期刊
GENOME RESEARCH
卷 33, 期 7, 页码 1188-1197出版社
COLD SPRING HARBOR LAB PRESS, PUBLICATIONS DEPT
DOI: 10.1101/gr.277679.123
关键词
-
DNA sequencing data are improving in terms of longer reads and lower error rates. In this paper, a novel strategy called mapquik is introduced, which creates accurate longer reads by anchoring alignments through matches of consecutively sampled minimizers. Mapquik significantly accelerates the seeding and chaining steps in read mapping, achieving high sensitivity and ultrafast mapping. The results show that mapquik outperforms the state-of-the-art tool minimap2 in terms of speed and accuracy.
DNA sequencing data continue to progress toward 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., Pacific Biosciences [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 ultrafast mapping while retaining high sensitivity. We show that mapquik significantly accelerates the seeding and chaining steps-fundamental bottlenecks to read mapping-for both the human and maize genomes with >96% sensitivity and near-perfect specificity. On the human genome, for both real and simulated reads, mapquik achieves a 37x speedup over the state-of-the-art tool minimap2, and on the maize genome, mapquik achieves a 410x speedup over minimap2, making mapquik the fastest mapper to date. These accelerations are enabled from not only minimizer-space seeding but also a novel heuristic O(n) pseudochaining algorithm, which improves upon the long-standing O(nlogn) bound. Minimizer-space computation builds the foundation for achieving real-time analysis of long-read sequencing data.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据