FridrichMethod featureCounts Counting
Count reads mapping to genomic features (genes, exons) from BAM files.
Basic Usage
# Single sample
featureCounts -a annotation.gtf -o counts.txt aligned.bam
# Multiple samples (recommended - single matrix output)
featureCounts -a annotation.gtf -o counts.txt sample1.bam sample2.bam sample3.bam
# All BAMs in directory
featureCounts -a annotation.gtf -o counts.txt *.bam
Paired-End Data
# Count fragments, not reads (required for paired-end)
featureCounts -p --countReadPairs -a annotation.gtf -o counts.txt *.bam
# Check proper pairs only
featureCounts -p --countReadPairs -B -C -a annotation.gtf -o counts.txt *.bam
Flags:
-p- Input is paired-end--countReadPairs- Count fragments instead of reads-B- Only count properly paired reads-C- Don't count chimeric fragments
Strand-Specific Libraries
# Unstranded (default)
featureCounts -s 0 -a annotation.gtf -o counts.txt *.bam
# Forward stranded (e.g., dUTP, NSR)
featureCounts -s 1 -a annotation.gtf -o counts.txt *.bam
# Reverse stranded (e.g., Illumina TruSeq, most common)
featureCounts -s 2 -a annotation.gtf -o counts.txt *.bam
Determining strandedness: Use infer_experiment.py from RSeQC or check library prep protocol.
Feature Types
# Count at gene level (default)
featureCounts -t exon -g gene_id -a annotation.gtf -o counts.txt *.bam
# Count at transcript level
featureCounts -t exon -g transcript_id -a annotation.gtf -o counts.txt *.bam
# Count CDS only
featureCounts -t CDS -g gene_id -a annotation.gtf -o counts.txt *.bam
Flags:
-t- Feature type in GTF (default: exon)-g- Meta-feature attribute (default: gene_id)
Multi-Mapping Reads
# Discard multi-mappers (default, recommended for DE)
featureCounts -a annotation.gtf -o counts.txt *.bam
# Count multi-mappers (fractional)
featureCounts -M --fraction -a annotation.gtf -o counts.txt *.bam
# Count multi-mappers (full count to each location)
featureCounts -M -a annotation.gtf -o counts.txt *.bam
Overlapping Features
# Discard reads overlapping multiple features (default)
featureCounts -a annotation.gtf -o counts.txt *.bam
# Count reads overlapping multiple features
featureCounts -O -a annotation.gtf -o counts.txt *.bam
# Fractional count for overlaps
featureCounts -O --fraction -a annotation.gtf -o counts.txt *.bam
Performance Options
# Use multiple threads
featureCounts -T 8 -a annotation.gtf -o counts.txt *.bam
# Use less memory (slower)
featureCounts --largeBAM -a annotation.gtf -o counts.txt *.bam
Output Files
featureCounts produces two files:
- counts.txt - Main count matrix
Geneid Chr Start End Strand Length sample1.bam sample2.bam
GENE1 chr1 100 500 + 400 1523 1891
GENE2 chr1 1000 2000 - 1000 892 756
- counts.txt.summary - Assignment statistics
Status sample1.bam sample2.bam
Assigned 1523456 1678234
Unassigned_Unmapped 12345 11234
Unassigned_NoFeatures 234567 245678
Extract Count Matrix
# Remove first 6 columns (metadata) to get just counts
cut -f1,7- counts.txt | tail -n +2 > count_matrix.txt
Python Processing
import pandas as pd
counts = pd.read_csv('counts.txt', sep='\t', comment='#')
count_matrix = counts.set_index('Geneid').iloc[:, 5:] # Skip metadata columns
count_matrix.columns = [c.replace('.bam', '') for c in count_matrix.columns]
count_matrix.to_csv('count_matrix.csv')
R Processing
counts <- read.table('counts.txt', header=TRUE, row.names=1, skip=1)
count_matrix <- counts[, 6:ncol(counts)] # Skip metadata columns
colnames(count_matrix) <- gsub('.bam', '', colnames(count_matrix))
Common Issues
Low assignment rate:
- Check strandedness setting (
-s) - Verify GTF matches reference genome version
- Check BAM alignment quality
Zero counts for known expressed genes:
- Ensure feature type matches GTF (
-t exonvs-t gene) - Check gene_id attribute name in GTF
Related Skills
- alignment-files/sam-bam-basics - Input BAM file handling
- genome-intervals/gtf-gff-handling - GTF annotation files
- differential-expression/deseq2-basics - Downstream analysis with counts
- rna-quantification/count-matrix-qc - QC of count data