RapMap: a rapid, sensitive and accurate tool for mapping RNA-seq reads to transcriptomes

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Abstract

Motivation: The alignment of sequencing reads to a transcriptome is a common and important step in many RNA-seq analysis tasks. When aligning RNA-seq reads directly to a transcriptome (as is common in the de novo setting or when a trusted reference annotation is available), care must be taken to report the potentially large number of multi-mapping locations per read. This can pose a substantial computational burden for existing aligners, and can considerably slow downstream analysis.

Results: We introduce a novel concept, quasi-mapping, and an efficient algorithm implementing this approach for mapping sequencing reads to a transcriptome. By attempting only to report the potential loci of origin of a sequencing read, and not the base-to-base alignment by which it derives from the reference, RapMap—our tool implementing quasi-mapping—is capable of mapping sequencing reads to a target transcriptome substantially faster than existing alignment tools. The algorithm we use to implement quasi-mapping uses several efficient data structures and takes advantage of the special structure of shared sequence prevalent in transcriptomes to rapidly provide highly-accurate mapping information. We demonstrate how quasi-mapping can be successfully applied to the problems of transcript-level quantification from RNA-seq reads and the clustering of contigs from de novo assembled transcriptomes into biologically meaningful groups.

Availability and implementation: RapMap is implemented in C ++11 and is available as open-source software, under GPL v3, at https://github.com/COMBINE-lab/RapMap.

Contact:rob.patro@cs.stonybrook.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

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