/salmon-rna-quantification

name: "salmon-rna-quantification"
description: "Ultra-fast RNA-seq transcript/gene quantification via quasi-mapping (no BAM). Builds a k-mer index from transcriptome FASTA, quantifies in minutes. Outputs TPM/count tables (quant.sf) with optional GC- and sequence-bias correction. Integrates with tximeta/tximport for DESeq2/edgeR. Use STAR when a genome-aligned BAM is needed."
license: "GPL-3.0"

Salmon — Fast RNA-seq Quantification

Overview

Salmon quantifies transcript abundance from RNA-seq reads using quasi-mapping — matching reads to a k-mer index of the transcriptome without full genome alignment. This makes Salmon 20–50× faster than alignment-based tools while producing accurate TPM and estimated count values. Salmon corrects for sequence-specific bias (`--seqBias`), GC-content bias (`--gcBias`), and fragment length distribution automatically. Output `quant.sf` files integrate directly with `tximeta` (R) or `pydeseq2` (Python) for differential expression analysis. For improved accuracy, decoy-aware indexing uses the full genome to identify spurious quasi-mappings.

When to Use

• Performing fast RNA-seq quantification when you do not need a genome-aligned BAM file
• Running large-scale RNA-seq studies where alignment speed is a bottleneck (Salmon is 20-50× faster than STAR + featureCounts)
• Computing TPM and estimated counts from bulk RNA-seq for differential expression with DESeq2 or edgeR
• Correcting for GC bias, fragment length, and sequence context bias with `--gcBias --seqBias`
• Estimating transcript-level uncertainty via bootstrap resampling with `--numBootstraps`
• Use **STAR** instead when you need a genome-aligned BAM for downstream tools (variant calling, deeptools, IGV visualization)
• Use **Kallisto** as an alternative for similar speed; Salmon provides better bias correction and decoy-aware indexing

Prerequisites

• **Software**: Salmon ≥ 1.10 (conda or pre-compiled binary)
• **Reference**: transcriptome FASTA (cDNA sequences, e.g., GENCODE or Ensembl) + genome FASTA for decoy-aware indexing
• **Python packages**: `pandas` for parsing output; `pydeseq2` for differential expression

> **Check before installing**: The tool may already be available in the current environment (e.g., inside a `pixi` / `conda` env). Run `command -v salmon` first and skip the install commands below if it returns a path. When running inside a pixi project, invoke the tool via `pixi run salmon` rather than bare `salmon`.

# Install with conda (recommended)
conda install -c bioconda salmon

# Verify
salmon --version
# salmon 1.10.3

# Or download pre-compiled binary
wget https://github.com/COMBINE-lab/salmon/releases/download/v1.10.0/salmon-1.10.0_linux_x86_64.tar.gz
tar xzvf salmon-1.10.0_linux_x86_64.tar.gz
export PATH="$PWD/salmon-latest_linux_x86_64/bin:$PATH"

Pre-flight Interview

Settle these with the user before writing any analysis code.

decisions:
  - id: D1
    param: reference
    kind: required
    source: user
    ask: "Which reference bundle should be used: species, genome assembly, and annotation release?"
    default: null

  - id: D2
    param: decoyAwareIndex
    kind: derived
    source: upstream
    depends_on: [D1]
    ask: "Does the selected reference bundle include a matching genome FASTA for building a decoy-aware index?"
    default: true

  - id: D3
    param: libType
    kind: required
    source: data
    ask: "Salmon inferred this library type from representative reads. Does it agree with the library preparation?"
    default: "A; inspect and confirm the inferred type in aux_info/meta_info.json"

  - id: D4
    param: biasCorrection
    kind: optional
    source: literature
    ask: "Should sequence- and GC-composition bias correction be enabled for cross-sample quantification?"
    default:
      seqBias: true
      gcBias: true

  - id: D5
    param: numBootstraps
    kind: optional_conditional
    source: upstream
    ask: "Will downstream analysis use inferential replicates (e.g., Swish/fishpond or sleuth)?"
    default: 0

  - id: D6
    param: selectiveAlignment
    kind: optional
    source: literature
    ask: "Should selective alignment with range-factorized equivalence classes be used for this quantification?"
    default:
      validateMappings: true
      rangeFactorizationBins: 4

  - id: D7
    param: threads
    kind: never_ask
    source: data
    reason: "Uses available CPU cores to affect runtime only, not quantification."
    default: "min(8, available_cores)"

D2 depends on D1 because the transcriptome and decoy genome must be from the same reference release. D3 is a required confirmation after inspecting Salmon's inferred library type; an ambiguous or unexpected inference is a QC failure.

Quick Start

# 1. Build transcriptome index (~5 min)
salmon index -t transcriptome.fa -i salmon_index/ -p 8

# 2. Quantify paired-end reads (~2-5 min per sample)
salmon quant \
    -i salmon_index/ \
    -l A \
    -1 sample_R1.fastq.gz \
    -2 sample_R2.fastq.gz \
    -p 8 \
    --gcBias --validateMappings \
    -o results/sample1/

# Output: results/sample1/quant.sf
head results/sample1/quant.sf

Workflow

Step 1: Download Transcriptome Reference

Fetch a transcript FASTA from GENCODE or Ensembl (cDNA sequences only — not genome).

# Human transcriptome from GENCODE (recommended)
wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_47/gencode.v47.transcripts.fa.gz
gunzip gencode.v47.transcripts.fa.gz

# Count transcripts
grep -c "^>" gencode.v47.transcripts.fa
# ~252,000 transcripts

echo "Reference ready."
ls -lh gencode.v47.transcripts.fa

Step 2: Build Salmon Index

Index the transcriptome for quasi-mapping. Add genome decoys for improved accuracy.

# Standard index (fast, sufficient for most analyses)
salmon index \
    -t gencode.v47.transcripts.fa \
    -i salmon_index/ \
    -p 8
echo "Standard index complete."

# Decoy-aware index (reco