Assessing graph-based read mappers against a novel baseline approach highlights strengths and weaknesses of the current generation of methods

Abstract

Graph-based reference genomes have become popular as they allow read mapping and follow-up analyses in settings where the exact haplotypes underlying a high-throughput sequencing experiment are not precisely known. Two recent papers show that mapping to graph-based reference genomes can improve accuracy as compared to methods using linear references. Both of these methods index the sequences for most paths up to a certain length in the graph in order to enable direct mapping of reads containing common variants. However, the combinatorial explosion of possible paths through nearby variants also leads to a huge search space and an increased chance of false positive alignments to highly variable regions.We here assess three prominent graph-based read mappers against a novel hybrid baseline approach that combines an initial path determination with a tuned linear read mapping method. We show, using a previously proposed benchmark, that this simple approach is able to improve accuracy of read-mapping to graph-based reference genomes.Our method is implemented in a tool Two-step Graph Mapper, which is available at https://github.com/uio-bmi/two_step_graph_mapper along with data and scripts for reproducing the experiments.

Bovine breed-specific augmented reference graphs facilitate accurate sequence read mapping and unbiased variant discovery

“…The use of variation-aware references primarily improves the mapping of reads that contain nonreference nucleotides. This agrees with the findings of Garrison et al and Grytten et al [22,34].…”

“…We simulated sequencing reads from haplotypes of BSW, FV, OBV and HOL cattle to obtain realistic and breed-specific base pair substitution and Indel rates. In agreement with previous studies [22,34], read mapping was more accurate using variation-aware than linear reference sequences. Grytten et al [34] observed that an adjusted parameter setting of BWA mem improves linear mapping.…”

“…In agreement with previous studies [22,34], read mapping was more accurate using variation-aware than linear reference sequences. Grytten et al [34] observed that an adjusted parameter setting of BWA mem improves linear mapping. Because we compared graph-based mapping to the most-widely used linear mapping approach, we did not tune BWA mem.…”

“…We constructed bovine variation-aware reference graphs using a Hereford-based linear reference sequence as backbone and variants that were filtered for allele frequency in four cattle breeds. Using both simulated and real short read data, our findings corroborate that a variation-aware reference facilitates accurate read mapping and unbiased sequence variant genotyping [22,23,[32][33][34].…”

“…The number of sequencing reads that aligned to the BSW-specific whole-genome graph with full identity increased considerably (+13%) over the linear reference sequence at the cost of a slightly reduced (-0.72%) number of unique alignments. A two-step alignment approach that exploits a refined search space might reduce the number of multiple mappings in dense variation-aware graphs [34]. Compared to a human whole-genome graph, the improvement in perfect mapping over the linear reference was slightly larger in our bovine whole genome graph (9.2%) [22].…”