muBLASTP: database-indexed protein sequence search on multicore CPUs

Abstract

BackgroundThe Basic Local Alignment Search Tool (BLAST) is a fundamental program in the life sciences that searches databases for sequences that are most similar to a query sequence. Currently, the BLAST algorithm utilizes a query-indexed approach. Although many approaches suggest that sequence search with a database index can achieve much higher throughput (e.g., BLAT, SSAHA, and CAFE), they cannot deliver the same level of sensitivity as the query-indexed BLAST, i.e., NCBI BLAST, or they can only support nucleotide sequence search, e.g., MegaBLAST. Due to different challenges and characteristics between query indexing and database indexing, the existing techniques for query-indexed search cannot be used into database indexed search.ResultsmuBLASTP, a novel database-indexed BLAST for protein sequence search, delivers identical hits returned to NCBI BLAST. On Intel Haswell multicore CPUs, for a single query, the single-threaded muBLASTP achieves up to a 4.41-fold speedup for alignment stages, and up to a 1.75-fold end-to-end speedup over single-threaded NCBI BLAST. For a batch of queries, the multithreaded muBLASTP achieves up to a 5.7-fold speedups for alignment stages, and up to a 4.56-fold end-to-end speedup over multithreaded NCBI BLAST.ConclusionsWith a newly designed index structure for protein database and associated optimizations in BLASTP algorithm, we re-factored BLASTP algorithm for modern multicore processors that achieves much higher throughput with acceptable memory footprint for the database index.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1302-4) contains supplementary material, which is available to authorized users.

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“…This process is used because alignments of interest are those whose score is greater than or equal to a minimum, and thus looking for such matches is similar to looking for them in an index. The techniques used to represent sequences and to create indexes depend on each tool used, but the most precise ones (like MegaBLAST) divide every sequence into k-mers, and store them in some type of index [6,38].…”

iBLAST: Incremental BLAST of new sequences via automated e-value correction

“…Given expanding amount of data, providing fast and biologically valuable sequence alignment tools via high-performance computing (HPC) and algorithmic innovations has been a highly active area of bioinformatics research, particularly in the context of rapidly expanding databases. For example, several sequence alignment programs have relied on contributing algorithmic improvements (e.g., HMMER [4], DIAMOND [5], CaBLAST [6]) while others have focused on improving parallelization to take advantage of emerging high-performance computing (HPC) platforms and programming paradigms (e.g., cuBLASTP [7], muBLASTP [8], mpiBLAST [9], SparkBLAST [10], and SparkLeBLAST [11]). Both DIAMOND [5] and CaBLAST [6] improve the execution time of sequence alignment by compressing the sequence database.…”

Algorithm optimization for weighted gene co-expression network analysis: accelerating the calculation of Topology Overlap Matrices with OpenMP and SQLite

“…If single-threaded algorithms can be changed to multi-threaded algorithms, it will be extremely improve the calculation speed. For example, the single-threaded algorithm of sequence comparison has been changed to the multi-threaded algorithm (M et al, 2017), and the algorithm of protein sequence search has been changed to the multi-threaded algorithm (Zhang et al, 2016).…”