mapAlign: An Efficient Approach for Mapping and Aligning Long Reads to Reference Genomes

Long reads play an important role for the identification of structural variants, sequencing repetitive regions, phasing of alleles, etc. In this paper, we propose a new approach for mapping long reads to reference genomes. We also propose a new method to generate accurate alignments of the long reads and the corresponding segments of reference genome. The new mapping algorithm is based on the longest common sub-sequence with distance constraints. The new (local) alignment algorithms is based on the idea of recursive alignment of variable size k-mers. Experiments show that our new method can generate better alignments in terms of both identity and alignment scores for both Nanopore and SMRT data sets. In particular, our method can align 91.53% and \(85.36\%\) of letters on reads to identical letters on reference genomes for human individuals of Nanopore and SMRT data sets, respectively. The state-of-the-art 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 state-of-the-art method.