2020/08/03 by Mohammed Alser, Zulal Bingol, Zülal Bingöl +5 · 83 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Acceleration #Algorithms and Data Compression #Bottleneck #Exploit #Genome #Genome Rearrangement Algorithms #Genomics and Phylogenetic Studies #Human genome #Hybrid genome assembly #Reference genome #cs.AR #q-bio.GN #stat.CO
paper · pdf · doi:10.1109/mm.2020.3013728
published in IEEE Micro 40(5), 65-75 (Institute of Electrical and Electronics Engineers) · This is an extended and updated version of a paper published in IEEE Micro, vol. 40, no. 5, pp. 65-75, 1 Sept.-Oct. 2020, https://doi.org/10.1109/MM.2020.3013728
openalex publication_date 2020/08/03 · crossref created 2020/08/03 · openalex created_date 2020/08/07 · crossref issued 2020/09/01 · crossref published 2020/09/01 · crossref published-print 2020/09/01 · arxiv created 2020/09/22 · arxiv updated 2020/10/29 · crossref deposited 2022/05/04 · openalex updated_date 2026/08/05 · crossref indexed 2026/08/05
Genome analysis fundamentally starts with a process known as read mapping, where sequenced fragments of an organism's genome are compared against a reference genome. Read mapping is currently a major bottleneck in the entire genome analysis pipeline, because state-of-the-art genome sequencing technologies are able to sequence a genome much faster than the computational techniques employed to analyze the genome. We describe the ongoing journey in significantly improving the performance of read mapping. We explain state-of-the-art algorithmic methods and hardware-based acceleration approaches. Algorithmic approaches exploit the structure of the genome as well as the structure of the underlying hardware. Hardware-based acceleration approaches exploit specialized microarchitectures or various execution paradigms (e.g., processing inside or near memory). We conclude with the challenges of adopting these hardware-accelerated read mappers.