salmon-rna-quantification

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"

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 (recommended for accuracy — uses full genome as decoy)
# Step 1: create decoy list from genome chromosome names
grep "^>" GRCh38.primary_assembly.genome.fa | cut -d " " -f 1 | sed 's/>//' > decoys.txt

# Step 2: concatenate transcriptome + genome
cat gencode.v47.transcripts.fa GRCh38.primary_assembly.genome.fa > gentrome.fa

# Step 3: build decoy-aware index
salmon index \
    -t gentrome.fa \
    -d decoys.txt \
    -i salmon_decoy_index/ \
    -p 8
echo "Decoy-aware index complete."

Step 3: Quantify Single-End Reads

Run Salmon on single-end FASTQ files.

# Single-end quantification
salmon quant \
    -i salmon_index/ \
    -l A \
    -r sample1.fastq.gz \
    -p 8 \
    --seqBias \
    --validateMappings \
    -o results/sample1/

echo "Mapping rate: $(grep 'Mapping rate' results/sample1/logs/salmon_quant.log | tail -1)"
echo "Output: results/sample1/quant.sf"

Step 4: Quantify Paired-End Reads with Bias Correction

Run Salmon on paired-end FASTQ files with recommended bias correction flags.

# Paired-end with GC bias + sequence bias correction
salmon quant \
    -i salmon_decoy_index/ \
    -l A \
    -1 sample1_R1.fastq.gz \
    -2 sample1_R2.fastq.gz \
    -p 8 \
    --gcBias \
    --seqBias \
    --validateMappings \
    --numBootstraps 100 \
    -o results/sample1/

# quant.sf columns: Name, Length, EffectiveLength, TPM, NumReads
head results/sample1/quant.sf

Step 5: Load and Summarize Quantification Output

Parse quant.sf to build a gene-level count matrix for differential expression.

import pandas as pd
from pathlib import Path

# Load single-sample output
quant = pd.read_csv("results/sample1/quant.sf", sep="\t")
print(f"Transcripts quantified: {len(quant)}")
print(f"Total estimated reads: {quant['NumReads'].sum():.0f}")
print(f"Transcripts with TPM > 1: {(quant['TPM'] > 1).sum()}")
print(quant.sort_values("TPM", ascending=False).head())

# Build a multi-sample TPM matrix
samples = ["ctrl_1", "ctrl_2", "treat_1", "treat_2"]
tpm_matrix = pd.DataFrame({
    s: pd.read_csv(f"results/{s}/quant.sf", sep="\t").set_index("Name")["TPM"]
    for s in samples
})
print(f"\nTPM matrix: {tpm_matrix.shape}")
tpm_matrix.to_csv("tpm_matrix.tsv", sep="\t")

Step 6: Aggregate to Gene Level and Run DESeq2

Summarize transcript-level estimates to gene level and perform differential expression.

import pandas as pd
import re
from pathlib import Path
from pydeseq2.dds import DeseqDataSet
from pydeseq2.default_inference import DefaultInference
from pydeseq2.ds import DeseqStats

# Aggregate transcript counts to gene level using Ensembl gene IDs
# quant.sf Name format: "ENST00000456328.2|ENSG00000223972.6|..."
def extract_gene_id(transcript_id):
    parts = transcript_id.split("|")
    return parts[1].split(".")[0] if len(parts) > 1 else transcript_id

samples = ["ctrl_1", "ctrl_2", "treat_1", "treat_2"]
count_frames = []
for s in samples:
    df = pd.read_csv(f"results/{s}/quant.sf", sep="\t")
    df["gene_id"] = df["Name"].apply(extract_gene_id)
    gene_counts = df.groupby("gene_id")["NumReads"].sum().round().astype(int)
    count_frames.append(gene_counts.rename(s))

count_matrix = pd.DataFrame(count_frames).fillna(0).astype(int)
metadata = pd.DataFrame({
    "condition": ["control", "control", "treated", "treated"]
}, index=samples)

# Run DESeq2
dds = DeseqDataSet(counts=count_matrix, metadata=metadata,
                   design_factors="condition",
                   inference=DefaultInference(n_cpus=4))
dds.deseq2()

stat_res = DeseqStats(dds, contrast=["condition", "treated", "control"],
                      inference=DefaultInference())
stat_res.summary()
results = stat_res.results_df
print(f"DE genes (padj < 0.05): {(results['padj'] < 0.05).sum()}")
print(results[results['padj'] < 0.05].sort_values('log2FoldChange').head())

Key Parameters

| Parameter | Default | Range/Options | Effect | |-----------|---------|---------------|--------| | -l / --libType | required | A (auto), SF, SR, IU, IS, MS, MR | Library strandedness; A auto-detects from first reads | | -p / --threads | 1 | 1–64 | CPU threads; 8–16 is typical | | --gcBias | off | flag | Correct for GC-content bias in fragment selection; recommended for most samples | | --seqBias | off | flag | Correct f