Coverage-based consensus calling (CbCC) of short sequence reads and comparison of CbCC results to identify SNPs in chickpea (Cicer arietinum; Fabaceae), a crop species without a reference genome

Premise of the study: Next-generation sequencing (NGS) technologies are frequently used for resequencing and mining of single nucleotide polymorphisms (SNPs) by comparison to a reference genome. In crop species such as chickpea (Cicer arietinum) that lack a reference genome sequence, NGS-based SNP d...

Full description

Saved in:
Bibliographic Details
Published inAmerican journal of botany Vol. 99; no. 2; pp. 186 - 192
Main Authors Azam, Sarwar, Thakur, Vivek, Ruperao, Pradeep, Shah, Trushar, Balaji, Jayashree, Amindala, BhanuPrakash, Farmer, Andrew D., Studholme, David J., May, Gregory D., Edwards, David, Jones, Jonathan D. G., Varshney, Rajeev K.
Format Journal Article
LanguageEnglish
Published United States Botanical Society of America 01.02.2012
Botanical Society of America, Inc
Subjects
Online AccessGet full text

Cover

Loading…
More Information
Summary:Premise of the study: Next-generation sequencing (NGS) technologies are frequently used for resequencing and mining of single nucleotide polymorphisms (SNPs) by comparison to a reference genome. In crop species such as chickpea (Cicer arietinum) that lack a reference genome sequence, NGS-based SNP discovery is a challenge. Therefore, unlike probability-based statistical approaches for consensus calling and by comparison with a reference sequence, a coverage-based consensus calling (CbCC) approach was applied and two genotypes were compared for SNP identification. Methods: A CbCC approach is used in this study with four commonly used short read alignment tools (Maq, Bowtie, Novoalign, and SOAP2) and 15.7 and 22.1 million Illumina reads for chickpea genotypes ICC4958 and ICC1882, together with the chickpea trancriptome assembly (CaTA). Key results: A nonredundant set of 4543 SNPs was identified between two chickpea genotypes. Experimental validation of 224 randomly selected SNPs showed superiority of Maq among individual tools, 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. SNP prediction accuracy generally increased with increasing reads depth. Conclusions: This study provides a benchmark comparison of tools as well as read depths for four commonly used tools for NGS SNP discovery in a crop species without a reference genome sequence. In addition, a large number of SNPs have been identified in chickpea that would be useful for molecular breeding.
Bibliography:The authors are grateful to the CGIAR Generation Challenge Program for financial support. Thanks are due to several colleagues, in particular Chris Town and Yongli Xiao from The J. Craig Venter Institute (USA); Jimmy Woodward from NCGR; Doug Cook from University of California‐Davis; Dan MacLean from TSL; Richard Bruskiewich from IRRI; and Abhishek Rathore, Spurthi Nayak, Pavana Hiremath, Roma Rani Das, Neha Gujaria, Ashish Kumar, and Shamshad Alam from ICRISAT for their cooperation and help in generation of some data, analysis, and interpretation of results presented in this study. S.A. and V.T. contributed equally to this study.
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0002-9122
1537-2197
DOI:10.3732/ajb.1100419