (R² = {r2_values["Zero-order"]:.4f})
+2. **First-order Model:** {models["First-order"]["equation"]} (R² = {r2_values["First-order"]:.4f})
+3. **Higuchi Model:** {models["Higuchi"]["equation"]} (R² = {r2_values["Higuchi"]:.4f})
+4. **Korsmeyer-Peppas Model:** {models["Korsmeyer-Peppas"]["equation"]} (R² = {r2_values["Korsmeyer-Peppas"]:.4f})
+
+**Best-fitting Model:** {best_model} (R² = {r2_values[best_model]:.4f})
+
+## Release Metrics
+- **Half-life (t50%):** {half_life if isinstance(half_life, str) else f"{half_life:.2f} hours"}
+- **Initial Release Rate:** {release_df["Release Rate"].iloc[0]:.4f} %/hour
+- **Average Release Rate:** {np.mean(release_df["Release Rate"]):.4f} %/hour
+
+## Files Generated
+1. Cumulative Release Plot: {cumulative_plot_path}
+2. Release Rate Plot: {rate_plot_path}
+3. Data CSV: {csv_path}
+
+## Interpretation
+The drug release profile of {drug_name} best follows a {
+        best_model
+    } kinetic model, which suggests that the release mechanism is primarily driven by {
+        "diffusion through a porous matrix"
+        if best_model == "Higuchi"
+        else "diffusion with erosion"
+        if best_model == "Korsmeyer-Peppas" and 0.43 <= models[best_model]["params"][1] <= 0.85
+        else "Fickian diffusion"
+        if best_model == "Korsmeyer-Peppas" and models[best_model]["params"][1] < 0.43
+        else "case-II transport"
+        if best_model == "Korsmeyer-Peppas" and models[best_model]["params"][1] > 0.85
+        else "concentration-dependent diffusion"
+        if best_model == "First-order"
+        else "constant release rate independent of concentration"
+        if best_model == "Zero-order"
+        else "complex mechanisms"
+    }.
+"""
+
+    return log.strip()
+
+
+def analyze_myofiber_morphology(
+    image_path,
+    nuclei_channel=2,
+    myofiber_channel=1,
+    threshold_method="otsu",
+    output_dir="./",
+):
+    """Quantifies morphological properties of myofibers in microscopy images of tissue sections.
+
+    Parameters
+    ----------
+    image_path : str
+        Path to the microscopy image file (typically a multichannel image with nuclei and myofiber staining)
+    nuclei_channel : int, default=2
+        Channel index containing nuclei staining (DAPI, Hoechst, etc.)
+    myofiber_channel : int, default=1
+        Channel index containing myofiber staining (α-Actinin, etc.)
+    threshold_method : str, default='otsu'
+        Method for thresholding ('otsu', 'adaptive', or 'manual')
+    output_dir : str, default='./'
+        Directory to save output files
+
+    Returns
+    -------
+    str
+        Research log summarizing the analysis steps and results
+
+    """
+    import os
+    from datetime import datetime
+
+    import numpy as np
+    import pandas as pd
+    from skimage import exposure, filters, io, measure, morphology
+    from skimage.color import label2rgb
+
+    # Create output directory if it doesn't exist
+    os.makedirs(output_dir, exist_ok=True)
+
+    # Load the image
+    image = io.imread(image_path)
+
+    # Extract channels (assuming multichannel image)
+    if len(image.shape) > 2:
+        if len(image.shape) == 3:
+            # RGB image
+            nuclei_img = image[:, :, nuclei_channel] if nuclei_channel < image.shape[2] else image[:, :, 0]
+            myofiber_img = image[:, :, myofiber_channel] if myofiber_channel < image.shape[2] else image[:, :, 1]
+        else:
+            # Multichannel image (e.g., from confocal)
+            nuclei_img = image[nuclei_channel, :, :] if nuclei_channel < image.shape[0] else image[0, :, :]
+            myofiber_img = image[myofiber_channel, :, :] if myofiber_channel < image.shape[0] else image[1, :, :]
+    else:
+        # Single channel image - can't separate nuclei and myofibers
+        return "Error: Input image must be multichannel to separate nuclei and myofibers"
+
+    # Enhance contrast
+    nuclei_img = exposure.equalize_adapthist(nuclei_img)
+    myofiber_img = exposure.equalize_adapthist(myofiber_img)
+
+    # Segment nuclei
+    if threshold_method == "otsu":
+        nuclei_thresh = filters.threshold_otsu(nuclei_img)
+    elif threshold_method == "adaptive":
+        nuclei_thresh = filters.threshold_local(nuclei_img, block_size=35)
+    else:  # manual
+        nuclei_thresh = np.mean(nuclei_img) * 1.5
+
+    nuclei_binary = nuclei_img > nuclei_thresh
+    nuclei_binary = morphology.remove_small_objects(nuclei_binary, min_size=30)
+    nuclei_binary = morphology.binary_closing(nuclei_binary)
+
+    # Label nuclei
+    nuclei_labels = measure.label(nuclei_binary)
+    nuclei_props = measure.regionprops(nuclei_labels)
+
+    # Segment myofibers
+    if threshold_method == "otsu":
+        myofiber_thresh = filters.threshold_otsu(myofiber_img)
+    elif threshold_method == "adaptive":
+        myofiber_thresh = filters.threshold_local(myofiber_img, block_size=101)
+    else:  # manual
+        myofiber_thresh = np.mean(myofiber_img) * 1.2
+
+    myofiber_binary = myofiber_img > myofiber_thresh
+    myofiber_binary = morphology.remove_small_objects(myofiber_binary, min_size=500)
+    myofiber_binary = morphology.binary_closing(myofiber_binary, morphology.disk(3))
+
+    # Label myofibers
+    myofiber_labels = measure.label(myofiber_binary)
+    myofiber_props = measure.regionprops(myofiber_labels)
+
+    # Count nuclei inside myofibers
+    nuclei_inside = 0
+    nuclei_total = len(nuclei_props)
+
+    for nucleus in nuclei_props:
+        y, x = nucleus.centroid
+        y, x = int(y), int(x)
+        if myofiber_binary[y, x]:
+            nuclei_inside += 1
+
+    percent_inside = nuclei_inside / nuclei_total * 100 if nuclei_total > 0 else 0
+
+    # Calculate myofiber morphological properties
+    myofiber_data = []
+    for fiber in myofiber_props:
+        myofiber_data.append(
+            {
+                "Area": fiber.area,
+                "Perimeter": fiber.perimeter,
+                "Eccentricity": fiber.eccentricity,
+                "Solidity": fiber.solidity,
+                "Orientation": fiber.orientation,
+            }
+        )
+
+    # Save results
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    results_file = f"{output_dir}/myofiber_analysis_{timestamp}.csv"
+
+    if myofiber_data:
+        df = pd.DataFrame(myofiber_data)
+        df.to_csv(results_file, index=False)
+
+        # Calculate summary statistics
+        mean_area = df["Area"].mean()
+        mean_perimeter = df["Perimeter"].mean()
+        mean_eccentricity = df["Eccentricity"].mean()
+    else:
+        mean_area = mean_perimeter = mean_eccentricity = 0
+
+    # Save labeled image
+    labeled_image = label2rgb(myofiber_labels, image=myofiber_img)
+    labeled_image_path = f"{output_dir}/labeled_myofibers_{timestamp}.png"
+    io.imsave(labeled_image_path, (labeled_image * 255).astype(np.uint8))
+
+    # Create research log
+    log = f"""
+MYOFIBER MORPHOLOGICAL ANALYSIS REPORT
+======================================
+Date: {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}
+Image: {image_path}
+
+ANALYSIS STEPS:
+1. Loaded multichannel microscopy image
+2. Extracted nuclei (channel {nuclei_channel}) and myofiber (channel {myofiber_channel}) signals
+3. Enhanced contrast using adaptive histogram equalization
+4. Segmented nuclei using {threshold_method} thresholding
+5. Segmented myofibers using {threshold_method} thresholding
+6. Performed morphological operations to refine segmentation
+7. Identified and measured individual myofibers and nuclei
+
+RESULTS:
+- Total myofibers detected: {len(myofiber_props)}
+- Total nuclei detected: {nuclei_total}
+- Nuclei inside myofibers: {nuclei_inside} ({percent_inside:.2f}%)
+- Mean myofiber area: {mean_area:.2f} pixels
+- Mean myofiber perimeter: {mean_perimeter:.2f} pixels
+- Mean myofiber eccentricity: {mean_eccentricity:.2f}
+
+FILES GENERATED:
+- Morphological measurements: {results_file}
+- Labeled myofiber image: {labeled_image_path}
+"""
+
+    return log
+
+
+def decode_behavior_from_neural_trajectories(neural_data, behavioral_data, n_components=10, output_dir="./"):
+    """Model neural activity trajectories and decode behavioral variables.
+
+    Parameters
+    ----------
+    neural_data : numpy.ndarray
+        Neural spiking activity data, shape (n_timepoints, n_neurons)
+    behavioral_data : numpy.ndarray
+        Behavioral data, shape (n_timepoints, n_behavioral_variables)
+    n_components : int, optional
+        Number of principal components to use for dimensionality reduction, default is 10
+    output_dir : str, optional
+        Directory to save output files, default is "./"
+
+    Returns
+    -------
+    str
+        Research log summarizing the steps taken and results
+
+    """
+    import os
+    import pickle
+
+    import matplotlib.pyplot as plt
+    import numpy as np
+    from pykalman import KalmanFilter
+    from sklearn.decomposition import PCA
+    from sklearn.metrics import mean_squared_error
+    from sklearn.model_selection import train_test_split
+
+    # Create output directory if it doesn't exist
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+
+    # Initialize research log
+    log = "# Neural Trajectory Modeling and Decoding Research Log\n\n"
+
+    # Step 1: Preprocess the data
+    log += "## Step 1: Data Preprocessing\n"
+    log += f"- Neural data shape: {neural_data.shape}\n"
+    log += f"- Behavioral data shape: {behavioral_data.shape}\n"
+
+    # Check for NaN values and replace with zeros
+    neural_data = np.nan_to_num(neural_data)
+    behavioral_data = np.nan_to_num(behavioral_data)
+
+    # Split data into training and testing sets
+    X_train, X_test, y_train, y_test = train_test_split(neural_data, behavioral_data, test_size=0.2, random_state=42)
+    log += f"- Training set size: {X_train.shape[0]} samples\n"
+    log += f"- Testing set size: {X_test.shape[0]} samples\n\n"
+
+    # Step 2: Dimensionality reduction with PCA
+    log += "## Step 2: Dimensionality Reduction\n"
+    log += f"- Reducing neural data from {neural_data.shape[1]} dimensions to {n_components} components\n"
+
+    pca = PCA(n_components=n_components)
+    X_train_pca = pca.fit_transform(X_train)
+    X_test_pca = pca.transform(X_test)
+
+    explained_variance = np.sum(pca.explained_variance_ratio_) * 100
+    log += f"- Total variance explained: {explained_variance:.2f}%\n\n"
+
+    # Save PCA components visualization
+    try:
+        plt.figure(figsize=(10, 6))
+        plt.bar(range(1, n_components + 1), pca.explained_variance_ratio_)
+        plt.xlabel("Principal Component")
+        plt.ylabel("Explained Variance Ratio")
+        plt.title("PCA Components Explained Variance")
+        plt.xticks(range(1, n_components + 1))
+        plt.tight_layout()
+
+        pca_plot_path = os.path.join(output_dir, "pca_explained_variance.png")
+        plt.savefig(pca_plot_path, dpi=300)
+        plt.close()
+
+        log += f"- PCA components visualization saved to: {pca_plot_path}\n\n"
+    except Exception as e:
+        log += f"- Error creating PCA visualization: {str(e)}\n\n"
+
+    # Step 3: Train a Kalman filter for decoding
+    log += "## Step 3: Trajectory Modeling and Decoding\n"
+    log += "- Training Kalman filter to decode behavioral variables from neural trajectories\n"
+
+    # Initialize and train Kalman filter
+    kf = KalmanFilter(initial_state_mean=np.zeros(y_train.shape[1]), n_dim_obs=X_train_pca.shape[1])
+
+    # Fit the Kalman filter to the data
+    kf.em(X_train_pca, y_train)
+
+    # Step 4: Decode behavioral variables
+    log += "## Step 4: Decoding Behavioral Variables\n"
+
+    # Use the Kalman filter to predict behavioral variables
+    y_pred, _ = kf.filter(X_test_pca)
+
+    # Evaluate performance
+    mse = mean_squared_error(y_test, y_pred)
+    log += f"- Mean squared error on test set: {mse:.4f}\n\n"
+
+    # Save the decoded trajectories visualization
+    try:
+        if y_test.shape[1] >= 2:
+            # Create visualization of true vs. predicted trajectories (first 2 dimensions)
+            plt.figure(figsize=(12, 6))
+
+            # First behavioral variable
+            plt.subplot(1, 2, 1)
+            plt.plot(y_test[:, 0], label="True")
+            plt.plot(y_pred[:, 0], label="Predicted")
+            plt.xlabel("Time steps")
+            plt.ylabel("Behavioral Variable 1")
+            plt.title("Decoding Performance - Variable 1")
+            plt.legend()
+
+            # Second behavioral variable
+            plt.subplot(1, 2, 2)
+            plt.plot(y_test[:, 1], label="True")
+            plt.plot(y_pred[:, 1], label="Predicted")
+            plt.xlabel("Time steps")
+            plt.ylabel("Behavioral Variable 2")
+            plt.title("Decoding Performance - Variable 2")
+            plt.legend()
+
+            plt.tight_layout()
+
+            trajectory_plot_path = os.path.join(output_dir, "decoded_trajectories.png")
+            plt.savefig(trajectory_plot_path, dpi=300)
+            plt.close()
+
+            log += f"- Decoded trajectories visualization saved to: {trajectory_plot_path}\n"
+    except Exception as e:
+        log += f"- Error creating trajectory visualization: {str(e)}\n"
+
+    # Save the results as a pickle file
+    results = {
+        "true_behavior": y_test,
+        "predicted_behavior": y_pred,
+        "pca_model": pca,
+        "kalman_filter": kf,
+        "mse": mse,
+    }
+
+    results_file = os.path.join(output_dir, "neural_decoding_results.pkl")
+    with open(results_file, "wb") as f:
+        pickle.dump(results, f)
+
+    # Also save a CSV with the first few predicted vs. actual values for easier inspection
+    try:
+        import pandas as pd
+
+        n_samples = min(100, y_test.shape[0])
+        n_vars = y_test.shape[1]
+
+        results_data = {}
+        for i in range(n_vars):
+            results_data[f"true_var{i + 1}"] = y_test[:n_samples, i]
+            results_data[f"pred_var{i + 1}"] = y_pred[:n_samples, i]
+
+        results_df = pd.DataFrame(results_data)
+        csv_path = os.path.join(output_dir, "decoding_results_sample.csv")
+        results_df.to_csv(csv_path, index=False)
+
+        log += f"- Sample of decoding results saved to: {csv_path}\n"
+    except Exception as e:
+        log += f"- Error creating CSV results: {str(e)}\n"
+
+    log += "\n## Results\n"
+    log += f"- Full decoded behavioral trajectories saved to: {results_file}\n"
+    log += f"- Decoder performance (MSE): {mse:.4f}\n"
+
+    # Save the log to a file
+    log_file = os.path.join(output_dir, "neural_decoding_log.txt")
+    with open(log_file, "w") as f:
+        f.write(log)
+
+    log += f"- Analysis log saved to: {log_file}\n"
+
+    return log
+
+
+def simulate_whole_cell_ode_model(
+    initial_conditions,
+    parameters,
+    ode_function=None,
+    time_span=(0, 100),
+    time_points=1000,
+    method="LSODA",
+):
+    """Simulate a whole-cell model represented as a system of ordinary differential equations (ODEs).
+
+    Parameters
+    ----------
+    initial_conditions : dict or array-like
+        Initial values for each state variable in the model. If dict, keys are variable names
+        and values are initial concentrations/values. If array-like, order must match the
+        order expected by the ODE function.
+    parameters : dict
+        Model parameters required by the ODE function. Keys are parameter names and
+        values are parameter values.
+    ode_function : callable, optional
+        Function defining the system of ODEs. Should take arguments ``(t, y, *args)`` where
+        ``t`` is time, ``y`` is the state vector, and ``args`` contains additional parameters.
+        If None, a simple example whole-cell model will be used.
+    time_span : tuple, default=(0, 100)
+        Tuple of (start_time, end_time) for the simulation.
+    time_points : int, default=1000
+        Number of time points to evaluate.
+    method : str, default='LSODA'
+        Numerical integration method to use (e.g., 'RK45', 'LSODA', 'BDF').
+
+    Returns
+    -------
+    str
+        Research log summarizing the simulation steps and results. Results are saved
+        to a CSV file and the filename is included in the log.
+
+    """
+    from datetime import datetime
+
+    import numpy as np
+    import pandas as pd
+    from scipy.integrate import solve_ivp
+
+    # Define a default ODE function if none is provided
+    if ode_function is None:
+
+        def default_whole_cell_model(t, y, params):
+            # Unpack state variables
+            # Simple model with:
+            # - mRNA (y[0])
+            # - Protein (y[1])
+            # - Metabolite (y[2])
+            # - ATP (y[3])
+            mRNA, protein, metabolite, atp = y
+
+            # Unpack parameters
+            k_transcription = params["k_transcription"]  # mRNA synthesis rate
+            k_translation = params["k_translation"]  # Protein synthesis rate
+            k_mrna_deg = params["k_mrna_deg"]  # mRNA degradation rate
+            k_protein_deg = params["k_protein_deg"]  # Protein degradation rate
+            k_metabolism = params["k_metabolism"]  # Metabolite production rate
+            k_atp_production = params["k_atp_production"]  # ATP production rate
+            k_atp_consumption = params["k_atp_consumption"]  # ATP consumption rate
+
+            # ODEs
+            dmRNA_dt = k_transcription - k_mrna_deg * mRNA
+            dprotein_dt = k_translation * mRNA * atp - k_protein_deg * protein
+            dmetabolite_dt = k_metabolism * protein - k_atp_production * metabolite
+            datp_dt = k_atp_production * metabolite - k_atp_consumption * atp - k_translation * mRNA * atp
+
+            return [dmRNA_dt, dprotein_dt, dmetabolite_dt, datp_dt]
+
+        ode_function = default_whole_cell_model
+
+    # Prepare initial conditions as array
+    if isinstance(initial_conditions, dict):
+        y0_values = list(initial_conditions.values())
+        variable_names = list(initial_conditions.keys())
+    else:
+        y0_values = initial_conditions
+        variable_names = [f"Variable_{i}" for i in range(len(initial_conditions))]
+
+    # Set up time points
+    t_eval = np.linspace(time_span[0], time_span[1], time_points)
+
+    # Start research log
+    log = []
+    log.append("# Whole-Cell ODE Model Simulation")
+    log.append(f"Date: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+    log.append("\n## Simulation Setup")
+    log.append(f"- Integration method: {method}")
+    log.append(f"- Time span: {time_span[0]} to {time_span[1]} time units")
+    log.append(f"- Number of time points: {time_points}")
+    log.append(f"- Number of state variables: {len(y0_values)}")
+    log.append("\n## Initial Conditions")
+    for _i, (name, value) in enumerate(zip(variable_names, y0_values, strict=False)):
+        log.append(f"- {name}: {value}")
+
+    log.append("\n## Model Parameters")
+    for param, value in parameters.items():
+        log.append(f"- {param}: {value}")
+
+    # Solve the ODE system
+    log.append("\n## Running Simulation")
+    try:
+        solution = solve_ivp(
+            lambda t, y: ode_function(t, y, parameters),
+            time_span,
+            y0_values,
+            method=method,
+            t_eval=t_eval,
+        )
+
+        # Check if simulation was successful
+        if solution.success:
+            log.append("Simulation completed successfully.")
+            log.append(f"- Number of function evaluations: {solution.nfev}")
+            log.append(f"- Number of Jacobian evaluations: {solution.njev}")
+            log.append(f"- Number of steps: {len(solution.t)}")
+
+            # Create DataFrame with results
+            results_df = pd.DataFrame(solution.y.T, columns=variable_names)
+            results_df.insert(0, "Time", solution.t)
+
+            # Save results to CSV
+            filename = f"whole_cell_simulation_results_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv"
+            results_df.to_csv(filename, index=False)
+
+            log.append("\n## Results Summary")
+            log.append(f"Simulation results saved to: {filename}")
+
+            # Calculate some basic statistics
+            final_state = results_df.iloc[-1].drop("Time").to_dict()
+            log.append("\n## Final State")
+            for var, value in final_state.items():
+                log.append(f"- {var}: {value:.6f}")
+
+        else:
+            log.append(f"Simulation failed with message: {solution.message}")
+
+    except Exception as e:
+        log.append(f"Error during simulation: {str(e)}")
+
+    # Return the research log
+    return "\n".join(log)

