BioRead-QC-Boundaries-20K

RegalFire — AI Data Foundry. An original synthetic biology / computational sequence QC computational fixture benchmark.

Problem and differentiator

Parse a synthetic four-line FASTQ fixture, trim its low-quality suffix and apply a documented per-read filter policy.

Why download this instead of existing data?

These single-read fixtures explicitly label format validity, threshold-sensitive trimming and filtering, retaining intentionally malformed inputs. Whole sequencing batches and instrument-calibrated simulators serve different purposes; this release isolates auditable parser and arithmetic boundary behavior.

Research context: onlinelibrary.wiley.com, github.com, arxiv.org. These references support the relevance of the task area; they do not establish customer demand or that this release improves any model.

Verified release

20,000 accepted records; 20,000 unique IDs; 20,000 unique semantic task fingerprints; 20,000 source groups. Zero rejected records. Independent computational validation: PASS. Full restarted generation was byte-identical (see reproduction_report.json). No model training/evaluation run is claimed.

Split	Records
train	15929
validation	2031
test	2040

Construction family	Records
ambiguous_bases	4000
clean	4000
length_boundary	4000
malformed_record	4000
quality_boundary	4000

Ground-truth contract

Original short random A/C/G/T/N strings; no biological reference genome. Four lines with @synthetic_read, sequence, +, ASCII Phred+33 quality in scores 0–41. Malformed fixtures deliberately violate one field. These are intended task inputs, not rejected dataset rows. Valid reads trim the longest suffix whose base scores are below trim_q_min. Pass iff remaining length >= min_length, N count <= max_n and quality sum >= mean_q_min times length. All comparisons are inclusive; metrics use integers. GC count is absolute G+C count of the trimmed sequence, not a measured organism property. Invalid records have null metrics and do not pass.

Generator parser/suffix loop. Independent validator uses separate field/character checks, last-qualified-index trimming and Counter-based metrics. Neither imports fastp/FastQC nor claims identical tool defaults.

Record and file structure

Each JSONL row has id, index, seed, family, input, expected, fingerprint, group_id, split and provenance. Input contains the complete task; expected contains computable labels and witnesses where relevant. schema.json specifies exact field/type requirements. There are five full worked examples in examples/sample.json. data/dataset.jsonl is the complete canonical dataset; HF default loading uses only the disjoint train/validation/test files.

For agent evaluation, remove expected before passing inputs to the agent and keep the answer files inaccessible to it. These are public fixed-label fixtures, not contamination-resistant secret evaluations or recorded agent rollouts.

Reproduce and validate

Seed: 20261001. Published source and the generation environment are recorded in SOURCE_LICENSE.json. Exact byte reproduction was verified in that environment; matching seed alone is not a guarantee across Python versions.

pip install -r requirements.txt
python generator/generate.py --out reproduced --count 20000 --seed 20261001
python validator/validate.py --dataset reproduced/dataset.jsonl --count 20000 --out reproduced_qa
python generator/generate.py --out reproduced --count 20000 --seed 20261001 --resume
python -m unittest discover -s tests -p test_oracles.py -v

Writes use committed 500-record chunks, atomic checkpoint updates and fresh final assembly. Resume loads committed shards, verifies binding to seed/count/source, and regenerates any uncommitted work. A missing final file or checkpoint does not destroy committed records. Do not mix different seeds, counts or generator versions in one output folder.

Generator and validator implement different label algorithms; shared helpers only cover serialization, schema and canonical semantic identities. This is independent computational implementation, not independent human scientific review. Nine unit tests cover label corruption, exact small cases, schema types, missing/corrupt files, semantic symmetries and crash-resume reproduction. All passed locally; see the QA and reproduction reports.

Deduplication, splits and quality limits

Family quotas are equal. Split assignment is deterministic by source-group hash (approximately 80/10/10; exact counts above). Identical source groups never cross splits. Grid rotations/reflections are canonicalized; agent-label swaps are canonicalized in coordination tasks. Interval translation equivalents are canonicalized. DNA source grouping canonicalizes reverse complements. Each paired edit or sequence pair remains within its record.

The DNA near-duplicate audit screens 7-mer bottom-four sketches and at most 25 previous candidates per bucket, then checks Jaccard >=0.85 across split groups. It found 0 flags. This is a bounded similarity screen, not exhaustive homology analysis. Grid and interval checks cover exact stated equivalences, not all possible near-isomorphic or related scenarios. Splits are by generated source, not proven biological species or real game distribution. Public fixtures can be memorized.

Provenance, privacy and licensing

Entirely generated by the original supplied procedural source. No external dataset records, genomes, annotations, patient data, PHI, human identity profiles, proprietary game images, dialogue, maps, textures or binaries were used. Cited tools and papers are research context only; their data/code were not copied. MIT applies to this original release and generator/validator code; retain the LICENSE notice. SOURCE_LICENSE.json records permissions, attribution and environment.

Scope and limitations

Computational QC test fixtures, not real sequencing data, realistic basecaller-error simulation, genomic identity data or biological-function training. Quality distributions are deliberately simplified, many are constant around thresholds. No PHI, patient records, taxonomy, clinical labels or diagnosis/treatment use is asserted. No instrument-performance claim.

Tests establish the specified computational cases, not universal software correctness, biological validity of a simulation, clinical suitability, benchmark novelty over every related work or market value. Generator and validator were authored with Codex and have not been reviewed by a domain expert.

Commercial extension: Private trimming/filter policies, parser formats and CI fixture exports with approved domain rules. Domain rules and privacy/access constraints must be agreed and tested before delivery.

Custom / Private Dataset Work

RegalFire builds custom AI datasets, evaluation sets and reproducible data pipelines for research and production systems.

Available adaptations include:

• private schemas
• larger volumes
• custom domains
• custom validators
• private evaluation sets
• continuous generation
• custom environments

Contact: ootiris@gmail.com Hugging Face: RegalFire