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• W4385563365 abstract "Abstract Motivation Efficiently aligning sequences is a fundamental problem in bioinformatics. Many recent algorithms for computing alignments through Smith–Waterman–Gotoh dynamic programming (DP) exploit Single Instruction Multiple Data (SIMD) operations on modern CPUs for speed. However, these advances have largely ignored difficulties associated with efficiently handling complex scoring matrices or large gaps (insertions or deletions). Results We propose a new SIMD-accelerated algorithm called Block Aligner for aligning nucleotide and protein sequences against other sequences or position-specific scoring matrices. We introduce a new paradigm that uses blocks in the DP matrix that greedily shift, grow, and shrink. This approach allows regions of the DP matrix to be adaptively computed. Our algorithm reaches over 5–10 times faster than some previous methods while incurring an error rate of less than 3% on protein and long read datasets, despite large gaps and low sequence identities. Availability and implementation Our algorithm is implemented for global, local, and X-drop alignments. It is available as a Rust library (with C bindings) at https://github.com/Daniel-Liu-c0deb0t/block-aligner." @default.
• W4385563365 created "2023-08-05" @default.
• W4385563365 creator A5019985343 @default.
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• W4385563365 date "2023-08-01" @default.
• W4385563365 modified "2023-10-14" @default.
• W4385563365 title "Block Aligner: an adaptive SIMD-accelerated aligner for sequences and position-specific scoring matrices" @default.
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• W4385563365 doi "https://doi.org/10.1093/bioinformatics/btad487" @default.
• W4385563365 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37535681" @default.
• W4385563365 hasPublicationYear "2023" @default.
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