BioScientist/agent_system/engines/v1_executor_backup/tool/example_mcp_tools/pubmed_mcp.py

@@ -0,0 +1,38 @@
+import os
+
+from Bio import Entrez
+from mcp.server.fastmcp import FastMCP
+from pydantic import BaseModel
+
+Entrez.email = os.getenv("NCBI_EMAIL") or "YOUR_EMAIL"
+mcp = FastMCP("pubmed-live")
+
+
+class Article(BaseModel):
+    pmid: str
+    title: str | None = None
+    journal: str | None = None
+    year: str | None = None
+    abstract: str | None = None
+
+
+@mcp.tool()
+def search_pubmed(query: str, max_results: int = 10) -> list[Article]:
+    ids = Entrez.read(Entrez.esearch(db="pubmed", term=query, retmax=max_results))["IdList"]
+    summaries = Entrez.read(Entrez.esummary(db="pubmed", id=",".join(ids))) if ids else []
+    articles: list[Article] = []
+    for s in summaries:
+        articles.append(
+            Article(pmid=s["Id"], title=s["Title"], journal=s.get("FullJournalName", ""), year=s.get("PubDate", "")[:4])
+        )
+    return articles
+
+
+@mcp.tool()
+def get_article_abstract(pmid: str) -> str:
+    with Entrez.efetch(db="pubmed", id=pmid, rettype="abstract", retmode="text") as h:
+        return h.read()
+
+
+if __name__ == "__main__":
+    mcp.run()

BioScientist/agent_system/engines/v1_executor_backup/tool/protocols/addgene/Addgene_ AAV Production.txt

