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