Introducing difference recurrence relations for faster semi-global alignment of long sequences

• Published in: BMC bioinformatics Vol. 19; no. Suppl 1; p. 45
• Main Authors: Suzuki, Hajime, Kasahara, Masahiro
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 19.02.2018; BioMed Central; BMC
• Subjects: Algorithms; Alignment; Base sequence; Humans; Long read; Methodology; Sequence Alignment - methods; Sequence analysis; Sequence Analysis, DNA - methods; Software; Sequence analysis; Alignment; Long read
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-018-2014-8

The read length of single-molecule DNA sequencers is reaching 1 Mb. Popular alignment software tools widely used for analyzing such long reads often take advantage of single-instruction multiple-data (SIMD) operations to accelerate calculation of dynamic programming (DP) matrices in the Smith-Waterman-Gotoh (SWG) algorithm with a fixed alignment start position at the origin. Nonetheless, 16-bit or 32-bit integers are necessary for storing the values in a DP matrix when sequences to be aligned are long; this situation hampers the use of the full SIMD width of modern processors. We proposed a faster semi-global alignment algorithm, "difference recurrence relations," that runs more rapidly than the state-of-the-art algorithm by a factor of 2.1. Instead of calculating and storing all the values in a DP matrix directly, our algorithm computes and stores mainly the differences between the values of adjacent cells in the matrix. Although the SWG algorithm and our algorithm can output exactly the same result, our algorithm mainly involves 8-bit integer operations, enabling us to exploit the full width of SIMD operations (e.g., 32) on modern processors. We also developed a library, libgaba, so that developers can easily integrate our algorithm into alignment programs. Our novel algorithm and optimized library implementation will facilitate accelerating nucleotide long-read analysis algorithms that use pairwise alignment stages. The library is implemented in the C programming language and available at https://github.com/ocxtal/libgaba .