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

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.