Vargas: heuristic-free alignment for assessing linear and graph read aligners | Zendy

Charlotte A. Darby | Zendy; Ravi Gaddipati | Zendy; Michael C. Schatz | Zendy; Ben Langmead | Zendy

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Vargas: heuristic-free alignment for assessing linear and graph read aligners

Author(s) -

Charlotte A. Darby,

Ravi Gaddipati,

Michael C. Schatz,

Ben Langmead

Publication year - 2020

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btaa265

Subject(s) - computer science , heuristics , heuristic , affine transformation , simd , algorithm , smith–waterman algorithm , source code , graph , parallel computing , sequence alignment , theoretical computer science , artificial intelligence , mathematics , biochemistry , chemistry , pure mathematics , peptide sequence , gene , operating system

Read alignment is central to many aspects of modern genomics. Most aligners use heuristics to accelerate processing, but these heuristics can fail to find the optimal alignments of reads. Alignment accuracy is typically measured through simulated reads; however, the simulated location may not be the (only) location with the optimal alignment score.

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