@@ -0,0 +1,325 @@
+PROTOCOL: AAV Production
+SOURCE: https://www.addgene.org/protocols/aav-production-hek293-cells/
+================================================================================
+
+INTRODUCTION
+================================================================================
+
+This protocol can be used to produce AAV from one Five Chambers Cell-Stack (CS5)
+(3,180 cm² - the same surface area as 21 x T-175 flasks). Cell stacks provide an
+efficient means to scale-up without having to handle a large number of T-175
+flasks.
+
+
+WORKFLOW TIMELINE
+================================================================================
+
+Day 0: Seed cells in CS2
+
+Day 2: Seed cells in CS5
+
+Day 3 (am): Transfect cells
+
+Day 7 (am): Harvest cells
+
+
+EQUIPMENT
+================================================================================
+
+• Class II, Type A2 Biological Safety Cabinet
+• 0.5–10 µL single channel pipette
+• 2–20 µL single channel pipette
+• 20–200 µL single channel pipette
+• 200–1000 µL single channel pipette
+• Hemocytometer or cell counter
+• Ice bucket
+• CO₂ incubator
+• Pipet controller
+• Hazardous waste container
+• pH meter
+• Stir plate
+• Magnetic stir bar
+• Sonicator
+• Ear protection
+• Vortex
+
+
+REAGENTS
+================================================================================
+
+• Adherent HEK293T cells (ideally AAV-293T clones)
+
+  💡 PRO TIP: While adherent, these cells are very loosely attached to the dish
+  surface and should be handled carefully. Avoid touching the cells when replacing
+  media.
+
+• T-175 flask, Corning 430825, 175 cm²
+• Cellstack 5, Corning 3319, 3180 cm²
+• Cellstack 2, Corning 3269, 1272 cm²
+• Heat-inactivated FBS (HI-FBS)
+
+  💡 PRO TIP: Different brands and lots of FBS can promote or inhibit
+  transfection. Test a variety of brands and lots of FBS to find one suitable for
+  your protocols. FBS can be purchased already heat-inactivated, or it can be
+  inactivated in the lab by heating to 56 °C for 30 minutes.
+
+• 0.45 μm polyethersulfone (PES) filter system, Nalgene, 565-0010 (or for larger
+  volumes 1000 mL 0.45 µm Rapid-Flow PES Filtration Unit, Nalgene 167-0045)
+
+  💡 PRO TIP: Do not use filters made of materials other than PES. AAV particles
+  stick to many other surfaces, but do not stick to PES. Using a PES filter will
+  maximize titer.
+
+• DMEM high glucose, Corning 10-013-CV
+• DMEM, low glucose (1 g/L) glucose, sodium pyruvate, Corning 10-014-CV (optional)
+• 7.5% sodium bicarbonate, Corning 25-035-CI (optional)
+• 1 M HEPES, HyClone SH30237.01 (optional)
+• L-alanyl-L-glutamine (or alternative stable glutamine such as glutaGRO,
+  Corning 25-015-CI)
+• Opti-MEM, Thermo Fisher 31985-070
+• 0.05% Trypsin/EDTA (e.g. TrypLE, Thermo Fisher, 12605010)
+• 1X PBS pH 7.4 without calcium or magnesium, Corning 21-040-CV (cations can
+  affect the attachment of adherent cells)
+• 1 mg/mL Polyethylenimine (PEI) 25 kDa MW
+
+  💡 PRO TIP: Other transfection reagents may be used in this protocol, but their
+  conditions must be optimized.
+
+• Plasmids for transfection: pHelper; pRC (Rep-Cap), plasmid expressing your gene
+  of interest
+• Triton X-100
+• Benzonase/DNAse I (Millipore 71205-3)
+• 40% Polyethylene Glycol 8000 (PEG) + 0.5 M NaCl
+• Cell lysis buffer (50 mM Tris HCl, 150 mM NaCl, 2 mM MgCl₂)
+
+
+REAGENT PREPARATION
+================================================================================
+
+DMEM Complete: 10% v/v FBS and 4 mM L-alanyl-L-glutamine (or stable alternative,
+such as glutaGRO)
+
+To a 500 mL bottle of DMEM high glucose, add 55 mL of heat-inactivated FBS and 5
+mL of glutaGRO 11 mL of 200 mM L-alanyl-L-glutamine. Store at 4 °C.
+
+D1 + 0.1 M sorbitol (optional): DMEM low glucose + 1% v/v HI-FBS + 0.1 M sorbitol
+
+In our hands, we have found that a lower glucose, lower FBS media causes less
+filter clogging during harvesting. The addition of sorbitol has been shown to
+increase viral titers by up to 1.8-fold (Rego et al., 2018).
+
+To prepare D1 + 0.1 M sorbitol, add 57 mL of 1.46 M sorbitol, 7.5 mL HI-FBS, and
+7.5 mL 100X glutaGRO, 7.5 mL of 7.5% Sodium Bicarbonate, 7.5 mL 1 M HEPES to 750
+mL DMEM + 1 g/L glucose.
+
+1 mg/mL polyethylenimine (PEI) solution:
+
+1. Dissolve 100 mg of PEI powder into 100 mL of deionized water.
+
+2. While stirring, slowly add hydrochloric acid until the solution clears.
+
+3. Check the pH of the solution and use hydrochloric acid or sodium hydroxide to
+   adjust the pH to 7.0.
+
+   💡 PRO TIP: The pH of this solution will drift pretty rapidly upon addition
+   of acid or base. Add only a few drops at a time. Allow them to mix and recheck
+   the pH to prevent over or undershooting the desired pH.
+
+4. Allow the solution to mix for 10 min and then recheck the pH to ensure that it
+   has not drifted.
+
+5. Filter the solution through a 0.22 μm membrane.
+
+6. Aliquot 500–1000 μL into sterile tubes.
+
+7. Store the tubes at -80 °C.
+
+8. After thawing, the solution can be stored at 4 °C for up to 2 months. After 2
+   months, discard the tube and thaw a new working stock.
+
+40% POLYETHYLENE GLYCOL (PEG) 8000 solution:
+
+1. Dissolve 400 g of PEG 8000 and 24 g of NaCl into deionized water and adjust to
+   a final volume of 1000 mL.
+
+2. Stir at RT until fully dissolved.
+
+   💡 PRO TIP: This step is challenging due to the high viscosity of PEG.
+   Stirring under medium heat will promote faster dissolution.
+
+3. Adjust the pH to ~7.4.
+
+4. Autoclave or sterile filter.
+
+   💡 PRO TIP: Stirring during the cooling period is recommended or the solution
+   may separate into phases.
+
+5. Aliquot and store at 4 °C.
+
+Cell Lysis Buffer: 50 mM Tris HCl, 150 mM NaCl, 2 mM MgCl₂
+
+1. Add the following to the 2 L sterile bottle: 1836 mL deionized water + 100 mL
+   of 1 M Tris HCl pH 8.5 + 60 mL of 5 M Sodium Chloride + 4 mL of 1 M Magnesium
+   Chloride
+
+2. Close the bottle and mix by inverting multiple times
+
+3. Adjust pH to ~8.5
+
+4. Filter sterilize through a 0.22 μm membrane.
+
+5. Store at 4 °C.
+
+
+CONSIDERATIONS BEFORE YOU START
+================================================================================
+
+• The health of the HEK293T cells is critical for optimal AAV yield.
+
+• Do not overgrow your cells. Pass the cells twice a week during the maintenance
+  phase and do not allow cells to reach 100% confluence (80–90% is ideal).
+
+• Pass and plate the cells the day before the transfection.
+
+• Thaw a new vial of cells after 30 passages.
+
+
+PROCEDURE
+================================================================================
+
+1. Trypsinize and resuspend the HEK293T cells from 2 x T-175 flasks. Cells should
+   be at ~80% confluence. For each T-175 flask:
+   • Aspirate culture media and rinse once with 10 mL of PBS.
+   • Aspirate PBS and add 2 mL of 0.05% Trypsin/EDTA. Wait ~2 min.
+   • Neutralize trypsin by adding 10 mL of DMEM Complete.
+   • Pipet back and forth vigorously multiple times to obtain a single cell
+     suspension (no clumps of cells).
+
+2. Pool cells from 2 x T-175 flasks. Adjust volume to 300 mL with DMEM complete
+   media and mix.
+
+3. Seed all cells in 1 CS2. Return to incubator for 48 h.
+
+4. Trypsinize and resuspend cells from the CS2. Cells should be at ~80%
+   confluence.
+   • Aspirate culture media and rinse once with 60 mL PBS.
+   • Aspirate PBS and add 35 mL of 0.05% Trypsin/EDTA.
+   • Wait ~2-3 minutes for cells to detach.
+   • Gently tap the sides of the CS2 to help detach the cells, add 200 mL of DMEM
+     complete media, then transfer the cells into a sterile bottle.
+   • Rinse the CS2 with 100 mL of DMEM complete medium and pool with the cells
+     harvested in the previous step.
+   • Pipette back and forth or shake vigorously to obtain a single cell suspension
+     (no clumps).
+
+5. Count cells using a hemocytometer or cell counter.
+
+6. Seed 350 million cells from the CS2 into one CS5 with a total volume of 850 mL.
+   Return to the incubator for 24 h–36 h.
+
+7. Proceed with transfection:
+   • Calculate the amount of each plasmid needed to have a 1:1:1 molar ratio with 2
+     mg total DNA per CS5
+
+     Example calculations:
+     - RepCap (7,265 bp): 582.1 μg = 582.1 μL (at 1.00 μg/μL)
+     - pHelper (11,854 bp): 949.8 μg = 949.8 μL (at 1.00 μg/μL)
+     - Transfer Plasmid (5,842 bp): 468.1 μg = 468.1 μL (at 1.00 μg/μL)
+
+   • Aliquot 100 mL of OptiMEM into a sterile 250 mL bottle.
+
+   • Aliquot 774 mL of DMEM + 2% HI-FBS into a sterile 1 L bottle.
+
+   • Add each plasmid DNA into the bottle containing the Opti-MEM. Mix well.
+
+   • Add 4 mL of PEI (1:2 μg DNA to μg PEI ratio). Shake the bottle up/down
+     vigorously for 30 sec (it's okay to make bubbles).
+
+   • Incubate at RT for 15 min. Note that longer incubation times can reduce
+     transfection efficiency.
+
+   • Add the OptiMEM + DNA + PEI solution to the bottle containing 774 mL of DMEM
+     + 2% HI-FBS. Mix well.
+
+8. Take the CS5 out of the incubator and pour the media into a waste container.
+
+9. Carefully add the OptiMEM + DNA + PEI solution to the CS5. Make sure that all
+   five layers are covered with media. 293T cells are delicate and detach very
+   easily - media should always be added away from the cells (not poured on them)
+   and can be adjusted by carefully tilting the CS5 back and forth.
+
+   💡 PRO TIP: To help distribute the media amongst the five layers, tilt the CS5
+   such that the media goes toward the cap. If the media touches the cap, replace
+   it with a new one before putting the CS5 in the incubator.
+
+10. Return the CS5 to the incubator for 96 h. This incubation time can be adjusted
+    depending upon the serotype. Often AAV2 are harvested at 48–72 h, while other
+    serotypes are harvested at 96–120 h.
+
+11. Optional: perform a media exchange at ~18 h post transfection. Changing the
+    media can reduce toxicity associated with PEI and allow for the addition of
+    other components, such as sorbitol and sodium bicarbonate, sorbitol has been
+    found to increase viral titers by up to 1.8-fold. See the recipe for D1 + 0.1 M
+    sorbitol above.
+    • Carefully pour off the media into a waste container.
+    • Gently pour new media into the CS5.
+    • Distribute the media evenly between the layers.
+    • Return the CS5 to the incubator for ~72 h.
+
+12. Harvest cells and media by tapping the sides of the CS5. Cells should detach
+    easily.
+
+13. Transfer cells and media into 500 mL conical vessels.
+
+14. Rinse CS5 once with 100 mL of PBS and add to the harvested media and cells.
+
+15. Centrifuge at 3900 rpm for 20 min at 4 °C to pellet the cells.
+
+16. Keep the cell pellet on ice and transfer supernatant to a sterile 500 mL
+    bottle.
+
+17. Process the supernatant as follows:
+    • Filter through a 0.45 μm PES membrane.
+    • Add 40% PEG such that the final PEG concentration is 8%.
+    • Add 25 mL of PEG solution to each 100 mL of supernatant. Split into 2 x 500
+      mL sterile bottles as needed.
+    • Add stir bar and stir slowly at 4 °C for 1 h, then keep at 4 °C for 3 h
+      without stirring to allow full precipitation. Precipitation of the viruses
+      can proceed overnight at 4 °C if needed.
+    • Transfer the entire sample to 3 x 500 mL conical bottles and centrifuge at
+      3900 rpm for 15 min at 4 °C.
+    • Discard the supernatant and resuspend the pellets in a total of 5 mL of cell
+      lysis buffer (recipe above). Pipet back and forth to resuspend each pellet
+      completely.
+    • Combine resuspended pellets and keep on ice.
+
+18. Process the cell pellet from above as follows:
+    • Resuspend and lyse the cells by adding a total of 12.5 mL of cell lysis
+      buffer. Pipet back and forth to resuspend each pellet completely.
+    • Combine all resuspended cell pellets and sonicate 5 x 1 sec pulses with at
+      least 5 minutes on ice between each pulse, 50% amplitude. Return to ice
+      between each round of sonication to avoid overheating of the sample. Mix
+      well between rounds of sonication.
+    • Sonicate until no live cells can be seen when stained with Trypan Blue.
+    • Pellet cell debris by centrifugation at 3900 rpm for 10 min at 4 °C.
+    • Transfer the cleared lysate to the tube containing the resuspended virus from
+      step 17 above.
+
+19. Add 50 units of benzonase per mL of viral suspension (you should have ~18 mL
+    from steps 17 & 18). Benzonase is an endonuclease that will degrade any
+    residual DNA carried over from the packaging process.
+
+20. Incubate at 37 °C for 45 min.
+
+21. You are now ready to purify your prep. The clarified supernatant can be kept
+    overnight at 4 °C before proceeding with the purification protocol.
+
+
+SAMPLE DATA
+================================================================================
+
+HEK293T cells at various confluencies. This AAV production protocol should be
+started with cells that are ~80% confluent (center panel). These cells were
+imaged with a 10X objective and were split 2 days before these images were taken.
+
+Last reviewed on: October 10, 2023

BioScientist/agent_system/engines/v1_executor_backup/tool/protocols/addgene/Addgene_ AAV Purification.txt

