Fast and Accurate Algorithms for Mapping and Aligning Long Reads

For DNA sequence analysis, we are facing challenging tasks such as the identification of structural variants, sequencing repetitive regions, and phasing of alleles. Those challenging tasks suffer from the short length of sequencing reads, where each read may cover less than 2 single nucleotide polymorphism (SNP), or less than two occurrences of a repeated region. It is believed that long reads can help to solve those challenging tasks. In this study, we have designed new algorithms for mapping long reads to reference genomes. We have also designed efficient and effective heuristic algorithms for local alignments of long reads against the corresponding segments of the reference genome. To design the new mapping algorithm, we formulate the problem as the longest common subsequence with distance constraints. The local alignment heuristic algorithm is based on the idea of recursive alignment of k-mers, where the size of k differs in each round. We have implemented all the algorithms in C++ and produce a software package named mapAlign. Experiments on real data sets showed that the newly proposed approach can generate better alignments in terms of both identity and alignment scores for both Nanopore and single molecule real time sequencing (SMRT) data sets. For human individuals of both Nanopore and SMRT data sets, the new method can successfully math/align 91.53% and 85.36% of letters from reads to identical letters on reference genomes, respectively. In comparison, the best known method can only align 88.44% and 79.08% letters of reads for Nanopore and SMRT data sets, respectively. Our method is also faster than the best known method.

Automated summary

For DNA sequence analysis, the challenge lies in the short length of sequencing reads, which can be addressed by utilizing long reads. By designing new mapping and local alignment algorithms, this study showed improved alignments for Nanopore and SMRT data sets. The new method successfully aligned a higher percentage of letters from reads to reference genomes, compared to the best known method, while also achieving faster performance.