RAPSearch2: a fast and memory-efficient protein similarity search tool for next-generation sequencing data. SUMMARY: 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. AVAILABILITY AND IMPLEMENTATION: Implemented in C++, the source code is freely available for download at the RAPSearch2 website: http://omics.informatics.indiana.edu/mg/RAPSearch2/.

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  1. Elahifasaee, Farzaneh; Li, Fan; Yang, Ming: A classification algorithm by combination of feature decomposition and kernel discriminant analysis (KDA) for automatic MR brain image classification and AD diagnosis (2019)
  2. Keith, Jonathan M. (ed.): Bioinformatics. Volume I. Data, sequence analysis, and evolution (2017)
  3. Ye, Yuzhen; Choi, Jeong-Hyeon; Tang, Haixu: Rapsearch: a fast protein similarity search tool for short reads (2011) ioport