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+PROTOCOL: AAV Purification
+SOURCE: https://www.addgene.org/protocols/aav-purification-iodixanol-gradient-ultracentrifugation/
+================================================================================
+
+INTRODUCTION
+================================================================================
+
+This protocol can be used to purify AAV of any serotype.
+
+In medicine, iodixanol can be used as an intravenous isomolar contrast agent. In
+research, iodixanol is used as an isomolar density gradient medium suitable for
+virus purification and isolation of cells, organelles, lipoproteins, and
+macromolecules. Importantly, iodixanol is inert and non-toxic to mammalian cells.
+Therefore, and unlike other density gradient media, it is reportedly not
+necessary to remove iodixanol prior to use of your purified virus. Nonetheless,
+we recommend performing a buffer exchange before using the purified AAV in vivo.
+
+The iodixanol gradient in this protocol is composed of steps that separate out
+contaminants from an impure AAV preparation. The 15% iodixanol step has 1M NaCl to
+destabilize ionic interactions between macromolecules. The 40% and 25% steps are
+used to remove contaminants with lower densities, including empty capsids. The 60%
+step serves as a cushion for genome-containing virions. Phenol red is added to
+clearly distinguish the steps. This method will help enrich the prep with full
+(genome containing) particles. On average, iodixanol-purified AAV preparations
+contain ~20% empty particles.
+
+60% iodixanol solution is available under the name OptiPrep from Sigma-Aldrich.
+
+
+WORKFLOW TIMELINE
+================================================================================
+
+Day 1: Purify
+
+Day 2: Buffer exchange and concentration
+
+Note: Both steps could be completed in one (long) day.
+
+
+EQUIPMENT
+================================================================================
+
+• Ultracentrifuge
+• T70i rotor or equivalent
+
+
+REAGENTS
+================================================================================
+
+• Opti-Prep (60% Iodixanol)
+• QuickSeal tubes
+• QuickSeal tube spacers
+• 16 ga needle
+• 18 ga needle
+• 10 mL syringe
+• 18 ga blunt edge needles, Hamilton
+• 1X PBS pH 7.4
+• 1X PBS-MK buffer
+• 100X Poloxamer 188
+• NaCl
+• MgCl₂
+• KCl
+• Centrifugal filter units (MWCO 100 kDa)
+
+
+REAGENT PREPARATION
+================================================================================
+
+1 M NaCl/PBS-MK buffer:
+
+Dissolve 5.84 g of NaCl, 26.3 mg of MgCl₂ and 14.91 mg of KCl in 1× PBS in a
+final volume of 100 mL. Sterilize by passing through a 0.22 μm filter and store
+at 4 °C.
+
+1X PBS-MK buffer:
+
+Dissolve 26.3 mg of MgCl₂, and 14.91 mg of KCl in 1× PBS in a final volume of
+100 mL. Sterilize by passing through a 0.22 μm filter and store at 4 °C.
+
+0.1% Poloxamer 188 in PBS (A):
+
+Add 500 µL of 10% Poloxamer 188 to 49.5 mL PBS
+
+0.01% Poloxamer 188 in PBS (B):
+
+Add 5 mL of Buffer A to 45 mL PBS
+
+0.001% Poloxamer 188 in PBS + 200 mM NaCl (C) (formulation buffer):
+
+Add 5 mL of Buffer B and 2 mL of 5 M NaCl to 43 mL PBS
+
+
+PROCEDURE
+================================================================================
+
+Preparation and loading of the iodixanol gradient:
+
+1. Prepare the gradient steps:
+   • 15% iodixanol step: mix 4.5 mL of 60% iodixanol and 13.5 mL of 1 M
+     NaCl/PBS-MK buffer
+   • 25% iodixanol step: mix 5 mL of 60% iodixanol and 7 mL of 1X PBS-MK buffer
+     and 30 μL of phenol red
+   • 40% iodixanol step: mix 6.7 mL of 60% iodixanol and 3.3 mL of 1X PBS-MK
+     buffer
+   • 60% iodixanol step: mix 10 mL of 60% iodixanol and 45 μL of phenol red
+
+2. Underlay each solution into a QuickSeal tube in the order below using a 10 mL
+   syringe and a blunt edge 18 ga Hamilton needle, taking care to avoid bubbles:
+   • 8 mL of 15% iodixanol step
+   • 5 mL of 25% iodixanol step
+   • 5 mL of 40% iodixanol step
+   • 3 mL of 60% iodixanol step
+
+3. Carefully add up to 5 mL of clarified supernatant on top of the gradient. Use
+   1X PBS (or formulation buffer) to top off the tube.
+
+4. Seal the QuickSeal tubes.
+
+5. Centrifuge at 350,000 x g for 90 min in a T70i rotor at 10 °C.
+
+   💡 PRO TIP: If you need more time, you can alternatively centrifuge for 2 h at
+   200,000 x g at 18 °C.
+
+6. Carefully take the QuickSeal tubes out of the rotor and place them in a stable
+   rack.
+
+   **Make sure not to disturb the gradient!**
+
+Collect Fractions
+
+Option #1:
+
+1. Prepare a row of roughly 20 open 1.5 mL microcentrifuge tubes in a rack.
+   These will be used to collect fractions from the gradient. You can collect fewer
+   fractions once you have a good idea when the virus elutes from the gradients.
+
+2. In a biosafety cabinet, carefully puncture the QuickSeal tube at the interface
+   of the 60% and 40% gradient (see Figure 2) with an 18 ga needle.
+
+3. Place the first microcentrifuge tube under the needle's opening to collect the
+   fractions.
+
+4. Puncture the top of the QuickSeal tube with a 16 ga needle and start
+   collecting 0.5 mL to 1 mL fractions per tube.
+
+5. Avoid the proteinaceous material near the 40–25% interface.
+
+   💡 PRO TIP: As soon as the top of the tube is punctured, AAV-containing
+   Iodixanol solution will flow from the needle set at the 40–60% interface.
+   Make sure that the microcentrifuge tubes are well positioned to collect the
+   solution, or you will lose a significant amount of your virus. When the first
+   fraction is collected, move the rack to the next empty tube to collect the next
+   fraction. The flow will be rapid at first and will progressively slow down.
+
+Option #2:
+
+1. Puncture the QuickSeal tube at the bottom using an 18 ga needle.
+
+2. Immediately start collecting 0.5 to 1 mL fractions in microcentrifuge tubes.
+
+3. Repeat for each QuickSeal tube.
+
+4. The first 3 mL collected corresponds to the 60% phase and can be discarded.
+
+5. The fractions obtained from the 40% phase contain the purified AAV.
+
+Option #3:
+
+1. Puncture the QuickSeal tube slightly below the 60–40% interface with an 18 ga
+   needle attached to a 10 mL syringe.
+
+2. The bevel of the needle should be up, facing the 40% step.
+
+3. Collect up to 5 mL per tube, taking care to avoid collecting the
+   proteinaceous material at the 40–25% interface.
+
+4. Repeat for each QuickSeal tube.
+
+5. (Optional) For first time users, it is a good idea to assay each fraction by
+   silver stain or SYPRO Ruby stain to determine purity. Only the cleanest
+   fractions should be kept and pooled for dialysis.
+
+6. Pool clean fractions.
+
+Concentration and buffer exchange:
+
+Stocks of Poloxamer 188:
+• Poloxamer 188, 10% solution
+• A: 0.1% in PBS: 49.5 mL PBS + 500 µl Poloxamer 188
+• B: 0.01% in PBS: 45 mL PBS + 5 ml A
+• C: 0.001% in PBS: 45 mL PBS + 5 ml B + 200 mM NaCl
+
+Procedure:
+
+1. Cover the filter membrane with 15 mL of 0.1% Poloxamer 188 PBS and incubate
+   for 10 min at RT
+
+2. Spin at 3000 rpm for 5 min at 4 °C and discard the flow through.
+
+3. Add 15 mL of 0.01% Poloxamer 188 PBS and incubate for 10 min at RT.
+
+4. Spin at 3000 rpm for 5 min at 4 °C and discard the flow through.
+
+5. Add 15 mL of 0.001% Poloxamer 188 + 200mM NaCl PBS.
+
+6. Spin at 3000 rpm for 5 min at 4 °C and discard the flow through.
+
+7. Add your sample.
+
+8. Spin at 3500 rpm for 5–8 min at 4 °C, discard the flow through.
+
+9. Add more sample and spin 3500 rpm for 2–5 min at 4 °C, discard the flow
+   through.
+
+10. Repeat this step as needed.
+
+    Please note that iodixanol is not easily removed. After each spin, add more
+    formulation buffer and sample and make sure to pipet back and forth a few
+    times to mix the iodixanol that has settled at the bottom of the column.
+
+11. We recommend concentrating to a minimum of 500 µL. If the concentrate volume
+    is less than 500 µL, bring up the volume with formulation buffer.
+
+12. Use a P1000 to the bottom of the filter and pipette up/down and wash off the
+    walls of the filter to recover as much virus as possible.
+
+13. Store at 4 °C for short term (2 weeks), or aliquot and store at -80 °C for
+    long term.
+
+
+SAMPLE DATA
+================================================================================
+
+Iodixanol gradient before ultracentrifugation. The steps are clearly
+distinguishable thanks to the use of phenol red.
+
+Iodixanol gradient after ultracentrifugation. The arrow indicates the 60–40%
+interface. The vertical black line indicates the location of the purified AAV.
+
+Example of an SDS-PAGE gel of 10 consecutive gradient fractions followed by
+silver stain. Note the increased number of contaminants in each fraction. * AAV
+capsid proteins VP1, VP2, VP3; M protein marker.
+
+Last reviewed on: November 7, 2023

BioScientist/agent_system/engines/v1_executor_backup/tool/protocols/addgene/Addgene_ AAV Titration by qPCR.txt

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+PROTOCOL: AAV Titration by qPCR
+SOURCE: https://www.addgene.org/protocols/aav-titration-qpcr-using-sybr-green-technology/
+================================================================================
+
+INTRODUCTION
+================================================================================
+
+The AAV Titration protocol can be used to determine the number of
+genome-containing particles of an AAV prep using SYBR green technology.
+
+This protocol was validated using an internal reference AAV of known titer,
+100837-AAV1, and by measuring the titer of samples obtained from academic viral
+vector cores. Our titers were similar to those reported by these institutions.
+
+This protocol is for a 96-well plate with 20 μL reaction volume.
+
+Last Update: February 13, 2019
+
+Estimate of time required: ~3 h
+
+
+WORKFLOW TIMELINE
+================================================================================
+
+Plate set-up: 2 h
+
+qPCR run: 1.5 h
+
+Data analysis: 30 min
+
+
+EQUIPMENT
+================================================================================
+
+• qPCR instrument
+• Heating plate
+• Pipettors
+• 1–10 µL single channel pipette
+• 20–200 µL single channel pipette
+• 200–1000 µL single channel pipette
+• 1–10 µL multichannel pipette
+• 2–50 µL multichannel pipette
+• 20–200 µL multichannel pipette
+
+
+REAGENTS
+================================================================================
+
+• Universal SYBR Master Mix 2X
+• Primer pair targeting AAV2 ITR (Aurnhammer et al., 2012)
+  - fwd ITR primer, 5'-GGAACCCCTAGTGATGGAGTT
+  - rev ITR primer, 5'-CGGCCTCAGTGAGCGA
+• ITR-containing plasmid for standard curve
+• RNase-free DNase
+• 10X DNase buffer
+• Nuclease-free water
+• Microcentrifuge tubes
+• 96-well optical plate
+• Pipette tips
+
+
+GENERAL CONSIDERATIONS
+================================================================================
+
+• Always run standards and samples in duplicate at least
+
+• If possible, include an AAV reference sample of known titer. The reference
+  material should have a titer within 1-log of the expected titer of the samples
+  being tested.
+
+• Always include a No Template Control (NTC), i.e master mix + water
+
+• Whenever possible use a multichannel pipet to minimize pipetting error and
+  variability
+
+• Mix samples very well by pipetting back and forth multiple times at each step
+
+
+REAGENT PREPARATION
+================================================================================
+
+Master Mix:
+
+Count the number of samples (n) and prepare master mix for an additional 10
+samples (n+10 – the additional amount will ensure that there is enough master mix
+for all samples). Each sample requires 15 μL of master mix.
+
+💡 PRO TIPS:
+
+• Use a "Universal" SYBR master mix which contains a high-quality DNA polymerase
+  and a blend of dTTP/dUTP to minimize carryover contamination. The master mix
+  should also contain an internal passive reference (typically ROX dye), to
+  normalize non-PCR–related fluorescence fluctuations and to minimize well-to-well
+  variability that result from a variety of causes, such as pipetting error and
+  sample evaporation.
+
+• Make the master mix after all the samples have been added to the qPCR plate.
+  Start by adding water, then SYBR master mix, then the forward and reverse
+  primers. Vortex briefly, immediately before use.
+
+• Use a reservoir and a multichannel pipette to dispense the master mix into the
+  wells.
+
+Master Mix Recipe:
+
+Reagent                    | Amount for ONE Reaction | Amount for 100 reactions
+---------------------------|------------------------|--------------------------
+Universal SYBR Master Mix  | 10 μL                  | 1,000 μL
+2X                         |                        |
+100 μM Forward Primer      | 0.15 μL                | 15 μL
+100 μM Reverse Primer      | 0.15 μL                | 15 μL
+Nuclease Free Water        | 4.7 μL                 | 470 μL
+
+
+PROCEDURE
+================================================================================
+
+1. Prepare a plasmid stock of 2 x 10⁹ molecules/μL to generate a standard curve:
+
+   One option is to use plasmid #59462 from Addgene. The values highlighted below
+   in red were calculated using this plasmid, but will change if you use a
+   different plasmid.
+
+   Sample Calculation:
+   • Size in bp of Addgene plasmid #59462: 6208 bp
+   • Concentration of Addgene plasmid #59462: 1.07 μg/μL
+   • Molecular Weight: 6208 bp × 650 dalton/bp = 4.03 × 10⁶ g/mole
+   • Moles/μl: 1.07 μg/μL × 1 g/10⁶ μg × 1 / 4.03 × 10⁶ g/mole = 2.65 × 10⁻¹³
+     moles/μL
+   • Molecules/μl: 2.65 × 10⁻¹³ moles/μL × 6.022145 × 10²³ molecules/mole = 1.59
+     × 10¹¹ molecules/μL
+   • To obtain a solution at 2 × 10⁹ molecules/μL: 1.59 × 10¹¹ / 2 × 10⁹ = 79.8X
+     dilution
+   • Therefore we need to dilute 1.25 μL stock into 98.74 μL H₂O
+
+   💡 PRO TIP: Once a validated standard curve is obtained, make a small aliquot
+   of each standard (enough for 1 or 2 plates) and store at -20 °C. Once a
+   standard is thawed do not freeze it again but store at 4 °C and use within 1
+   week.
+
+   Keep track of the Ct value for each standard over time. They should remain
+   within 0.5 Ct of their initial value. If the Ct value of the standard starts
+   to drift, it's time to make a new one.
+
+   When developing the assay multiple plasmids containing ITR were tested.
+   Plasmid #59462 is one plasmid that gave reliable and consistent results. Use
+   the recommended plasmid, or test multiple plasmids to find a suitable one.
+
+   Some labs have reported better results when the plasmid is linearized.
+
+2. Treat the purified AAV samples with DNase I to eliminate any contaminating
+   plasmid DNA carried over from the production process (DNase does not penetrate
+   the virion).
+
+   • 5 μL sample + 39 μL H₂O + 5 μL 10X DNase buffer + 1 μL DNase
+   • Gently mix sample (do not vortex)
+   • Incubate 30 min at 37 °C
+   • Transfer to ice
+
+   ** Critical: do NOT treat your plasmid standard with DNase **
+
+3. Make 6 serial dilutions, in duplicate, of your standard curve plasmid (2 ×
+   10⁹ stock made in step #1):
+
+   Volume of 2 × 10⁹ stock | Volume of nuclease-free | Molecules per μL
+   or previous dilution   | water (uL)              |
+   (μL)                    |                         |
+   ------------------------|-------------------------|------------------
+   10                      | 90                      | 2 × 10⁸
+   10 of 2 × 10⁸ dilution  | 90                      | 2 × 10⁷
+   10 of 2 × 10⁷ dilution  | 90                      | 2 × 10⁶
+   10 of 2 × 10⁶ dilution  | 90                      | 2 × 10⁵
+   10 of 2 × 10⁵ dilution  | 90                      | 2 × 10⁴
+   10 of 2 × 10⁴ dilution  | 90                      | 2 × 10³
+
+   💡 PRO TIP: To help stabilize the standards add carrier DNA to a final
+   concentration of 4 ug/mL to each standard dilution.
+
+4. Dilute DNase-treated and AAV reference samples according to the dilution
+   scheme in the table below:
+
+   Dilution Series | Volume of sample | Volume of nuclease | Dilution | Total
+                   | (uL)             | free water (uL)    | factor   | dilution
+   ----------------|------------------|--------------------|----------|----------
+   Dilution 1      | 5 uL AAV stock   | 45 uL              | 10X      | 10X
+   (DNase step)    |                  |                    |          |
+   Dilution 2      | 5 uL Dil. 1     | 95 uL              | 20X      | 200X
+   Dilution 3      | 20 uL Dil. 2    | 80 uL              | 5X       | 1000X
+   Dilution 4      | 20 uL Dil. 3    | 80 uL              | 5X       | 5000X
+   Dilution 5      | 20 uL Dil. 4    | 80 uL              | 5X       | 25000X
+   Dilution 6      | 20 uL Dil. 5    | 80 uL              | 5X       | 125000X
+   Dilution 7      | 20 uL Dil. 6    | 80 uL              | 5X       | 625000X
+   Dilution 8      | 20 uL Dil. 7    | 80 uL              | 5X       | 3125000X
+
+   Dilutions highlighted in green are the ones loaded onto the qPCR plate for
+   most samples.
+
+   • If sample is expected to have a titer <1 × 10¹² GC/mL, use dilutions 3–6
+   • If sample is expected to have a titer >3 × 10¹³ GC/mL, use dilutions 5–8
+
+   Note: at Addgene, samples typically range from 1 × 10¹² GC/mL to >2 × 10¹³
+   GC/mL and we use an internal reference virus that is 1 × 10¹³ GC/mL. We
+   recommend always using a reference within 1-log of the expected titer.
+
+   💡 PRO TIPS:
+   • The quality of the sample dilution series is critical. Make sure to pipet
+     each dilution up and down at least 10 times, and use at least half of the
+     final volume (mix with >50 uL if your well contains 100 uL)
+   • Use a multichannel pipette to load the standards and samples onto the qPCR
+     plate
+
+5. Set up and load the 96-well plate:
+   • Load 5 μL of each standard in duplicate
+   • Load 5 μL of each sample in duplicate. Do not forget to include a no
+     template control (NTC = master mix + water).
+   • Add 15 μL of Master Mix per well and mix well by pipetting back and forth
+     at least 5 times.
+   • Seal plate with transparent film.
+   • Centrifuge at 3,000 rpm for 2 min to bring the sample to the bottom of the
+     tube.
+
+6. Run the following protocol in your qPCR instrument using SYBR detection:
+
+   98 °C 3 min / 98 °C 15 sec / 58 °C 30 sec / read plate/ repeat 39x from step
+   3 / melt curve
+
+7. Perform data analysis using the instrument's software. Determine the physical
+   titer of samples (viral genomes (vg)/mL) based on the standard curve and the
+   sample dilutions.
+
+   💡 PRO TIP: Make sure that the qPCR is valid by checking to the following:
+   • Standard curve: R² (coefficient of correlation) ~ 1.0, E (efficiency of PCR)
+     ~100% (90%–110% range is acceptable)
+   • Baseline removal: all samples will have some small amount of background
+     signal that is most evident during initial PCR cycles. This background
+     signal must be removed to accurately determine differences between samples.
+   • Melt curve analysis: a single peak should be seen. The presence of a second
+     peak at a temperature of ~70–75 °C usually indicates the presence of primer
+     dimers which can increase background signal and alter the Ct values of your
+     samples.
+   • Quality of your standard curve: you should observe differences in Ct values
+     that make sense for your dilutions (~3.3 difference Ct for a 10-fold dilution
+     is appropriate).
+   • Quality of duplicates: Exclude duplicates from analyses if there is more than
+     a 0.5 Ct difference between them.
+
+
+SAMPLE DATA
+================================================================================
+
+Example of a valid 8-point standard curve.
+
+Example of the amplification plots obtained from an AAV sample. Each curve
+represents a dilution.
+
+
+REFERENCES
+================================================================================
+
+Aurnhammer C, Haase M, Muether N, Hausl M, Rauschhuber C, Huber I, Nitschko H,
+Busch U, Sing A, Ehrhardt A, Baiker A. Universal real-time PCR for the detection
+and quantification of adeno-associated virus serotype 2-derived inverted
+terminal repeat sequences. Hum Gene Ther Methods. 2012 Feb;23(1):18-28. PMID:
+22428977

