101. Vargas: heuristic-free alignment for assessing linear and graph read aligners
- Author
-
Ben Langmead, Charlotte A. Darby, Ravi Gaddipati, and Michael C. Schatz
- Subjects
Statistics and Probability ,Computer science ,Sequence analysis ,Genomics ,Biochemistry ,Genome ,03 medical and health sciences ,chemistry.chemical_compound ,0302 clinical medicine ,Heuristics ,Molecular Biology ,030304 developmental biology ,Smith–Waterman algorithm ,0303 health sciences ,High-Throughput Nucleotide Sequencing ,Sequence Analysis, DNA ,Original Papers ,Computer Science Applications ,Computational Mathematics ,Computational Theory and Mathematics ,chemistry ,Graph (abstract data type) ,Optimal alignment ,Sequence Analysis ,Algorithm ,Algorithms ,Software ,030217 neurology & neurosurgery ,DNA - Abstract
Motivation 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. Results Vargas implements a heuristic-free algorithm guaranteed to find the highest-scoring alignment for real sequencing reads to a linear or graph genome. With semiglobal and local alignment modes and affine gap and quality-scaled mismatch penalties, it can implement the scoring functions of commonly used aligners to calculate optimal alignments. While this is computationally intensive, Vargas uses multi-core parallelization and vectorized (SIMD) instructions to make it practical to optimally align large numbers of reads, achieving a maximum speed of 456 billion cell updates per second. We demonstrate how these ‘gold standard’ Vargas alignments can be used to improve heuristic alignment accuracy by optimizing command-line parameters in Bowtie 2, BWA-maximal exact match and vg to align more reads correctly. Availability and implementation Source code implemented in C++ and compiled binary releases are available at https://github.com/langmead-lab/vargas under the MIT license. Supplementary information Supplementary data are available at Bioinformatics online.
- Published
- 2020