featureCounts — RNA-seq Read Counting

Overview

featureCounts (part of the Subread package) assigns sequencing reads in BAM files to genomic features defined in a GTF/GFF annotation. It counts how many reads overlap each gene (or exon, intron, or custom feature), producing a gene × sample count matrix suitable for differential expression analysis with DESeq2 or edgeR. featureCounts processes multiple BAM files in a single command, reporting read assignment statistics (assigned, unassigned by category) alongside the count matrix. It is the standard counting step after STAR alignment in RNA-seq pipelines.

When to Use

• Generating gene-level count matrices from STAR-aligned BAM files for DESeq2 or edgeR
• Counting reads from multiple samples simultaneously in a single featureCounts command
• Handling stranded RNA-seq libraries where sense/antisense assignment matters
• Producing exon-level or custom-feature counts (e.g., for splicing analysis with DEXSeq)
• Verifying strandedness of an RNA-seq library when protocol documentation is unavailable
• Use Salmon instead when no BAM file exists and fast pseudoalignment is preferred
• Use HTSeq-count as an alternative with slower but more flexible counting modes

Prerequisites

• Software: Subread package (contains featureCounts)
• Input: Sorted BAM files from STAR or HISAT2, plus a matching GTF annotation file

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

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

# Verify
featureCounts -v
# featureCounts v2.0.6

# Alternative: install via apt (Ubuntu/Debian)
sudo apt-get install subread

Quick Start

# Count reads for multiple samples (unstranded paired-end RNA-seq)
featureCounts \
    -a gencode.v47.annotation.gtf \
    -o counts/gene_counts.txt \
    -T 8 \
    -p --countReadPairs \
    results/sample1/Aligned.sortedByCoord.out.bam \
    results/sample2/Aligned.sortedByCoord.out.bam

echo "Count matrix: counts/gene_counts.txt"
head -3 counts/gene_counts.txt

Workflow

Step 1: Prepare BAM Files and GTF

Ensure BAM files are sorted and indexed, and the GTF matches the genome assembly.

# Verify BAM files are sorted
samtools view -H results/sample1/Aligned.sortedByCoord.out.bam | grep "SO:"
# Expected: SO:coordinate

# List BAMs to count
ls results/*/Aligned.sortedByCoord.out.bam | head -5

# Download GENCODE GTF (same version used for STAR indexing)
wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_47/gencode.v47.primary_assembly.annotation.gtf.gz
gunzip gencode.v47.primary_assembly.annotation.gtf.gz

echo "GTF lines: $(wc -l < gencode.v47.primary_assembly.annotation.gtf)"

Step 2: Determine Library Strandedness

Test strandedness using a small read count to set the -s parameter correctly.

# Quick strandedness check: count 1 sample with all 3 modes
# Compare assigned rates: highest = correct mode
for strand in 0 1 2; do
    echo "=== Strandedness -s $strand ==="
    featureCounts \
        -a gencode.v47.primary_assembly.annotation.gtf \
        -o /tmp/test_s${strand}.txt \
        -T 4 \
        -p --countReadPairs \
        -s $strand \
        results/sample1/Aligned.sortedByCoord.out.bam 2>&1 \
        | grep "Successfully assigned"
done
# Rule: 0=unstranded if similar rates; 1 or 2 if one is much higher

Step 3: Count Unstranded Paired-End RNA-seq

Standard configuration for unstranded libraries (most polyA-selected RNA-seq).

mkdir -p counts

# Multi-sample batch counting: pass all BAMs as positional arguments
featureCounts \
    -a gencode.v47.primary_assembly.annotation.gtf \
    -o counts/gene_counts.txt \
    -T 8 \
    -p \
    --countReadPairs \
    -s 0 \
    -t exon \
    -g gene_id \
    results/ctrl_1/Aligned.sortedByCoord.out.bam \
    results/ctrl_2/Aligned.sortedByCoord.out.bam \
    results/treat_1/Aligned.sortedByCoord.out.bam \
    results/treat_2/Aligned.sortedByCoord.out.bam

echo "Count matrix: $(wc -l < counts/gene_counts.txt) genes"
# Also generates: counts/gene_counts.txt.summary (assignment stats)
cat counts/gene_counts.txt.summary

Step 4: Count Stranded Libraries

For strand-specific libraries (TruSeq Stranded, QuantSeq), set the correct strandedness.

# Reverse-stranded library (most TruSeq Stranded protocols): -s 2
featureCounts \
    -a gencode.v47.primary_assembly.annotation.gtf \
    -o counts/gene_counts_stranded.txt \
    -T 8 \
    -p --countReadPairs \
    -s 2 \
    results/*/Aligned.sortedByCoord.out.bam

# Forward-stranded (e.g., Lexogen QuantSeq, Takara SMARTer): -s 1
# featureCounts ... -s 1 ...

echo "Stranded count complete."
head -2 counts/gene_counts_stranded.txt

Step 5: Load Count Matrix into Python for DESeq2

Parse the featureCounts output file and prepare for differential expression.

import pandas as pd

# featureCounts output has 6 metadata columns before count columns
counts_raw = pd.read_csv("counts/gene_counts.txt", sep="\t", comment="#")
print(f"Columns: {list(counts_raw.columns)}")

# Metadata columns: Geneid, Chr, Start, End, Strand, Length
# Count columns start at index 6
count_cols = counts_raw.columns[6:]  # BAM file paths as column names
counts = counts_raw.set_index("Geneid")[count_cols].copy()

# Rename columns to sample names (strip path and file extension)
import re
counts.columns = [re.sub(r".*/|Aligned\.sortedByCoord\.out\.bam", "", col)
                  for col in counts.columns]

print(f"Count matrix shape: {counts.shape}")  # (genes × samples)
print(f"Samples: {list(counts.columns)}")
print(f"Genes with counts > 0: {(counts.sum(axis=1) > 0).sum()}")
counts.to_csv("gene_count_matrix.tsv", sep="\t")
print("Saved: gene_count_matrix.tsv")

Step 6: Run DESeq2 with featureCounts Matrix

Use the count matrix directly in pydeseq2 for differential expression.

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

# Load count matrix (genes × samples)
counts = pd.read_csv("gene_count_matrix.tsv", sep="\t", index_col=0).T
print(f"Count matrix: {counts.shape} (samples × genes)")

# Sample metadata
metadata = pd.DataFrame({
    "condition": ["control", "control", "treated", "treated"]
}, index=counts.index)

# Filter low-count genes (recommended before DESeq2)
counts_filtered = counts.loc[:, counts.sum() > 10]
print(f"Genes after low-count filter: {counts_filtered.shape[1]}")

# Run DESeq2
dds = DeseqDataSet(counts=counts_filtered, metadata=metadata,
                   design_factors="condition",
                   inference=DefaultInference(n_cpus=8))
dds.deseq2()

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

Key Parameters