BioScientist/agent_system/engines/v1_executor_backup/tool/protocols/addgene/Addgene_ AAV ddPCR Titration.txt

@@ -0,0 +1,318 @@
+PROTOCOL: AAV ddPCR Titration
+SOURCE: https://www.addgene.org/protocols/aav-ddpcr-titration/
+================================================================================
+
+INTRODUCTION
+================================================================================
+
+This protocol describes how to use droplet digital PCR (ddPCR) to titer purified
+recombinant Adeno-associated viral vectors (AAV). This protocol specifically uses
+primers and probes targeting the ITR elements in the viral vectors but can be
+modified for other targets. The dilution series outlined in this protocol are
+based on an AAV titer range of 5E+12–5E+13 GC/mL, where GC is genome copies and
+generally represents the physical number of viral particles. Users may need to
+run lower or higher dilutions depending on their particular sample. For
+additional tips on AAV titering using ddPCR, read this blog post on ddPCR for
+AAV quantification.
+
+This protocol was modified from the publication Lock et al. (2014).
+
+
+BEFORE STARTING
+================================================================================
+
+• To reduce the risk of contaminating reagents we recommend making small aliquots
+  of master mixes, primers, and probes prior to use.
+
+• Thaw the master mix, primers, and probe on ice before use.
+
+• Wipe down all pipettes and surfaces with 10% bleach.
+
+
+SAFETY WARNINGS
+================================================================================
+
+AAV is generally considered biosafety level 1 but may require BSL-2 handling
+depending on the insert. Please ensure that you are in compliance with your
+institution's biosafety regulations.
+
+
+EQUIPMENT
+================================================================================
+
+• Class II, Type A2 Biological Safety Cabinet, Labconco 302411100
+• Droplet Digital PCR System, Bio-Rad, DX200
+• Thermal Cycler, Bio-Rad, T100
+• PCR Plate Sealer, Bio-Rad, PX1
+• 1–10 µL single channel pipette
+• 20–200 µL single channel pipette
+• 200–1000 µL single channel pipette
+• 1–10 µL multichannel pipette
+• 2–50 µL multichannel pipette
+• 20–200 µL multichannel pipette
+• Vortex, VWR, 10153-688
+• Mini Centrifuge, Thermo Scientific, 10199-452
+• Ice bucket
+• 96-well freezer blocks (x 3)
+
+
+REAGENTS
+================================================================================
+
+• Molecular Biology Grade Water, Hyclone, SH30538.02
+• GeneAmp 10X PCR Buffer, Applied Biosystems, N8080129
+• ddPCR Supermix for Probes no dUTP, Bio-Rad,1863023
+• 10% Poloxamer 188, Thermo Fisher, 24040032
+• Droplet generation oil, Bio-Rad, 1863005
+• DG8 cartridge, Bio-Rad, 1864008
+• DG8 gasket, Bio-Rad, 1863009
+• DG8 cartridge holder, Bio-Rad, 1863051
+• 8-strip PCR tubes, Axygen, PCR-02-FCP-C
+• ddPCR 96-well PCR plates, Bio-Rad, 12001925
+• 48-well dilution plate, Bio-Rad, MLL4801
+• Pierceable Foil Heat Seal, Bio-Rad, 1814040
+• Microseal adhesive seal, Bio-Rad, MSB1001
+• Polystyrene Reservoirs, VWR, 89094-662
+• Microcentrifuge tubes, VWR, 87003-294
+
+Primers/probe targeting ITR:
+• ITR Forward Primer: 5'-CGGCCTCAGTGAGCGA
+• ITR Reverse Primer: 5'-GGAACCCCTAGTGATGGAGTT
+• ITR Probe: -FAM-CACTCCCTCTCTGCGCGCTCG-BBQ-
+
+
+REAGENT PREPARATION
+================================================================================
+
+1X PCR Buffer containing 0.05% Poloxamer 188:
+
+Prepare immediately before use and vortex before using.
+
+• 500 µL of GeneAmp 10X Buffer
+• 25 µL of 10% Poloxamer 188
+• 4475 µL Molecular Biology Grade Water
+
+
+PROCEDURE
+================================================================================
+
+Preparation
+
+1. Before handling any viruses, get materials ready.
+
+2. Ensure that primers, probe, 10X PCR buffer, and master mix are thawed.
+
+3. Vortex primers, probe and master mix for 15 sec then spin 10 sec in a mini
+   centrifuge and place on ice.
+
+4. Wipe down a DG8 cartridge holder with bleach and place in the Biological
+   Safety Cabinet (BSC).
+
+5. Make sure that the BSC to be used for dilution and the BSC to be used for
+   droplet generation are supplied with sufficient pipette tips and reagent
+   reservoirs.
+
+   💡 PRO TIP: Prepare the viral dilutions in a separate biological safety
+   cabinet (dilution BSC) than that used for master mix preparation and droplet
+   generation (droplet generation BSC) to ensure no contamination of the PCR
+   reagents and the NTC with virus.
+
+6. Pre-warm the 96-well plate sealer by gently touching the screen.
+
+Prepare the Serial Dilution
+
+1. Place a 48-well dilution plate in a chilled 96-well freezer block and place in
+   the dilution BSC.
+
+2. Prepare 1X dilution buffer (see recipe above).
+
+3. Save ~25 µL of 1X dilution buffer in a microcentrifuge tube to be used as
+   the NTC.
+
+4. Pour the 1X dilution buffer into a polystyrene reagent reservoir.
+
+5. Using a 20–200 µL multichannel pipette, carefully add the dilution buffer to
+   the dilution plate according to the dilution scheme listed in step 13.
+
+6. Use a single channel 1–10 µL pipette to add 5 µL of each viral sample to
+   Dilution 1 in the 48-well dilution plate and pipette 5–10 times to mix.
+
+7. Dilute the virus as follows: Mix dilutions thoroughly, but pipette slowly to
+   avoid generating aerosols. Be sure to change tips each time.
+
+   • Dilution 1 (20X): 5 µL in 95 µL 1X PCR buffer (1:20)
+   • Dilution 2 (20X): 5 µL in 95 µL 1X PCR buffer (1:400)
+   • Dilution 3 (20X): 5 µL in 95 µL 1X PCR buffer (1:8,000)
+   • Dilution 4 (20X): 5 µL in 95 µL 1X PCR buffer (1:160,000)
+   • Dilution 5 (20X): 5 µL in 95 µL 1X PCR buffer (1:3,200,000)
+   • Dilution 6 (2X): 50 µL in 50 µL 1X PCR buffer (1:6,400,000)
+   • Dilution 7 (2X): 50 µL in 50 µL 1X PCR buffer (1:12,800,000)
+   • Dilution 8 (2X): 50 µL in 50 µL 1X PCR buffer (1:25,600,000)
+
+8. Use multichannel pipettes for the dilution series.
+   • For dilutions 1–5, use the 1–10 µL multichannel pipette set to 5 µL.
+   • For dilutions 6–8, use the 20–200 µL multichannel pipette set to 50 µL.
+   • For mixing between dilutions, use the 20–200 µL multichannel pipette set to
+     90 µL and mix by pipetting the liquid up and down 10–20 times.
+
+9. Gently cover the entire dilution plate with Microseal adhesive seal - do not
+   press the film, just gently cover so nothing falls into the plate.
+
+10. Leave dilutions on the chilled 96-well freezer block in the BSC until ready
+    to use.
+
+Prepare the Master Mix
+
+1. Place a ddPCR plate onto a chilled 96-well freezer block and set aside in the
+   droplet generation BSC to cool.
+
+2. Prepare the ITR master mix in a microcentrifuge tube as shown below. For 8
+   samples prepare enough master mix for 9 samples.
+
+   ITR Master Mix:
+
+   Component                        | Volume | 9X Volume | Final Concentration
+   ---------------------------------|--------|-----------|--------------------
+   2X ddPCR Supermix for Probes,     | 10 µL  | 90 µL     | 1X
+   no dUTP                           |        |           |
+   10 µM ITR probe (FAM)             | 0.5 µL | 4.5 µL    | 250 nM
+   Forward ITR Primer (10 µM)        | 1.8 µL | 16.2 µL   | 900 nM
+   Reverse ITR Primer (10 µM)        | 1.8 µL | 16.2 µL   | 900 nM
+   Nuclease-free water               | 5.9 µL | 71.55 µL  |
+   Total Volume                      | 20 µL  |           |
+
+3. Vortex the master mix for 15 sec and spin in a mini centrifuge for 10 sec
+   before use.
+
+4. Place an 8-well PCR tube strip into a chilled 96-well freezer block.
+
+5. Add 20 µL of the master mix to each PCR tube. Be careful to dispense to the
+   bottom of the tube without collecting drops along the side of the tube.
+
+6. Cap gently - do not push the cap in all the way, just ensure the samples are
+   covered.
+
+7. Bring the PCR tubes to the BSC used for dilutions.
+
+8. Without disturbing the liquid in the tube, gently uncap the PCR tubes.
+
+9. Add 5 µL of dilutions 6–8 to the appropriate PCR tubes. Pipette back and
+   forth 5 times.
+
+10. Lightly cap the PCR tubes.
+
+Generate the Droplets
+
+1. Bring the PCR tubes to the droplet generation BSC.
+
+2. Without disturbing the liquid in the tube, gently uncap the tubes.
+
+3. Add 5 µL of the previously aliquoted 1X dilution buffer from step 9 to the
+   NTC tube.
+
+4. Place a DG8 cartridge into the cartridge holder.
+
+5. Using a 2–50 µL multichannel pipet, load 20 µL of the reaction mixtures into
+   the middle wells of the cartridge.
+
+6. Add 800 µL of droplet generation oil to a polystyrene reagent reservoir.
+
+7. Using the 20–200 µL multichannel pipet, load 70 µL of droplet generation oil
+   into the bottom row of wells.
+
+8. Cover the cartridge with the DG8 gasket, making sure that it is secure.
+
+9. Transfer the cartridge holder to the droplet generator. Close the lid and wait
+   for the droplets to be generated.
+
+10. Once the droplets have been generated, use a 20–200 µL multichannel pipet to
+    aspirate 40 µL of droplets.
+
+    💡 PRO TIP: To ensure that the droplets are not disrupted, insert the pipette
+    tips directly in the center of the well and tilt to a 45° angle. Count to 20
+    while slowly and gently aspirating the droplets.
+
+11. Transfer the droplets to a prechilled PCR plate.
+
+    💡 PRO TIP: To ensure that the droplets are not disrupted insert the pipette
+    tips and gently touch the bottom of the well. Lift the tips ~1 mm. Touch the
+    side of the well and tilt the pipette tips at a 45° angle. Count to 20 while
+    slowly and gently dispensing the droplets down the side of the tube.
+
+12. Place a Pierceable Foil Heat Seal on the PCR plate with the red line facing
+    up. If the plate sealer is not at temperature, touch the screen on the plate
+    sealer to allow it to get to temperature. Once the temperature is reached,
+    place the PCR plate with the foil onto the metal support block. Place the
+    block in the plate sealer and press the 'Seal' button.
+
+13. After the plate has been sealed, proceed to thermocycling.
+
+Thermal Cycling
+
+Run the following PCR parameters:
+
+Cycling Step          | Temperature (°C) | Time (min) | Ramp Rate (°C/sec) | # Cycles
+----------------------|------------------|------------|-------------------|----------
+Denaturation          | 95               | 10         | 2                 | 1
+Denaturation          | 95               | 0.5        | 2                 | 50
+Annealing/Extension   | 60               | 1          | 2                 | 50
+Signal Stabilization  | 98               | 10         | 2                 | 1
+Hold                  | 4                | ∞          | 2                 | 1
+
+After PCR is complete, transfer the plate to the Droplet Reader.
+
+1. Open the QuantaSoft software to set up a new plate layout. Designate the
+   sample name, experiment type, supermix type (ddPCR Supermix for Probes), the
+   target names and target types.
+
+2. When the plate layout is complete, select 'Run' to begin the droplet reading.
+
+3. When the droplet reading is complete, export the data from all wells as a CSV
+   file and use the following equation to calculate the titer for each sample:
+
+   T = (R × C × D × 1000) / V
+
+   Where:
+   • T = Titer, GC/mL
+   • R = Reaction volume (20 µL)
+   • C = Copies/µL
+   • D = Dilution factor of virus
+   • V = Volume of virus in reaction mix (4 µL)
+
+
+SAMPLE DATA
+================================================================================
+
+When analyzing data there should be a clear distinction between negative droplets
+(black) and positive droplets (blue).
+
+The no template control (NTC) should be close to zero (B08). At Addgene, we
+consider runs with an NTC > 10 to be invalid.
+
+To reduce NTC values, we recommend wiping down all pipettes and equipment with
+10% bleach prior to use and keeping all reagents and samples on ice or
+pre-chilled 96-well freezer blocks during use.
+
+In this protocol, a dilution series is prepared for each AAV sample, and the
+three final dilutions are assayed. The samples that are assayed are diluted
+2-fold serially, therefore the concentration obtained by ddPCR should decrease by
+a factor of 2 across the dilutions.
+
+In the example below, 2-fold serial dilutions of a sample were loaded in wells
+A04, A05 and A06. As shown in the image (Figure 1) and table below, the
+concentration of positive droplets decreases by a factor of ~2.
+
+To increase the accuracy of the titer, take the average of several measurements.
+
+Example dilutions and titrations:
+
+Sample      | Dilution | Concentration | Titer (GC/mL) | Average Titer (GC/mL)
+------------|----------|---------------|---------------|----------------------
+AAV Sample 1| 6400000  | 965           | 3.09E+13      | 3.2E+13
+AAV Sample 1| 12800000 | 474           | 3.03E+13      |
+AAV Sample 1| 25600000 | 271           | 3.47E+13      |
+NTC         | N/A      | 2.5           | N/A           | N/A
+
+For additional tips on AAV titering using ddPCR, read our blog post.
+
+Last reviewed on: August 31, 2023

