RAPSearch2: a fast and memory-efficient protein similarity search tool for next-generation sequencing data.
CONCLUSIONS
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.


METHODS
Implemented in C++, the source code is freely available for download at the RAPSearch2 website: http://omics.informatics.indiana.edu/mg/RAPSearch2/.


BACKGROUND
yye@indiana.edu


BACKGROUND
Available at the RAPSearch2 website.
School of Informatics and Computing, Indiana University, Bloomington, IN 47404, USA.
Yongan
Y
Zhao
Haixu
H
Tang
Yuzhen
Y
Ye
D000465:Algorithms; D030562:Databases, Protein; D020869:Gene Expression Profiling; D059014:High-Throughput Nucleotide Sequencing; D011381:Programming Languages; D011506:Proteins / Q000737:chemistry / Q000235:genetics*; D057386:Search Engine; D012984:Software
D011506:Proteins