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featureCounts Tutorial: Gene Expression Quantification for RNA-Seq

featureCounts Tutorial: Gene Expression Quantification for RNA-Seq

Last updated: March 13, 2026

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

Introduction

In RNA-seq analysis using next-generation sequencing, you obtain raw data known as FASTQ files (reads). After mapping each read to a reference genome, gene expression levels are quantified by counting the number of reads mapped to each gene.

This page walks you through how to use featureCounts, a tool for counting reads mapped to genomic features.

Installing featureCounts

The easiest way to install featureCounts is through Bioconda. Since featureCounts is part of the Subread package, you need to install Subread.

$ conda install -c bioconda subread

Try displaying the help message to confirm the installation.

$ featureCounts

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

Version 2.0.1 Usage: featureCounts [options] -a <annotation_file> -o <output_file> input_file1 [input_file2] ... ## Mandatory arguments: -a <string> Name of an annotation file. GTF/GFF format by default. See -F option for more format information. Inbuilt annotations (SAF format) is available in 'annotation' directory of the package. Gzipped file is also accepted. ...

Running Read Counting

Use the following command to count reads. In this example, we process four samples: sample1, sample2, sample3, and sample4.

$ featureCounts -p -t exon -g gene_id -a annotation.gtf -o counts.txt sample1.bam sample2.bam sample3.bam sample4.bam

Option Details

OptionDescription
-pCounts fragments instead of individual reads. Use this option for paired-end data.
-tSpecifies the feature type in the GTF file to use for read counting. The default is 'exon'.
-gSpecifies the GTF attribute to use as the grouping unit for read counting. The default is 'gene_id'.

In this example, reads are counted as fragments rather than individual reads, only reads mapped to exons are included, and the counts are aggregated by gene_id.

Results

Running the command above produces output like the following.

featureCountsの結果

The first line contains the featureCounts version and the command that was executed. From the seventh column onward, the read count results for each sample are shown.

Columns 1 through 6 contain the following information.

Column NumberColumn NameDescription
1GeneidGene ID
2ChrChromosome
3StartStart positions of all exons, separated by semicolons.
4EndEnd positions of all exons, separated by semicolons.
5StrandStrand orientation of all exons, separated by semicolons.
6LengthTotal gene length. If exons overlap, this value will be shorter than the sum of all individual exon lengths.

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.

overview

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.

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

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