BioScientist/agent_system/engines/v1_executor_backup/tool/protocols/addgene/Addgene_ Affinity Purification of Recombinant Antibodies with Protein A or Protein G.txt

@@ -0,0 +1,310 @@
+PROTOCOL: Affinity Purification of Recombinant Antibodies with Protein A or Protein G
+SOURCE: https://www.addgene.org/protocols/affinity-purification-recombinant-antibodies/
+================================================================================
+
+INTRODUCTION
+================================================================================
+
+This protocol describes how to affinity purify recombinant antibodies from cell
+culture supernatant using Protein A or Protein G columns.
+
+Last Update: February 7, 2022
+
+
+SAFETY WARNINGS
+================================================================================
+
+Sodium azide is toxic. When handling, use proper personal protective equipment
+including laboratory coat, laboratory goggles, and gloves. Sodium azide containing
+waste should be properly disposed of following your institution's hazardous waste
+procedures.
+
+
+WORKFLOW TIMELINE
+================================================================================
+
+Day 1: Purify antibody
+
+Day 2 or later: Buffer exchange
+
+
+EQUIPMENT
+================================================================================
+
+• Class II, Type A2 Biological Safety Cabinet
+• 4 °C Refrigerator
+• Pipette controller
+• Benchtop centrifuge compatible with 50 mL conical tubes
+• NanoDrop spectrophotometer
+• 37 °C, 5% CO₂ incubator with shaking platform set to 120 rpm
+• 37 °C bead bath
+• Clamp stand and clamps
+• Autoclave
+• 0.1–1 mL single channel pipette
+• 0.5–10 µL single channel pipette
+• 20–200 µL single channel pipette
+• 2–20 µL single channel pipette
+
+
+REAGENTS
+================================================================================
+
+• Aspirating pipette, VWR 414004-265
+• 5 mL pipette, VWR 89130-896
+• 10 mL pipette, VWR 89130-898
+• 25 mL pipette, VWR 89130-900
+• 50 mL pipette, VWR 89130-902
+• 50 mL conical tubes, VWR 89039-656
+• Microcentrifuge tubes, VWR 89126-724
+• Filtering system 0.2 µm PES, VWR 431098
+• Sodium phosphate monobasic monohydrate, Millipore Sigma 71507-250G
+• Sodium phosphate dibasic, Millipore Sigma S7907-500G
+• 1 M Trizma hydrochloride solution pH 9.0, Millipore Sigma T2819-1L
+• 250 mL sterile bottle, VWR 430281
+• 0.45 µm PES complete filtration unit, 500 mL, rapid-flow, VWR 73520-984
+• Syringes, 30 mL, VWR BD302832
+• Filter, 0.2 µm (luer-lock), VWR 431229
+• IgG Elution Buffer, Thermo Fisher 21028
+• Protein G GraviTrap columns, Cytiva 28-9852-55 or rProtein A GraviTrap
+  columns, Cytiva 28-9852-54
+• LabMate PD10 Buffer Reservoir, Millipre Sigma GE18-3216-03
+• 5% Sodium Azide, 500 mL, VWR 7144.8-16
+• PBS, 1X pH 7.4, VWR 45000-446
+• Benzamide, Millipore Sigma 12072
+• Antipain, Millipore Sigma 10791
+• Leupeptin, Millipore Sigma L2884
+• Aprotinin, Millipore Sigma A6279
+• Zeba Spin Desalting Columns, 7 kDa MWCO, 10 mL, Thermo Fisher 89893
+• Amicon Ultra-15 30 kDa MWCO column, Millipore Sigma UFC903024
+• 50 mL LoBind tubes, VWR 76289-498
+
+
+BEFORE STARTING
+================================================================================
+
+• Wipe down all pipettes and equipment with 10% bleach prior to use.
+
+• Warm the affinity column, binding buffer, and elution buffer to room
+  temperature (RT) before use.
+
+
+REAGENT PREPARATION
+================================================================================
+
+1 M sodium phosphate monobasic monohydrate (NaH₂PO₄∙H₂O), pH 7.0:
+
+• 138 g NaH₂PO₄∙H₂O
+• 1 L deionized water
+• Adjust pH to 7.0
+• Autoclave or filter sterilize
+
+1 M sodium phosphate dibasic (Na₂HPO₄), pH 7.0:
+
+• 142 g of Na₂HPO₄∙H₂O
+• 1 L deionized water
+• Adjust pH to 7.0
+• Autoclave or filter sterilize
+
+1 M of sodium phosphate buffer, pH 7.0:
+
+• 390 mL of sterile sodium phosphate monobasic monohydrate (NaH₂PO₄∙H₂O)
+• 610 mL of sterile sodium phosphate dibasic (Na₂HPO₄)
+• Store up to 1 month at 4 °C
+• Adjust pH to 7.0
+• Autoclave or filter sterilize
+
+Protein A/G binding buffer (20 mM sodium phosphate buffer, pH 7.0):
+
+• 20 mL 1M sodium phosphate buffer
+• 980 mL of deionized water
+• Adjust pH to 7.0
+• Autoclave or filter sterilize
+
+1000X protease inhibitor cocktail:
+
+• 25 mg leupeptin
+• 50 mg antipain
+• 250 mg benzamidine
+• 25 mL of 2 mg/mL aprotinin
+• Mix well and sterilize through a 0.2 µm PES filter.
+• Aliquot and freeze upright at -20 °C
+
+
+PROCEDURE
+================================================================================
+
+Section 1: Affinity chromatography
+
+1. Equilibrate filtered tissue culture supernatant containing the recombinant
+   antibody to room temperature (see Transfection for Recombinant Antibody
+   protocol).
+
+   💡 PRO TIP: Add protease inhibitors to 1X if this has not already been done.
+   We typically start with about 250 mL of supernatant and add 250 µL of 100X
+   protease inhibitor cocktail.
+
+2. Add 1 part Protein A/G binding buffer to 1 part tissue culture supernatant
+   and mix well.
+
+3. Uncap the Protein G Gravitrap or rProtein A Gravitrap columns and gently pour
+   off the ethanol storage buffer.
+
+4. Tightly attach LabMate PD10 Buffer Reservoirs to the top of the Gravitrap
+   column.
+
+   💡 PRO TIP: Gravitrap columns have a finite binding capacity. If your sample
+   exceeds the capacity, divide the sample among multiple columns. For
+   information about the total binding capacity for your columns, refer to the
+   manufacturer's instructions.
+
+5. Attach the Gravitrap column to a clamp on a clamp stand.
+
+6. Use scissors to cut the bottom of the Gravitrap column at the indentation in
+   the tip.
+
+7. Equilibrate the column with 10 mL of RT Protein A/G binding buffer.
+
+8. Collect the flow-through in a 50 mL conical tube and dispose after use.
+
+9. Carefully pour the filtered and diluted cell culture supernatant into the
+   Gravitrap column.
+
+10. Collect the flow through in a 250 mL bottle.
+
+11. Repeat steps 9–10 such that the supernatant passes through the column a total
+    of 2x.
+
+    💡 PRO TIP: This will ensure depletion of the recombinant antibody from the
+    supernatant.
+
+12. Wash the column with 25 mL of Protein A/G binding buffer 2x.
+
+13. Add 50 µL of 1 M Trizma hydrochloride pH 9.0 to 10 microcentrifuge tubes.
+
+14. Cap the Gravitrap columns at the bottom.
+
+15. Add 5 mL of Pierce IgG Elution Buffer to the capped Gravitrap column and
+    incubate for 10 min at RT.
+
+16. Uncap the column and collect 500 µL fractions into the tubes containing 50 µL
+    1 M Trizma hydrochloride pH 9.0.
+
+17. Cap the tubes and vortex for 5 s to mix.
+
+18. Determine the protein concentration of each fraction on the NanoDrop
+    Spectrophotometer using the A280 IgG setting.
+
+19. Combine all eluates with measurable protein.
+
+20. Determine the protein concentration of the pooled sample on the NanoDrop
+    Spectrophotometer using the A280 IgG setting.
+
+21. If the concentration of the pooled sample is above 1.0 mg/mL proceed to Option
+    1 with a buffer exchange using a Zeba Spin 7 kDa MWCO desalting column.
+
+22. If the concentration of the pooled sample is below 1.0 mg/mL proceed to
+    Option 2 with a buffer exchange/concentration using an Amicon 30 kDa MWCO buffer
+    exchange column.
+
+23. (Optional) Regenerate the column by washing with 25 mL of Protein A/G binding
+    buffer and store in 20% ethanol at 4 °C.
+
+    💡 PRO TIP: Columns may be re-used up to 5x when purifying the same
+    recombinant antibody.
+
+Section 2: Buffer exchange
+
+Choose Option 1 or Option 2 based on the concentration of the pooled sample above.
+
+Option 1: Buffer exchange using a desalting column
+
+The 10 mL Zeba Spin Desalting Columns can accommodate up to 4 mL of recombinant
+antibody sample.
+
+💡 PRO TIP: If the volume of the sample is greater than 4 mL, then divide it into
+2 columns.
+
+1. Twist off the 10 mL Zeba Spin Desalting Column's bottom closure and loosen cap.
+
+2. Place column in a 50 mL conical collection tube.
+
+3. Centrifuge column at 1000 x g for 2 min to remove storage solution.
+
+4. Place a mark on the side of the column where the compacted resin is slanted
+   upward. Place column in centrifuge with the mark facing outward in all
+   subsequent centrifugation step
+
+   💡 PRO TIP: Resin will appear compacted after centrifugation.
+
+5. Discard the flow through into a waste container.
+
+6. Add 5 mL PBS to the column.
+
+7. Centrifuge at 1000 x g for 2 min to remove PBS.
+
+8. Repeat Steps 6–7 3x, discarding buffer from the collection tube each time.
+
+9. Place column in a new 50 mL LoBind collection tube.
+
+10. Slowly apply the sample to the center of the compact resin bed.
+
+    💡 PRO TIP: For sample volumes <1.5 mL top off the sample with 0.2 mL PBS to
+    aid protein recovery.
+
+11. Allow the resin bed to fully absorb the sample.
+
+12. Centrifuge at 1000 x g for 2 min to collect the sample.
+
+13. Discard column after use.
+
+14. Determine antibody concentration on the NanoDrop Spectrophotometer.
+
+15. Dilute antibody to 1 mg/mL with PBS if needed.
+
+16. For long term storage, add sterile sodium azide to 1 mM.
+
+Option 2: Buffer exchange and concentration with an Amicon column
+
+1. Apply 15 mL of PBS to the reservoir of a Amicon Ultra-4 30 kDa MWCO column.
+
+2. Incubate for 10 min at RT.
+
+3. Centrifuge at 3100 x g for 8 min.
+
+4. Discard the flow through into a waste container.
+
+5. Add the recombinant antibody sample to the reservoir of the column.
+
+6. Fill the remaining space in the column with PBS and pipette several times to
+   mix.
+
+7. Centrifuge at 3100 x g for 8 min.
+
+8. Discard the flow through into a waste container.
+
+9. Repeat steps 5-8 at least 4 additional times to ensure a full buffer exchange.
+
+10. Discard the flow through into a waste container.
+
+11. Fill the remaining space in the column with PBS and pipette several times to
+    mix.
+
+12. Centrifuge at 3100 x g for 8 min.
+
+13. Remove a small aliquot of your sample from the filter reservoir to check the
+    concentration on the NanoDrop Spectrophotometer to see if the sample has
+    reached the desired concentration.
+
+14. If the sample concentration is still too low, repeat the centrifugation until
+    the volume of the sample is reduced enough to reach the desired concentration.
+
+    💡 PRO TIP: Periodically check the concentration on the NanoDrop
+    Spectrophotometer to see if the sample has reached the desired concentration.
+
+15. Gently transfer the sample from the reservoir to a LoBind tube.
+
+    💡 PRO TIP: Do not scrape the filter with the pipette tip.
+
+16. Determine the concentration of the sample on the NanoDrop Spectrophotometer.
+
+17. For long term storage, add sterile sodium azide to 1 mM.

