RT Journal Article
SR Electronic
T1 Vargas: heuristic-free alignment for assessing linear and graph read aligners
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2019.12.20.884676
DO 10.1101/2019.12.20.884676
A1 Charlotte A. Darby
A1 Ravi Gaddipati
A1 Michael C. Schatz
A1 Ben Langmead
YR 2019
UL http://biorxiv.org/content/early/2019/12/21/2019.12.20.884676.abstract
AB 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. 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 437 billion cell updates per second. We demonstrate how these “gold standard” Vargas alignments can be used to optimize heuristic alignment accuracy and improve correctness of difficult ChIP-seq reads by 30% over Bowtie 2’s most sensitive alignment mode. Source code implemented in C++ is available at https://github.com/langmead-lab/vargas under the MIT license.