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

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

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

QC Thresholds

Read Depth Guidelines

Execution

Quick Start (Local Docker)

nextflow run scripts/main.nf \
  -profile local \
  --reads 'fastq/*_R{1,2}.fq.gz' \
  --genome GRCh38 \
  --outdir results/

SLURM HPC

nextflow run scripts/main.nf \
  -profile slurm \
  --reads 'fastq/*_R{1,2}.fq.gz' \
  --genome GRCh38 \
  --outdir results/

Google Cloud

nextflow run scripts/main.nf \
  -profile gcp \
  --reads 'gs://bucket/fastq/*_R{1,2}.fq.gz' \
  --genome GRCh38 \
  --outdir 'gs://bucket/results/'

AWS Batch

nextflow run scripts/main.nf \
  -profile aws \
  --reads 's3://bucket/fastq/*_R{1,2}.fq.gz' \
  --genome GRCh38 \
  --outdir 's3://bucket/results/'

Cloud Cost Estimates