BioScientist/agent_system/engines/v1_executor_backup/tool/protocols/addgene/Addgene_ Antibody Validation Using the Indirect ELISA Method.txt

@@ -0,0 +1,280 @@
+PROTOCOL: Antibody Validation Using the Indirect ELISA Method
+SOURCE: https://www.addgene.org/protocols/ab-validation-indirect-elisa/
+================================================================================
+
+INTRODUCTION
+================================================================================
+
+This protocol describes how to run an indirect Enzyme-Linked ImmunoSorbent Assay
+(ELISA) against a purified antigen to validate an antibody. This protocol will
+describe how to create a standard curve using known concentrations of the antigen
+and how to probe with an antibody against the target to show a dose response.
+
+
+GENERAL CONSIDERATIONS
+================================================================================
+
+The ELISA can be completed in a single day or broken up over several days by
+incubating certain steps at 4 °C instead of room temperature or 37 °C. The
+protocol notes when there are different incubation options available.
+
+Caution: Use the Stop Solution in a well ventilated area and avoid breathing in
+the vapors.
+
+
+WORKFLOW TIMELINE
+================================================================================
+
+Day 1: Antigen Coating
+
+Day 2: Blocking
+
+Day 3: Primary antibody incubation
+
+Day 4: Secondary antibody incubation and plate read
+
+
+EQUIPMENT
+================================================================================
+
+• Spectrophotometer compatible with 96-well plates
+• 1–10 µL single channel pipette
+• 2–20 µL single channel pipette
+• 20–200 µL single channel pipette
+• 200–1000 µL single channel pipette
+• 20–200 µL multichannel pipette
+• Multichannel pipette reagent reservoirs
+• Pipette controller
+• Microcentrifuge
+• Microplate shaker
+• ELISA plate washer (optional)
+• Scale
+• Microsoft Excel or similar software
+
+
+REAGENTS
+================================================================================
+
+• Purified antigen
+• Purified recombinant antibody
+• Isotype control antibody
+• HRP-conjugated isotype-specific secondary antibody
+• ELISA 96-well microplate, Corning 9018
+• Bovine serum albumin (BSA), VWR 10273-264
+• 50 mL conical tubes, VWR 89039-656
+• 96-well polyester (clear) microplate seal, Thermo Scientific 5701
+• Microcentrifuge tubes, Neptune 3745.X
+• Pipette tips, 1000 µL, VWR 76322-154
+• Pipette tips, 10 µL, VWR 76322-132
+• Pipette tips, 200 µL, VWR 76322-150
+• Pipette tips, 20 µL, VWR 76322-134
+• Pipettes, 10 mL, VWR 89130-898
+• 1 L polystyrene bottle, Corning 430518
+• PBS, 1X pH 7.4, VWR 45000-446
+• TMB (3,3',5,5'-tetramethyl-[1,1'-biphenyl]-4,4'-diamine) substrate kit, Pierce
+  34021
+• Tween-20, VWR 0777-1L
+• Foil
+
+
+BEFORE STARTING
+================================================================================
+
+Warm reagents to room temperature.
+
+
+PROCEDURE
+================================================================================
+
+Section 1: Prepare the Antigen Standard and coat the plate
+
+1. Dilute purified antigen to 20 ng/µL in a microfuge tube and vortex.
+
+   💡 PRO TIP: You may need to try a few different concentrations of antigen to
+   determine the ideal concentration.
+
+2. Prepare serial dilutions of the purified antigen as follows:
+   • 2 ng/µL: Add 100 µL of 20 ng/µL stock into 900 µL PBS in a microfuge tube
+     and vortex.
+   • 1 ng/µL: Add 450 µL of 2 ng/µL stock into 450 uL PBS in a microfuge tube and
+     vortex.
+   • 0.5 ng/µL: Add 450 µL of 1 ng/µL stock into 450 µL PBS in a microfuge tube
+     and vortex.
+   • 0.25 ng/µL: Add 450 µL of 0.5 ng/µL stock into 450 µL PBS in a microfuge tube
+     and vortex.
+   • 0.125 ng/µL: Add 450 µL of 0.25 ng/µL stock into 450 µL PBS in a microfuge
+     tube and vortex.
+   • 0 ng/µL: 450 µL PBS only.
+
+3. Pipette 50 µL into the first three wells of each row, using a different
+   standard solution for each row of a 96-well ELISA microplate.
+
+4. Cover the plate with a 96-well plate seal and incubate at 37 °C for 30 min,
+   or overnight at 4 °C.
+
+Section 2: Block the plate
+
+1. Prepare the wash buffer (0.05% Tween-20 in PBS) as follows:
+   • Pipette 0.5 mL of Tween-20 to into 999.5 mL 1X PBS
+   • Cap the bottle and invert several times to mix.
+
+2. Carefully remove the plate seal from the 96-well plate and set aside.
+
+3. Aspirate the wells and use a multichannel pipette to add 200 µL wash buffer to
+   each well. Washing can be done by hand but if available, use an automated ELISA
+   plate washer.
+
+4. Seal the plate.
+
+5. Place the plate on a microplate shaker set at 400 rpm and shake for 1 min.
+
+6. Repeat steps 3–5 twice for a total of three washes.
+
+7. After the final wash, aspirate the wash buffer from the wells.
+
+8. Prepare the blocking buffer (1% BSA in PBS) as follows:
+   • Add 250 mg of BSA to 25 mL of PBS.
+   • Cap the tube and invert several times to mix.
+
+9. Using a multichannel pipette, add 200 µL of blocking buffer to each well.
+
+10. Cover the plate with a plate seal and incubate on a microplate shaker set to
+    400 rpm for 2 h at room temperature or overnight at 4 °C.
+
+Section 3: Primary antibody incubation
+
+1. Carefully remove the plate seal from the 96-well plate and set aside.
+
+2. Aspirate the wells and use a multichannel pipette to add 200 µL wash buffer to
+   each well. Washing can be done by hand but if available, use an automated ELISA
+   plate washer.
+
+3. Seal the plate.
+
+4. Place the plate on a microplate shaker set at 400 rpm and shake for 1 min.
+
+5. Repeat steps 2–4 twice for a total of three washes.
+
+6. After the final wash, aspirate the wash buffer from the wells.
+
+7. Prepare antibody dilution buffer (0.1% BSA in PBS) as follows:
+   • Dilute 50 mg BSA into 50 mL PBS.
+   • Cap the tube and invert several times to mix.
+
+8. Dilute the primary antibody and an isotype control antibody to the desired
+   concentrations in the antibody dilution buffer.
+
+   💡 PRO TIP: The ideal antibody concentration will vary and needs to be
+   empirically determined. We suggest starting with three concentrations between
+   1–10 µg/mL.
+
+9. Cap and invert the tubes several times to mix.
+
+10. Using a multichannel pipette, add 100 µL of primary antibody solution to the
+    appropriate wells.
+
+11. Cover the plate with a plate seal and incubate on a microplate shaker set to
+    400 rpm for 2 h at room temperature or overnight at 4 °C.
+
+Section 4: Secondary antibody incubation
+
+1. Carefully remove the plate seal from the 96-well plate and set aside.
+
+2. Aspirate the wells and use a multichannel pipette to add 200 µL wash buffer to
+   each well. Washing can be done by hand but if available, use an automated ELISA
+   plate washer.
+
+3. Seal the plate.
+
+4. Place the plate on a microplate shaker set at 400 rpm and shake for 1 min.
+
+5. Repeat steps 2–4 twice for a total of three washes.
+
+6. After the final wash, aspirate the wash buffer from the wells.
+
+7. Dilute the HRP-conjugated isotype-specific secondary antibody to the desired
+   concentrations in the antibody dilution buffer.
+
+8. Cap and invert the tube several times to mix.
+
+9. Using a multichannel pipette, add 100 µL of secondary antibody to each well.
+
+10. Cover the plate with a plate seal and incubate on a microplate shaker set to
+    400 rpm for 2 h at room temperature or overnight at 4 °C.
+
+Section 5: TMB reaction
+
+1. Carefully remove the plate seal from the 96-well plate and set aside.
+
+2. Aspirate the wells and use a multichannel pipette to add 200 µL wash buffer to
+   each well. Washing can be done by hand but if available, use an automated ELISA
+   plate washer.
+
+3. Seal the plate.
+
+4. Place the plate on a microplate shaker set at 400 rpm and shake for 1 min.
+
+5. Repeat steps 2–4 twice for a total of three washes.
+
+6. After the final wash, aspirate the wash buffer from the wells.
+
+7. Immediately before use, mix 5 mL TMB Solution and 5 mL Peroxide Solution from
+   the TMB substrate kit.
+
+8. Cap the tube and invert several times to mix.
+
+9. Using a multichannel pipette, add 100 µL of the TMB/Peroxide mix to each well.
+
+10. Cover the plate with a plate seal and wrap in foil.
+
+11. Incubate plate at room temperature on a microplate shaker set at 400 rpm for
+    15–30 min.
+
+    💡 PRO TIP: Check the plate periodically to determine when the desired color
+    change has occurred. The color will change from yellow to blue and generally
+    takes between 15 to 30 min. High concentrations of HRP yield a greenish
+    solution. Stop the reaction before any wells display a green product. A
+    precipitated product indicates the presence of too much HRP and the need to
+    optimize experimental conditions.
+
+12. When the desired color change has occurred, gently remove the plate seal.
+
+13. Using a multichannel pipette, add 100 µL of TMB Stop Solution directly to the
+    wells to stop the reaction.
+
+14. Measure the absorbance of each well at 450 nm on the spectrophotometer. Use the
+    spectrophotometer to generate a standard curve. If you don't see any color
+    change or dose response, your antibody concentration may have been too low. In
+    this case, use higher antibody concentrations. If the reaction is saturated and
+    you don't see any dose response, your antibody concentration may have been too
+    high. In this case, use lower antibody concentrations. The ideal range of the
+    standard curve will vary between targets and needs to be empirically determined.
+    The dilution series created in section 1, step 2 is a suggested starting point.
+    If your unknown sample's absorbance falls above the range of the standard curve
+    you will need to either include higher concentrations in the standard curve or
+    dilute the unknown sample (don't forget to factor in that dilution when
+    calculating the original sample concentration!).
+
+
+TIPS AND TROUBLESHOOTING
+================================================================================
+
+• The optimal concentration of primary antibody to use will vary and needs to be
+  empirically determined. If possible, run an initial test with serially diluted
+  primary antibody and determine the best concentration for your assay.
+
+• Washing steps can be done by hand but if available, use an automated ELISA plate
+  washer.
+
+
+SAMPLE DATA
+================================================================================
+
+The plate was coated with serial dilutions of human recombinant purified Desmin
+(blue) or a negative control protein, Apolipoprotein L3 (red) overnight,
+blocked, and incubated overnight with Anti-Desmin [D7]. Plates were incubated with
+HRP-linked anti-Human IgG secondary antibody for 2 hours, reacted with TMB
+substrate and the reaction stopped after 15 minutes. Absorbance was read at 450 nm
+and plotted against the micrograms of antigen loaded.
+
+Last reviewed on: October 8, 2024

BioScientist/agent_system/engines/v1_executor_backup/tool/protocols/addgene/Addgene_ CRISPR Library Amplification.txt

