Salmon Tutorial: Ultra-Fast RNA-Seq Quantification

Last updated: March 13, 2026

📖 RNA-Seq Data Analysis Workflow — check it out for an overview.

Introduction

Salmon is an ultra-fast tool for quantifying gene expression from RNA-Seq data. Most gene expression quantification tools require a separate alignment step beforehand, but Salmon does not. Instead, Salmon performs a lightweight process called "Quasi-Mapping" and uses those results to estimate expression levels, achieving remarkably fast processing speeds.

For an overview of the entire RNA-Seq data analysis workflow, see the RNA-Seq analysis workflow guide.

Installation

Pre-built binaries are available, so if your environment is compatible, downloading them is the easiest option.

$ wget https://github.com/COMBINE-lab/salmon/releases/download/v1.10.0/salmon-1.10.0_linux_x86_64.tar.gz $ tar -zxvf salmon-1.10.0_linux_x86_64.tar.gz

You can also install Salmon via conda.

$ conda install -c bioconda salmon

Try displaying the help message to verify the installation.

$ salmon -h

If you see output similar to the following, the installation was successful.

salmon -h salmon v1.10.0 Usage: salmon -h|--help or salmon -v|--version or salmon -c|--cite or salmon [--no-version-check] <COMMAND> [-h | options] Commands: index : create a salmon index quant : quantify a sample alevin : single cell analysis swim : perform super-secret operation quantmerge : merge multiple quantifications into a single file

Creating an Index

First, build an index of the reference sequences using the following command.

$ salmon index -t cdna.fasta.gz -i salmon_index

The `cdna.fasta.gz` file is a transcript FASTA file. For human data, you can use a file such as Homo_sapiens.GRCh38.cdna.all.fa from Ensembl.

The following files, which are needed for expression quantification, will be created inside the `salmon_index` directory.

complete_ref_lens.bin duplicate_clusters.tsv pos.bin refAccumLengths.bin refseq.bin ctable.bin info.json pre_indexing.log ref_indexing.log seq.bin ctg_offsets.bin mphf.bin rank.bin reflengths.bin versionInfo.json

Gene Expression Quantification

Next, quantify gene expression levels with the following command.

$ salmon quant -i salmon_index -l A -1 sample1_1.fastq.gz -2 sample1_2.fastq.gz --validateMappings -o sample1_salmon_result

The `validateMappings` option improves mapping sensitivity and specificity. While it slightly increases the analysis time, it is generally recommended to enable it.

The quantification results are stored in the `quant.sf` file inside the output directory, as shown below.

Name Length EffectiveLength TPM NumReads AT1G76820.1 3857 3712.468 0.000000 0.000 ATMG00060.1 542 397.697 21.821769 83.000 AT4G16360.1 1685 1540.468 25.317486 373.000 AT5G26800.1 637 492.546 33.540907 158.000 AT4G16110.1 2666 2521.468 16.545652 399.000 AT5G39100.1 808 663.507 0.000000 0.000 AT4G15130.1 1234 1089.468 40.518705 422.188 ATMG01320.1 922 777.486 20.897638 155.391 ...

Preparation for DEG Extraction

Use `tximport` to aggregate the results into a table so that they can be analyzed with DESeq2 or edgeR.

library(tximport) files <- file.path(list.files('.', pattern = 'salmon_result'), 'quant.sf') names(files) <- c('sample1', 'sample2', 'sample3', ...) txi <- tximport(files, type = "salmon", txOut = TRUE) head(txi$counts)

You can export the results to a CSV file as follows.

write.csv(txi$counts, file="counts.csv")

RNA-Seq Data Analysis Software

This is an RNA-Seq Data Analysis Software recommended for those who:

✔︎ Seeking to avoid outsourcing or collaboration for RNA-Seq data analysis.

✔︎ Lacking time to learn RNA-Seq data analysis.

✔︎ Frustrated by the complexity of existing tools.

Users can perform gene expression quantification, identification of differentially expressed genes, gene ontology(GO) analysis, pathway analysis, as well as drawing volcano plots, MA plots, and heatmaps.

About the Author

BxINFO LLC

A research support company specializing in bioinformatics.

We provide tools and information to support life science research, with a focus on RNA-Seq analysis.

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