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Authors: Zhang, Jikai and Lan, Haidong and Chan, Yuandong and Shang, Yuan and Schmidt, Bertil and Liu, Weiguo

Modern bioinformatics tools for analyzing large-scale NGS datasets often need to include fast implementations of core sequence alignment algorithms in order to achieve reasonable execution times. We address this need by presenting the BGSA toolkit for optimized implementations of popular bit-parallel global pairwise alignment algorithms on modern microprocessors.BGSA outperforms Edlib, SeqAn, and BitPAl for pairwise edit distance computations and Parasail, SeqAn, and BitPAl when using more general scoring schemes for pairwise alignments of a batch of sequence reads on both standard multi-core CPUs and Xeon Phi many-core CPUs. Furthermore, banded edit distance performance of BGSA on a Xeon Phi-7210 outperforms the highly optimized NVBio implementation on a Titan X GPU for the seed verification stage of a read mapper by a factor of 4.4.BGSA is open-source and available at https://github.com/sdu-hpcl/BGSA.Supplementary data are available at Bioinformatics online.

Journal: Bioinformatics
DOI: 10.1093/bioinformatics/bty930
Year: 2018

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