Heap: a highly sensitive and accurate SNP detection tool for low-coverage high-throughput sequencing data

Abstract

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, which must be aligned to the reference genome sequences in advance. To reduce false positive SNPs, Heap determines genotypes and calls SNPs at each site except for sites at the both ends of reads or containing a minor allele supported by only one read. Performance comparison with existing tools showed that Heap achieved the highest F-scores with low coverage (7X) restriction-site associated DNA sequencing reads of sorghum and rice individuals. This will facilitate cost-effective GWAS and GP studies in this NGS era. Code and documentation of Heap are freely available from https://github.com/meiji-bioinf/heap (29 March 2017, date last accessed) and our web site (http://bioinf.mind.meiji.ac.jp/lab/en/tools.html (29 March 2017, date last accessed)).

Comparison of shape quantification methods for genomic prediction, and genome-wide association study of sorghum seed morphology

“…A reference panel of 65 inbred lines with whole-genome sequence data was used to impute the missing marker genotypes of the remaining 404 inbred lines for the 127,587 SNPs. The genome sequences of two lines were newly determined by the authors and those of the other 63 inbred lines by Kobayashi et al [23], Mace et al [24], and Zheng et al [25]. BEAGLE ver.…”

A Fast and Scalable Workflow for SNPs Detection in Genome Sequences Using Hadoop Map-Reduce

“…Here, multiple instances are created to perform SNPs calling. Heap is an accurate and highly sensitive SNP detection tool for high throughput sequencing data and offers equally dependable SNPs with distinct locus to genomic prediction (GP) and genome-wide association studies (GWAS) [8]. Heap performs the read filtering in order to obtain a high-quality score based on Phred-scale as shown in Equations (1) and (2).…”

“…Similarly, on the other hand, if an SNP is not detected, then it has also two categories i.e., true negative (TN) or false negative (FN). In [8], true detection of SNPs is based on sensitivity, positive predictive value (PPV), F-score, and accuracy. With the use of efficient SNPs detection algorithmic solution, the rate of TP and TN helps to increase the F-score and accuracy rate.…”

“…It is investigated that there is a thoughtful issue of computation slant in genomic data, i.e., the size of the input file is the same while processing time of variant calling is still significantly different [4]. Single nucleotide polymorphism (SNP) is a variant of a single nucleotide which exists at a particular locus in the genome, where respective variant exists up-to noticeable degree in a population of a residence [5][6][7][8]. SNP is a genetic variation triggered by the alteration of a single nucleotide base i.e., A, C, G, and T in DNA sequences [9].…”

“…Moreover, mining of SNPs is also introduced in the proposed workflow. The results obtained are compared with state-of-the-art algorithms i.e., GATK [32], FaSD [22], Halvade [33], SparkGA [38], and Heap [8] algorithms.…”