Generic accelerated sequence alignment in SeqAn using vectorization and multi-threading

Abstract

Supplementary data are available at Bioinformatics online.

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

“…We measured performance using GCUPS (giga cell updates per second), which is the number of cells in the dynamic programming matrix computed per second. This standard metric used extensively in the literature normalizes performance to enable comparisons between experiments conducted using reads and references of different lengths (Rahn et al, 2018;Daily, 2016;Jain et al, 2019;Liu and Schmidt, 2014;Liu et al, 2013).…”

“…For a particular row and column position in the matrix, SIMD instructions are used to compute the value for 8-64 queries simultaneously, depending on the vector capacity. The query-parallel strategy has been used for alignment to linear references in SeqAn (Rahn et al, 2018) and to directed acyclic graphs in PaSGAL (Jain et al, 2019).…”

“…When the chromosome 19 graph contained all 1000 Genomes Phase 3 SNPs and indels (5.1 million nodes and 3.66% increase in linearized genome size) we observed 237 GCUPS with the same configuration. When aligning 150bp reads to chromosome 10, SeqAn (Rahn et al, 2018) reported 420 GCUPS using 40 threads on SKX with AVX512BW; on the same dataset, Parasail (Daily, 2016) recorded 74 GCUPS, but used AVX2 instructions that offer only half the throughput per instruction. PaSGAL (Jain et al, 2019), reported 317 GCUPS with 48 threads on SKX with AVX512 instructions when aligning 100bp reads to a graph genome of the 1Mbp Leukocyte Receptor Complex locus including all variants from 1000 Genomes Project Phase 3, not including traceback.…”

“…Rognes (2011) proposed a query-parallel strategy that fills the same element (same row and column) in several dynamic programming matrices at once. This has been shown to be efficient and practical in prior studies (Rahn et al, 2018;Jain et al, 2019). We adopt this approach in Vargas and provide further evidence that it is efficient on modern general-purpose architectures for both linear and graph alignment.…”

Dashing: Fast and Accurate Genomic Distances with HyperLogLog

“…It uses OpenMP for multithreading, with both the sketching and distance phases readily scaling to 100 simultaneous threads. It also uses data-parallel SIMD instructions, including the recent AVX512-BW extensions that have been effective at accelerating other bioinformatics software [22]. Dashing has Python bindings that enable other developers to use the HLL implementation.…”