The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote

By Yang Liao, Gordon K. Smyth, Wei Shi. Nucleic Acids Research, 2013

2,860 citations recorded by the source corpus.

Read alignment is an ongoing challenge for the analysis of data from sequencing technologies. This article proposes an elegantly simple multi-seed strategy, called seed-and-vote, for mapping reads to a reference genome. The new strategy chooses the mapped genomic location for the read directly from the seeds. It uses a relatively large number of short seeds (called subreads) extracted from each read and allows all the seeds to vote on the optimal location. When the read length is <160 bp, overlapping subreads are used. More conventional alignment algorithms are then used to fill in detailed mismatch and indel information between the subreads that make up the winning voting block. The strategy is fast because the overall genomic location has already been chosen before the detailed alignment is done. It is sensitive because no individual subread is required to map exactly, nor are individual subreads constrained to map close by other subreads. It is accurate because the final location must be supported by several different subreads. The strategy extends easily to find exon junctions, by locating reads that contain sets of subreads mapping to different exons of the same gene. It scales up efficiently for longer reads.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2587f44b866392c939190cdd787fd83e7f7d54f4

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Citations in the corpus

It's DE-licious: A Recipe for Differential Expression Analyses of RNA-seq Experiments Using Quasi-Likelihood Methods in edgeR (2016, methodology): “This can be done efficiently with a splice-aware aligner such as TopHat [13] or subread [4].”
limma powers differential expression analyses for RNA-sequencing and microarray studies (2015, methodology): “The authors of this article find the Subread (32) and featureCounts pipeline particularly convenient because it is fast, accurate (8) and can be run from the R prompt using the Rsubread package.”
cGAS surveillance of micronuclei links genome instability to innate immunity (2017, positive): “For quality control and pre-processing, quantification of mapped reads per gene was calculated using Rsubread 47.”
QuasR: quantification and annotation of short reads in R (2014, positive): “Availability and implementation: QuasR is implemented in R and C/Cþþ.”
voom: precision weights unlock linear model analysis tools for RNA-seq read counts (2014, methodology): “2 of the human genome using the Subread aligner [58].”
Normal aging induces A1-like astrocyte reactivity (2018, methodology): “To generate raw read counts from TopHat accepted hits output, we used featureCounts (52), an Rsubread package (53).”
The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads (2018, methodology): “Subread and Subjunc were the first aligners to implement such a two-pass strategy [13].”
Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses (2015, methodology): “5) (26) and summarized at the gene-level using the featureCounts procedure (27).”
PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data (2019, neutral): “The featureCounts program of subread (12) v.”
RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR (2016, neutral): “## [1] "Xkr4" "Rgs20" "Cpa6" "Sulf1" "Eya1" ## [6] "Msc" "Sbspon" "Pi15" "Crispld1" "Kcnq5" ## [11] "Ptpn18" "Arhgef4" "2010300C02Rik" "Aff3" "Npas2" ## [16] "Tbc1d8" "Creg2" "Il1r1" "Il18r1" "Il18rap"”
RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis (2018, background): “The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote.”