/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 / Trim Galore ──> STAR (2-pass) ──> Genome BAM + Transcriptome BAM
  |                                                        |              |
  |                  ┌─────────────────────────────────────┘              |
  |                  v                                                    v
  |         Signal Track Generation                              RSEM Quantification
  |          (strand-specific bigWig)                        (gene + transcript + isoform)
  |                  |                                                    |
  |                  v                                                    v
  |          Plus strand bigWig                                genes.results (TPM/FPKM)
  |          Minus strand bigWig                               isoforms.results
  |                                                                       |
  |         ┌───────────────────────────────────────────────────────────┘
  |         v
  |   Kallisto (optional fast pseudoalignment)
  |         |
  |         v
  └──> RSeQC + MultiQC ──> Aggregated QC Report

Stage Summary

| Stage | Tool | Input | Output | Reference | |-------|------|-------|--------|-----------| | 1. QC & Trimming | FastQC, Trim Galore | Raw FASTQ | Trimmed FASTQ | references/01-qc-trimming.md | | 2. Alignment | STAR (2-pass) | Trimmed FASTQ | Genome BAM + Transcriptome BAM | references/02-star-alignment.md | | 3. Quantification | RSEM, Kallisto | Transcriptome BAM / 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 | BAM, counts | Strandedness, coverage, saturation | references/05-qc-metrics.md |

Input Requirements

Required Files

• **RNA-seq FASTQ**: Paired-end reads (ENCODE standard; single-end supported)
• **Reference genome**: STAR-indexed genome with gene annotation (GRCh38 + GENCODE for human)
• **Gene annotation**: GENCODE GTF (v38+ for human, vM27+ for mouse)

Sample Sheet Format

sample_id,read1,read2,replicate,strandedness
SAMPLE1_rep1,rna_R1.fq.gz,rna_R2.fq.gz,1,reverse
SAMPLE1_rep2,rna_R1.fq.gz,rna_R2.fq.gz,2,reverse

**Strandedness**: ENCODE uses dUTP-based stranded libraries. The resulting reads are `reverse` stranded (read 2 matches the sense strand). If unknown, the pipeline will auto-detect strandedness using RSeQC `infer_experiment.py`.

Library Strandedness

| Protocol | Strandedness | RSEM flag | Kallisto flag | Common Usage | |----------|-------------|-----------|---------------|-------------| | dUTP (ENCODE standard) | Reverse | `--strandedness reverse` | `--rf-stranded` | Most ENCODE RNA-seq | | SMARTer / SMART-Seq2 | Unstranded | `--strandedness none` | (default) | Single-cell, low-input | | Illumina TruSeq Stranded | Reverse | `--strandedness reverse` | `--rf-stranded` | Standard bulk RNA-seq | | Directional ligation | Forward | `--strandedness forwar