Kmer2SNP: reference-free SNP calling from raw reads based on matching

Abstract

The development of DNA sequencing technologies provides the opportunity to call heterozygous SNPs for each individual. SNP calling is a fundamental problem of genetic analysis and has many applications, such as gene-disease diagnosis, drug design, and ancestry inference. Reference-based SNP calling approaches generate highly accurate results, but they face serious limitations especially when high-quality reference genomes are not available for many species. Although reference-free approaches have the potential to call SNPs without using the reference genome, they have not been widely applied on large and complex genomes because existing approaches suffer from low recall/precision or high runtime. We develop a reference-free algorithm Kmer2SNP to call SNP directly from raw reads. Kmer2SNP first computes the k-mer frequency distribution from reads and identifies potential heterozygous k-mers which only appear in one haplotype. Kmer2SNP then constructs a graph by choosing these heterozygous k-mers as vertices and connecting edges between pairs of heterozygous k-mers that might correspond to SNPs. Kmer2SNP further assigns a weight to each edge using overlapping information between heterozygous k-mers, computes a maximum weight matching and finally outputs SNPs as edges between k-mer pairs in the matching. We benchmark Kmer2SNP against reference-free methods including hybrid (assembly-based) and assembly-free methods on both simulated and real datasets. Experimental results show that Kmer2SNP achieves better SNP calling quality while being an order of magnitude faster than the state-of-the-art methods. Kmer2SNP shows the potential of calling SNPs only using k-mers from raw reads without assembly. The source code is freely available at https://github.com/yanboANU/Kmer2SNP.