The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads

By Yang Liao, Gordon K. Smyth, Wei Shi. bioRxiv, 2018

2,534 citations recorded by the source corpus.

The first steps in the analysis of RNA sequencing (RNA-seq) data are usually to map the reads to a reference genome and then to count reads by gene, by exon or by exon-exon junction. These two steps are at once the most common and also typically the most expensive computational steps in an RNA-seq analysis. These steps are typically undertaken using Unix command-line or Python software tools, even when downstream analysis is to be undertaken using R. We present Rsubread, a Bioconductor software package that provides high-performance alignment and counting functions for RNA-seq reads. Rsubread provides the ease-of-use of the R programming environment, creating a matrix of read counts directly as an R object ready for downstream analysis. It has no software dependencies other than R itself. Using SEQC data and simulations, we compare Rsubread to the popular non-R tools TopHat2, STAR and HTSeq. We also compare to counting functions provided in the Bioconductor infrastructure packages. We show that Rsubread is faster, uses less memory and produces read count summaries that more accurately correlate with true values. The results show that users can adopt the R environment for alignment and quantification without suffering any loss of performance.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3361a397b315ab3542548ac9e0fd333a8454ea0a

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In Molecular Biology

Citations in the corpus

Opportunities and challenges in long-read sequencing data analysis (2020, methodology): “The featureCounts [140] function from the Subread package [141, 142] supports long-read gene level counting.”
Orchestrating single-cell analysis with Bioconductor (2019, methodology): “A pipeline to process scRNA-seq data based on the R/Bioconductor software project is now available through the scPipe package [32], which uses the Rsubread Bioconductor package [33, 34] to provide alignments within R.”
A Patient-Derived Glioblastoma Organoid Model and Biobank Recapitulates Inter- and Intra-tumoral Heterogeneity. (2019, methodology): “Multimapping and chimeric alignments were discarded, and uniquely mapped reads were quantified at the exon level and summarized to gene counts using Rsubread (Liao et al., 2019).”
edgeR v4: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets (2024, neutral): “Alternatively, the feature-Counts2DGEList function imports output from Rsubread’s featureCounts function [ 47 ] directly without the need to write intermediate files to disk.”