lra: A long read aligner for sequences and contigs

Abstract

It is computationally challenging to detect variation by aligning single-molecule sequencing (SMS) reads, or contigs from SMS assemblies. One approach to efficiently align SMS reads is sparse dynamic programming (SDP), where optimal chains of exact matches are found between the sequence and the genome. While straightforward implementations of SDP penalize gaps with a cost that is a linear function of gap length, biological variation is more accurately represented when gap cost is a concave function of gap length. We have developed a method, lra, that uses SDP with a concave-cost gap penalty, and used lra to align long-read sequences from PacBio and Oxford Nanopore (ONT) instruments as well as de novo assembly contigs. This alignment approach increases sensitivity and specificity for SV discovery, particularly for variants above 1kb and when discovering variation from ONT reads, while having runtime that are comparable (1.05-3.76×) to current methods. When applied to calling variation from de novo assembly contigs, there is a 3.2% increase in Truvari F1 score compared to minimap2+htsbox. lra is available in bioconda ( https://anaconda.org/bioconda/lra) and github ( https://github.com/ChaissonLab/LRA).

Author summary

Any two human genomes will have sequence differences across multiple scales: from single-nucleotide variants to large gains, losses, or rearrangements of DNA called structural variants. Long-read single-molecule sequencing has been shown to help discover structural variation because the reads span across the entire variant. The computational problem for discovering a structural variant is to find the optimal alignment of the read to the genome with gaps that accurately reflect the variant. Here we demonstrate a method, lra, that uses an efficient implementation of concave-cost alignment for structural variant discovery using long reads. On standardized benchmark data, we show that structural variant discovery is improved for multiple combinations of variant detection algorithms and long-read sequence using alignments generated by lra compared to existing methods. Finally, we show that it is possible to use lra to accurately discover a complete spectrum of structural variants using de novo assemblies constructed from long-read sequence data. This implies a future model of comparative genomics where variants are discovered only by comparing de novo assemblies and not a comparison of reads against a reference.