B Ekim. mapquik: Efficient low-divergence mapping of long reads in minimizer space скачать в хорошем качестве

B Ekim. mapquik: Efficient low-divergence mapping of long reads in minimizer space

"mapquik: Efficient low-divergence mapping of long reads in minimizer space" by Baris Ekim, Kristoffer Sahlin, Paul Medvedev, Bonnie Berger and Rayan Chikhi Abstract: DNA sequencing data continues to progress towards longer reads with increasingly lower sequencing error rates. We focus on the critical problem of mapping, or aligning, low-divergence sequences from long reads (e.g. PacBio HiFi) to a reference genome, which poses challenges in terms of accuracy and computational resources when using cutting-edge read mapping approaches that are designed for all types of alignments. A natural idea would be to optimize efficiency with longer seeds to reduce the probability of extraneous matches; however, contiguous exact seeds quickly reach a sensitivity limit. We introduce mapquik, a novel strategy that creates accurate longer seeds by anchoring alignments through matches of k consecutively-sampled minimizers (k-min-mers) and only indexing k-min-mers that occur once in the reference genome, thereby unlocking ultra-fast mapping while retaining high sensitivity. Figure 1 gives an overview of the algorithmic pipeline of mapquik, compared with those of the state-of-the-art methods that use minimizers as seeds. We demonstrate that mapquik significantly accelerates the seeding and chaining steps—fundamental bottlenecks to read mapping—for both the human and maize genomes with greater than 96% sensitivity and near-perfect specificity. On the human genome, for both simulated and real HiFi reads, mapquik achieves a 30× speed-up over the state-of-the-art tool minimap2, and on the maize genome, a 350× speed-up over minimap2, making mapquik the fastest mapper to date. These accelerations are enabled not only by minimizer-space seeding but also a novel heuristic O(n) pseudo-chaining algorithm, which improves over the long-standing O(n log n) bound. Minimizer-space computation builds the foundation for achieving real-time analysis of long-read sequencing data