MoleMap: fast alignment-free molecule mapping for long-read and linked-read sequencing data

Abstract Background With sequencing costs continuing to decline, the computational expenses of read alignment in genome analysis are becoming an increasingly important consideration. When only portions of the genome are of interest – as in most clinical genome analyses – or base-pair precise alignment is not required, performing full read alignment on whole-genome sequencing (WGS) data imposes unnecessary computational costs. Alignment provides the base-by-base precision necessar​y to identify variants relative to a reference sequence. To improve efficiency, sequence reads can first be rapidly mapped to approximate chromosomal locations, allowing expensive alignment to be restricted to specified target regions subsequently. This mapping-first approach avoids performing base-pair precise alignment across the entire genome. Results We introduce MoleMap , a “molecule mapping” method for long reads and linked-read molecules that pre-filters WGS data for downstream targeted analyses. MoleMap uses a minimized open-addressing k -mer index of the reference genome and a fast k -mer clustering procedure to assign reads or molecules to genomic regions. In our evaluation, MoleMap identifies the correct mapping location for a comparable number of reads as other mapping and standard alignment tools on simulated data and achieves high agreement with other tools on real data while running 3 − 8 × faster than other mappers and 10 − 60 × faster than full aligners on 32 threads. In addition, its low memory footprint allows whole-genome processing on a standard laptop computer. As proofs of concept, we use MoleMap to filter reads for local assembly of a known variant region that involves non-reference sequence and showcase its use in diagnosing a patient with a rare disease. Conclusions MoleMap is a practical mapping tool for whole-genome data that accelerates targeted analyses by selecting reads from loci of interest before costly alignment. By identifying locus-specific reads, it decreases the volume of data passed to downstream processing steps. In a clinical sequencing setting, this can substantially reduce computational costs and diagnostic turnaround times.