Heap: A Highly Sensitive and Accurate SNP Calling Tool with Low-Coverage High-Throughput Sequencing Data

Recent availability of large scale genomic resources enables us to conduct so called genome-wide association studies (GWAS) and genomic prediction (GP) studies, particularly with next-generation sequencing (NGS) data. The effectiveness of GWAS and GP depends on not only their mathematical models, but the quality and quantity of variants employed in the analysis. In NGS single nucleotide polymorphism (SNP) calling, conventional tools ideally require more reads for higher SNP sensitivity and accuracy. In this study, we aimed to develop a tool, 'Heap', that enables robustly sensitive and accurate calling of SNPs, particularly with a low coverage NGS data. Heap calls SNPs from NGS reads, which are mapped to the reference genome sequences in advance. Performance comparison with existing tools showed that Heap achieved the highest F-scores with low coverage (7X) restriction-site associated DNA (RAD) sequencing reads of sorghum and rice individuals. This will facilitate cost-effective GWAS and GP studies in this NGS era. Heap code and documentation are freely available from https://github.com/meiji-bioinf/heap and our web site (http://bioinf.mind.meiji.ac.jp/lab/en/tools.html).
This research was partially supported by Research Funding for Computational Software Supporting Program from Meiji Univ. to K.Y.. Computations were partially performed on the NIG (National Institute of Genetics) SuperComputer Facilities hosted at NIG/ROIS (Research Organization of Information and Systems).