Paper Info
Title
RapMap: a rapid, sensitive and accurate tool for mapping RNA-seq reads to transcriptomes.
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.
Year
DOI
Venue
2016
10.1093/bioinformatics/btw277
BIOINFORMATICS
Field
DocType
Volume
Data mining,Data structure,Annotation,RNA-Seq,Biology,Transcriptome,Software,Bioinformatics,Locus (genetics),Genetics
Journal
32
Issue
ISSN
Citations 
12
1367-4803
5
PageRank 
References 
Authors
0.50
12
4
Name
Order
Citations
PageRank
Avi Srivastava1133.73
Hirak Sarkar261.87
Nitish Gupta350.50
Rob Patro411112.98