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- Title
RAPSearch2: a fast and memory-efficient protein similarity search tool for next-generation sequencing data.
- Authors
Zhao, Yongan; Tang, Haixu; Ye, Yuzhen
- Abstract
With the wide application of next-generation sequencing (NGS) techniques, fast tools for protein similarity search that scale well to large query datasets and large databases are highly desirable. In a previous work, we developed RAPSearch, an algorithm that achieved a ~20-90-fold speedup relative to BLAST while still achieving similar levels of sensitivity for short protein fragments derived from NGS data. RAPSearch, however, requires a substantial memory footprint to identify alignment seeds, due to its use of a suffix array data structure. Here we present RAPSearch2, a new memory-efficient implementation of the RAPSearch algorithm that uses a collision-free hash table to index a similarity search database. The utilization of an optimized data structure further speeds up the similarity search-another 2-3 times. We also implemented multi-threading in RAPSearch2, and the multi-thread modes achieve significant acceleration (e.g. 3.5X for 4-thread mode). RAPSearch2 requires up to 2G memory when running in single thread mode, or up to 3.5G memory when running in 4-thread mode.
- Publication
Bioinformatics (Oxford, England), 2012, Vol 28, Issue 1, p125
- ISSN
1367-4811
- Publication type
Journal Article
- DOI
10.1093/bioinformatics/btr595