@@ -0,0 +1,330 @@
+PROTOCOL: CRISPR Library Amplification
+SOURCE: https://www.addgene.org/protocols/pooled-library-amplification/
+================================================================================
+
+INTRODUCTION
+================================================================================
+
+Please refer to our pooled library material pages for amplification protocols that
+have been developed by the depositor for a specific pooled library available at
+Addgene. We recommend following the depositor's specific protocol if one is
+available. If a pooled library does not yet have an amplification protocol, the
+following protocol can be used as a starting point for CRISPR libraries.
+
+This protocol allows the amplification of a pooled-plasmid library in
+Escherichia coli cells. Pooled libraries contain tens to millions of different
+plasmids in a single sample. They are often used for screening, barcoding, or
+other high throughput multiplexed experiments. These pooled plasmids differ by
+only a short region of DNA: guide RNAs targeting different genes in an organism's
+genome, for example.
+
+Amplification is usually necessary to produce sufficient quantities of library for
+experimental applications. Repeated amplifications should be avoided as best as
+possible due to the inherent possibility of altering the composition of the
+library. Bottlenecks, fitness differences, and plasmid recombination can all
+impact the representation of individual plasmids in the pooled library during
+amplification. This protocol is designed to be as general as possible but note
+that individual libraries may require modifications dictated by the originating
+laboratory for optimal results. If you obtained the pooled library from Addgene,
+these protocols can be found on the Pooled Library page for the specific library.
+
+This protocol assumes familiarity with standard bacterial transformation and
+basic knowledge of the library being amplified. Please see our protocol on
+Bacterial Transformation. The generalized pooled library amplification protocol
+is expected to obtain satisfactory results for libraries up to 200,000 individual
+plasmids and can be scaled according to the actual number.
+
+
+REQUIRED QUALITY CONTROL (QC)
+================================================================================
+
+Pooled libraries can be challenging and expensive to ensure adequate quality, but
+this upfront cost will save headaches and expense later.
+
+At a minimum we recommend the use of a diagnostic digest and high-throughput next
+generation sequencing (NGS). Select a restriction enzyme for digest that will
+cut the shared plasmid backbone a single time and visualize that digestion on an
+agarose gel (see restriction digest protocol here). Lentiviral plasmids can
+recombine between their LTRs resulting in a smaller plasmid containing only the
+elements required for bacterial propagation (origin of replication and antibiotic
+selection). This recombination, at a low rate, is not typically a problem as
+these sequences are not efficiently packaged into lentiviral particles. See below
+for options if the recombinant band makes up a significant proportion of the DNA
+pool.
+
+Last Update: August 17, 2023
+
+
+WORKFLOW TIMELINE
+================================================================================
+
+Day 1: Transform, recover, set up overnight growth (Estimated time 2-3 hours)
+
+    Transformation should be performed at the end of the day to ensure that
+    growth times are limited.
+
+Day 2: Harvest cells and purify DNA (Estimated time 3-4 hours)
+
+    Cells should be harvested first thing in the morning to ensure that outgrowth
+    and competition are limited.
+
+
+EQUIPMENT
+================================================================================
+
+• Table top centrifuge
+• BioRad Electroporator (MicroPulser TM, Bio-Rad 1652100)
+
+
+REAGENTS
+================================================================================
+
+• 200 µL electrocompetent cells (Default: 4 tubes of Endura Duos, Lucigen,
+  60242-1)
+
+  Alternatives include Stbl4 cells or other ultra-high efficiency
+  electrocompetent cells that are suitable for unstable or recombination-prone
+  DNA.
+
+  The use of electrocompetent cells is essential to ensure high efficiency
+  uptake of plasmid library DNA.
+
+  This quantity of cells is sufficient for libraries up to 200,000 individual
+  plasmids. For larger libraries scale the number of cells and DNA accordingly.
+
+  💡 PRO TIP: Use of extreme high efficiency (1×10¹⁰ cfu/µg) commercially
+  prepared electrocompetent cells is strongly recommended.
+
+• 800 ng pooled library DNA (100 ng plasmid DNA per 25 µL electrocompetent cells)
+
+  Less library DNA can be used but this dramatically increases the chances of
+  individual plasmids being lost from the pool and/or increasing the skewness of
+  the pool.
+
+• 8 electroporation cuvettes (BioRad, Micropulser, 0.1 cm)
+• 20 mL SOC recovery media (Lucigen, 80026-1)
+• 8X LB Agar + Antibiotic 245 mm bioassay plates (Molecular Devices, X602)
+
+  💡 PRO TIP: Pour these plates at least one day in advance to allow adequate
+  time to fully gel and to dry slightly. We routinely use 350 mL of LB Agar per
+  bioassay plate.
+
+• 14 mL Vented Falcon Tubes (BD Biosciences, 352059)
+• 3X LB Agar + Antibiotic 65 mm Petri dish (VWR, 11019-552)
+• 4 MaxiPreps (Qiagen HiSpeed Max, Catalog #12663)
+• Tips (1000 µL, 200 µL, 10 µL)
+• Bacti Cell Spreaders (VWR, 60828-680)
+• 5 mL and 10 mL Serological pipettes
+• Ice slurry (Ice bucket with ice and water to create slurry)
+• 100 mL LB
+
+  At Addgene we use premixed LB (VWR 101414-072) for convenience but any brand
+  or house-made LB that supports normal growth is expected to work.
+
+• 50 mL Falcon Conical tubes (Fisher, 14-432-22)
+
+
+REAGENT PREPARATION
+================================================================================
+
+• Prepare, sterilize, and pour all LB Agar + Antibiotic plates.
+
+• Prewarm 12 mL recovery media at 37 °C (for at least 15 minutes).
+
+• Prewarm 3X LB Agar + Antibiotic plates at 37 °C.
+
+• Prewarm 8X LB Agar + Antibiotic Bioassay plates.
+
+• Prechill Micropulser cuvettes on ice.
+
+• Thaw 4 tubes of electrocompetent cells on ice for 15-20 minutes or until
+  completely thawed.
+
+• Chill a box of 200 µL micropipette tips in a -20 °C freezer.
+
+• Aliquot 3 mL SOC into each of four 14 mL Vented Falcon Tubes and have 1 mL SOC
+  per electroporation readily available for post-electroporation recovery of
+  cells.
+
+• Ensure access to autoclaved, sterile reagents for all steps.
+
+
+PROCEDURE
+================================================================================
+
+Day 1
+
+1. Add 200 ng DNA to each 50 µL aliquot of thawed Endura Duos on ice. Flick
+   gently to mix.
+
+2. Electroporate cells (one at a time for a total of eight electroporations):
+
+   Electroporator Conditions: Bio-Rad Micropulser Ec1 0.1 cm cuvette, 1.8 kV, 1
+   pulse.
+
+   a. Aliquot 25 µL DNA-Endura into pre-chilled cuvette.
+   b. Pulse.
+
+      Caution: Electroporation involves the use of high voltages, please use
+      caution when activating pulse and follow all specifications described in the
+      equipment manual.
+
+   c. Immediately add 1 mL SOC to cuvette.
+   d. Remove all liquid from cuvette and add to 14 mL vented Falcon Tube
+      containing 3 mL SOC.
+   e. Repeat for each of the 25 µl aliquots of cell/DNA mixture used (8 in the
+      described protocol) using a new vented falcon tube for every two
+      transformations. Each of the four 14 mL Vented Falcon Tubes should contain
+      a total of 5 mL (3 mL SOC + 2 mL transformed Endura from two separate
+      transformations).
+
+   💡 PRO TIP: Do not pipette repeatedly or mix when removing SOC containing
+   transformed DNA-Endura from cuvette.
+
+3. Shake four 14 mL Vented Falcon Tubes at 30-37 ℃, 225 rpm for 1 hour.
+
+4. After the 1 hour shaking period, pool and gently mix the four tubes.
+
+5. Perform sequential 1:100 dilutions of the cells (add 10 µL of the pool to 990
+   µL LB then perform a second and then third 1:100 dilution). Plate 100 µL of
+   each dilution onto a prewarmed Petri dish.
+
+6. Incubate plates at 30 ℃ overnight.
+
+7. Plate 2.5 mL of the transformed cells on each of the eight bioassay plates (two
+   plates per tube). Distribute evenly with a sterile spreader until all liquid
+   has been absorbed by the agar. This usually takes 1-2 minutes.
+
+   Critical: Be careful not to rip or shred the agar. Do so by gentle spreading.
+   Some spreaders have a sharp edge that can scrape plates more abrasively at a
+   certain angle.
+
+8. Incubate plates upside down at 30 ℃ overnight.
+
+   Critical: Ensure at this stage that no unabsorbed media drips onto the lid.
+   Let plates remain agar side up until dried before overnight incubation if
+   needed.
+
+9. Place 100 mL sterile LB at 4 ℃.
+
+Day 2 (morning)
+
+1. Before beginning, prechill at least four conical tubes on ice and ensure
+   access to sterile scraper and cold LB.
+
+   💡 PRO TIP: Prepare one to two more conical tubes on ice in case you need to
+   spread out the harvested cells further than four Maxipreps worth.
+
+2. Count colonies on the most dilute Petri dish.
+
+   Total colony yield = count × 100 × 100 × 100 ÷ 0.1
+
+   This number should be at least 1000X greater than number of perturbations in
+   library.
+
+   Example: At least 10 colonies on the most diluted plate for 1000x coverage of
+   a library of 100,000 plasmids.
+
+   Colonies may appear small and require extra incubation time in order to be
+   enumerated accurately.
+
+   Frequently the number of colonies can be too great to count. Ideally,
+   dilutions would have been sufficient to enumerate single colonies but as long
+   as one can ensure more than the required colonies are present. If colonies are
+   not present on the most dilute plate, count the second plate (ie. the 1:10,000
+   dilution).
+
+3. After 12-18 hours of growth, use spreader and cold LB to scrape bioassay
+   plates and remove bacteria.
+
+   • Use one scraper for all plates.
+   • Use two 10 mL pipettes (one for dispensing cold LB and one for removing LB
+     containing bacteria from plate).
+
+   💡 PRO TIP: Scraped bacteria in LB can clog the pipette either mix gently up
+   and down avoiding introducing bubbles or pour off plates into conical tubes as
+   needed.
+
+4. Add 10 mL cold LB to each plate for each scrape and use spreader to scrape
+   plates.
+
+   💡 PRO TIP: Pushing motion is better than pulling motion. Take care not to
+   split or gouge agar during the scraping process.
+
+5. Add each scrape into a 50 mL conical tube on ice pooling the scrapings of two
+   plates into each tube. Keep each tube on ice while scraping.
+
+6. Repeat addition of 10 mL cold LB and scrape for each plate until agar is clear
+   (all bacteria have been removed). This should produce 25 mL cold LB-bacteria
+   per plate.
+
+7. Centrifuge tubes (4000 G, 4 ℃, 15 minutes) to pellet bacteria.
+
+8. Decant LB and weigh pellet. The total weight of each pellet should be ~1-2 g.
+
+   💡 PRO TIP: Make sure to weigh the empty tube beforehand! If you've already
+   gone too far, weigh an identical empty tube. It will be close enough for the
+   purpose of a Maxiprep.
+
+9. Purify plasmid DNA using the Qiagen HiSpeed Maxi Kit (one conical is its own
+   Maxiprep).
+
+   Critical: Do not freeze pellets for later purification. Immediately purify them!
+   Commercial Maxipreps rely on incremental, ordered cell lysis. E. coli cells
+   are subject to lysis by freeze-thaw if not suspended in cryoprotectant like
+   Glycerol or DMSO solutions.
+
+10. Quantify the individual Maxipreps by Nanodrop, Picogreen, or Qubit.
+
+    Each different DNA measurement type can report slightly different
+    concentrations of DNA. In our hands fluorescent dye methods tend to report
+    lower values than Nanodrop, although most protocols have been designed with
+    Nanodrop as the quantification method.
+
+11. If all Maxipreps appear to contain sufficient DNA for use, pool samples, and
+    continue with analysis.
+
+    "Sufficient" DNA will vary upon intended use. Typically yields range from
+    several hundred micrograms to a milligram of total plasmid DNA.
+
+
+TIPS AND TROUBLESHOOTING
+================================================================================
+
+What do I do if my transformation efficiency is not high enough?
+
+Repeat the experiment with more cells, more DNA, gentler practices, and colder
+reagents (not including the recovery media!). Do not proceed with Maxipreps or
+NGS until adequate transformation efficiency is obtained. Ensure that
+electrocompetent cells are being used. Chemically competent cells will not
+provide adequate transformation efficiency! Ensure that no arcing is taking place
+during electroporation. Arcing would manifest as a loud pop often accompanied by
+some light in the cuvette during the electroporation step.
+
+I got a heavy recombined band by digest.
+
+Recombination is thought to be in part related to selective pressure. These
+recombined plasmids are much smaller and are a smaller nutritional burden on the
+bacteria. Bacteria that contain a recombinant plasmid likely grow faster in the
+closed system of a crowded plate or culture. Try restricting growth to the bare
+minimum by growing at 30 ℃, limiting growth time further, or potentially
+spreading less bacteria on a given plate (example: by increasing the total number
+of bioassay plates used). Scaling up the experiment using a Gigaprep can be used
+to obtain sufficient intact pooled library even in the face of a recombined
+fraction but care must be taken to perform adequate NGS based QC to ensure no
+change in representation compared to the pre-amplified stock. A last option is
+to perform a gel extraction of either the original sample or the amplified
+sample, followed by reamplification of the DNA, but please note that NGS should
+be performed to ensure representation is maintained.
+
+Maxipreps - Less is more:
+
+Do not overload the Maxipreps as yield can dramatically plateau and sometimes fall
+off entirely if the column or reagents are significantly overloaded. If you find
+that your pellet outstrips the capacity listed in the Maxiprep protocol, scale
+the reagent volume and column number as needed. The use of Mega or Gigapreps is
+acceptable when scaling up or reducing the number of tandem purifications. Consult
+the manufacturers handbooks for appropriate volumes and numbers of tandem
+purifications.
+
+Visit our Help Center to get details on how to process your Addgene pooled library