/pipeline-rnaseq

name: pipeline-rnaseq
description: "Execute ENCODE RNA-seq pipeline from FASTQ to gene quantification and signal tracks. Child of pipeline-guide. Provides Nextflow execution with Docker and cloud deployment. Use when processing RNA-seq data with STAR alignment, RSEM/Kallisto quantification, or generating expression matrices. Trigger on: RNA-seq pipeline, gene expression, STAR alignment, RSEM quantification, transcript quantification, TPM, FPKM, RNA processing, run RNA-seq."

ENCODE RNA-seq Pipeline

When to Use

• User wants to run an RNA-seq processing pipeline from FASTQ to gene quantification
• User asks about "RNA-seq pipeline", "STAR alignment", "RSEM", "gene expression quantification", or "Kallisto"
• User needs to process bulk RNA-seq data with ENCODE-standard 2-pass STAR alignment
• Example queries: "process my RNA-seq FASTQs", "quantify gene expression from RNA-seq", "run STAR and RSEM on my data"

Execute the ENCODE RNA-seq processing pipeline from raw FASTQ files through splice-aware alignment, gene/transcript quantification, and strand-specific signal track generation. This skill provides a complete Nextflow DSL2 implementation following ENCODE uniform analysis standards.

Overview

RNA-seq measures transcriptome-wide gene expression by sequencing cDNA derived from cellular RNA. The ENCODE pipeline processes RNA-seq data through quality control, splice-aware alignment with STAR (2-pass mode), gene and transcript quantification with RSEM, optional fast pseudoalignment with Kallisto, and generation of strand-specific signal tracks as bigWig files.

Key design decisions: STAR 2-pass mode for maximum splice junction sensitivity, RSEM for accurate gene/transcript/isoform quantification including multi-mapped reads, stranded library protocol (dUTP/rf-stranded) as the ENCODE standard, and paired-end sequencing with a minimum of 30 million uniquely mapped reads per replicate.

Key Literature

| Reference | Journal | Year | DOI | Relevance | |-----------|---------|------|-----|-----------| | Dobin et al. "STAR: ultrafast universal RNA-seq aligner" | Bioinformatics | 2013 | 10.1093/bioinformatics/bts635 | Splice-aware aligner (~12,000 citations) | | Li & Dewey "RSEM: accurate transcript quantification from RNA-Seq data" | BMC Bioinformatics | 2011 | 10.1186/1471-2105-12-323 | Gene/transcript quantification (~6,000 citations) | | Bray et al. "Near-optimal probabilistic RNA-seq quantification" | Nature Biotechnology | 2016 | 10.1038/nbt.3519 | Fast pseudoalignment (~4,000 citations) | | Wang et al. "RSeQC: quality control of RNA-seq experiments" | Bioinformatics | 2012 | 10.1093/bioinformatics/bts356 | RNA-seq QC suite (~3,500 citations) | | ENCODE Project Consortium "Expanded encyclopaedias" | Nature | 2020 | 10.1038/s41586-020-2493-4 | ENCODE Phase 3 standards | | Frankish et al. "GENCODE 2021" | Nucleic Acids Research | 2021 | 10.1093/nar/gkaa1087 | Gene annotation reference |

Pipeline Stages

FASTQ
  ├─> FastQC (raw reads)
  └─> Trim Galore (+ FastQC on the trimmed reads)
        ├─> Kallisto (optional) ──────────> kallisto/<sample>/abundance.tsv
        └─> STAR (2-pass)
              ├─> transcriptome BAM ─> RSEM ─> <sample>.genes.results / .isoforms.results
              ├─> bedGraph str1/str2 ─> bedGraphToBigWig ─> signal/<sample>_{plus,minus}.bw
              └─> genome BAM ─> RSeQC (infer_experiment, read_distribution,
                                       geneBody_coverage, inner_distance)

MultiQC <── FastQC (raw + trimmed), trimming reports, STAR Log.final.out,
            RSEM .stat/, RSeQC infer_experiment + read_distribution
  └─> qc/multiqc/multiqc_report.html

The Kallisto abundances, the gene body coverage and the inner distance files are published but are not part of the MultiQC report; read those files directly.

Stage Summary

| Stage | Tool | Input | Output | Reference | |-------|------|-------|--------|-----------| | 1. QC & Trimming | FastQC, Trim Galore | Raw FASTQ | Trimmed FASTQ + FastQC reports | references/01-qc-trimming.md | | 2. Alignment | STAR (2-pass) | Trimmed FASTQ | Genome BAM + Transcriptome BAM + bedGraph | references/02-star-alignment.md | | 3. Quantification | RSEM, Kallisto | Transcriptome BAM / trimmed FASTQ | Gene/transcript counts, TPM, FPKM | references/03-quantification.md | | 4. Signal Tracks | bedGraphToBigWig | STAR bedGraph | Strand-specific bigWig | references/04-signal-tracks.md | | 5. QC Metrics | RSeQC, MultiQC | Genome BAM, logs | Strandedness, read distribution, gene body coverage | references/05-qc-metrics.md |

Input Requirements

Required Files

• **RNA-seq FASTQ**: paired-end reads matched by the `--reads` glob (ENCODE standard;

single-end with `--single_end`)

• **STAR genome index directory** (`--star_index`)
• **RSEM reference prefix** (`--rsem_index`) produced by `rsem-prepare-reference`
• **BED12 gene model for RSeQC** (`--rseqc_bed`)
• **Kallisto index file** (`--kallisto_index`) built with kallisto 0.50.1, unless

`--skip_kallisto` is set. kallisto 0.50.1 writes index version 13 and rejects an index built with 0.48 or earlier

There is no sample sheet: samples are the pairs that `--reads` matches, and the sample ID is the shared prefix of each pair. The gene annotation is not a workflow parameter and there is no `--gtf`; the GTF is consumed when the STAR and RSEM references are built (references/02 and references/03), so the annotation is fixed by the index you pass in.

Library Strandedness

`--strandedness` takes one value for the whole run — `reverse` (default), `forward` or `none` — and is validated before the first task. It drives three things at once: the RSEM `--strandedness` flag, the kallisto strand flag, and which STAR bedGraph becomes which signal track.

| Protocol | `--strandedness` | RSEM receives | Kallisto receives | Signal tracks | |----------|------------------|---------------|-------------------|---------------| | dUTP (ENCODE standard), Illumina TruSe