---
pretty_name: miniENCODE
tags:
  - biology
  - genomics
  - rna-seq
  - small-rna-seq
  - mirna
  - cell-lines
---

# miniENCODE

Bulk RNA-seq and small-RNA-seq of 29 human cancer and reference cell lines: raw reads, alignments,
quantifications and count matrices. This is a copy of the project folder, uploaded unchanged
(1,101 files, 102.19 GB). `FILELIST.tsv` lists every file with its size in bytes and modification time.
One 5.5 KB R history file (`RNA-seq/process.Rhistory`) could not be read and is not included.

No license is specified. Contact the data owners before reusing the data.

## Download

```bash
huggingface-cli download adibvafa/miniENCODE --repo-type dataset --local-dir miniENCODE
```

## Samples

The cell lines are A2058, A375, A549, BxPC3, C4-2B, Colo320, H1299, H23, H358, HCC44, HCT116,
HEK293T (4 replicates), HepG2, Hs707A, K562, LNCaP, LS174T, MCF7, MDA-MB-231, MDA-453,
MIA PaCa-2, PANC-1, PC3, SW480, SW620 and ZR-75-1.

`smRNA-seq/meta.tsv` maps each sample name to its cell line, tissue, library size and TCGA cancer
code. Sample names keep the original spellings: A2508 = A2058, PACA2 = MIA PaCa-2, C42Bu = C4-2B,
ZR751 = ZR-75-1, and a "u" suffix in SW480u, SW620u, H23u and H358u.

## `RNA-seq/` (454 files, 69.95 GB)

| Path | Contents |
|---|---|
| `<n>-<line>_S<n>_L00{1,2}_R{1,2}_001.fastq.gz` | Paired-end raw reads: 2 lanes × 2 mates × 29 samples (116 files) |
| `<n>-<line>_S<n>_salmon/` | Salmon 0.14.1 output per sample: `quant.sf`, `cmd_info.json`, `lib_format_counts.json`, `logs/`, `aux_info/` (`meta_info.json` has the mapping rate), `libParams/` |
| `gencode.v35.transcripts.salmon.index/` | The Salmon index (~4.3 GB) |
| `gencode.v35.transcripts.fa.gz`, `gencode.v35.transcripts.tx2gene.csv` | The GENCODE v35 transcriptome and its transcript-to-gene map |
| `cell-lines_tpm_salmon.txt` | Transcript-level TPM matrix for all samples |
| `cell-lines_tpm_gene_salmon.txt`, `cell-lines_tpm_gene_symbol_salmon.txt`, `cell-lines_tpm_gene_col_salmon.txt` | Gene-level TPM matrices (by gene ID, by gene symbol, and with another column layout) |
| `pipeline.txt` | The exact quantification loop: `salmon quant --validateMappings -i gencode.v35.transcripts.salmon.index -l ISR -1 L001_R1 L002_R1 -2 L001_R2 L002_R2 -p 12` |
| `RNA-seq.ipynb` | Analysis notebook |

## `smRNA-seq/` (647 files, 32.24 GB)

| Path | Contents |
|---|---|
| `fastq/` | 116 files: per sample, the raw `<line>_S<n>_L003_R{1,2}_001.fastq.gz`, a merged `<line>.fastq.gz` and an adapter-trimmed `<line>.trim.fastq.gz` |
| `bam/` | 319 files: sorted (`.srt`), deduplicated (`.srt.dd`), MAPQ-filtered (`.srt.dd.q<N>`) and miRNA-only (`.hsa-miR.srt.dd`) alignments, with `.bai` indexes |
| `bed/` | 29 files: `<line>.srt.dd.bed`, the alignments as BED |
| `count/` | 145 files: per-sample `.smRNA.cnt`, `.tRNA.cnt`, `.annotated.cnt`, `.hsa-miR.cnt` and `.oncRNA.cnt` |
| `smRNA_counts.txt`, `smRNA_norm-counts.txt` | Locus × sample counts, raw and normalized |
| `annotated_smRNA_*.txt`, `miRNA_*counts.txt`, `tRF_*counts.txt` | Annotated small-RNA, miRNA and tRNA-fragment count matrices |
| `smRNA-seq_oncRNA_{count,cpm,bin}.tsv`, `lib_size.q10.txt` | oncRNA matrices and library sizes |
| `combined_smRNA_loci{,.c}.bed.gz` | Small-RNA locus definitions |
| `mature.fa.gz`, `mature.hsa.fa(.fai)`, `mature_hsa*.bt2` | miRBase mature sequences and a bowtie2 index of human mature miRNAs |
| `smRNA_process.ipynb`, `miRNA_analysis.ipynb`, `oncRNA_analysis.ipynb`, `append_umi.py` | Processing and analysis code (`append_umi.py` moves the UMI into read names) |
| `*.pdf`, `smRNA-seq_TCGA_by_code_overlap.tsv` | Heatmap, PCA, TCGA-overlap and UpSet figures, and the overlap table |
| `meta.tsv` | Sample sheet |

`.ipynb_checkpoints/` folders (Jupyter autosaves) are included because the whole folder was copied.