FeatureCounts

FeatureCounts summarizes mapped sequencing reads to quantify RNA-seq and gDNA-seq signal over predefined genomic elements such as genes, exons, promoters, gene bodies, and genomic bins.

Key Features:

• Read quantification: Counts the number of sequence reads that map to predefined genomic elements for gene- and exon-level summarization.
• Supported sequencing types: Operates on RNA-seq and gDNA-seq data.
• Read layouts: Supports both single-end and paired-end reads.
• Target genomic features: Summarizes counts over genes, exons, promoters, gene bodies, and genomic bins.
• Algorithms: Employs chromosome hashing and feature blocking to accelerate read assignment to features.
• Performance: Achieves low memory usage and speed improvements reported up to an order of magnitude for gene-level summarization compared with existing methods.
• Implementation: Available as part of the Rsubread Bioconductor package and the Subread SourceForge package.

Scientific Applications:

• Differential expression analysis: Provides gene- and exon-level count matrices used for differential expression studies.
• Genome-wide association and genomic analyses: Produces feature-level read counts for genome-wide association studies and other analyses that require read summarization over promoters, gene bodies, or genomic bins.
• General read summarization: Supplies quantitative summaries of mapped reads for downstream genomic and transcriptomic analyses.

Methodology:

Assigns reads from single-end or paired-end RNA-seq and gDNA-seq to predefined genomic elements by counting mapped reads and using chromosome hashing and feature blocking to reduce memory usage and increase speed.