Coverage-based Consensus Calling (CbCC) Method for Identification of SNPs in Crop Species Without a Reference Genome

Next-generation sequencing (NGS) approach, due to inexpensive and faster in nature, is facilitating identification of SNPs in crop species. While several tools and pipelines are available for SNP discovery in the crop species that have the reference genome, SNP discovery with a higher precision and better efficiency is still challenging in the crop species that do not have the reference genome. In this context, we have developed a coverage-based consensus calling (CbCC) approach. As an example, this approach has been used to identify SNPs between two genotypes (ICC 4958 and ICC 1882) of chickpea (Cicer arietinum), a crop species without a reference genome. Four open source  short read alignment tools (Bowtie, Maq, NovoAlign, and SOAP2) were used on 15.7 and 22.1 million Illumina reads (36 bp long) from ICC4958 and ICC1882, respectively onto a transcriptome assembly of chickpea with 46,740 transcriptome assembly contigs (TACs). By using CbCC approach on above-mentioned tools, a non-redundant set of 4543 SNPs of which 224 SNPs were randomly selected for experimental validation. By comparing the in silico and wet lab results, Maq in comparison to other individual tools, showed superiority as 50.0% of SNPs predicted by Maq were true SNPs. For combinations of two tools, greatest accuracy (55.7%) was reported for Maq and Bowtie, with a combination of Bowtie, Maq, and Novoalign identifying 61.5% true SNPs. In summary, this study provides a benchmark of comparison of tools as well as read depths for four commonly used tools for SNP discovery in a non-reference genome species.