Current Research in Bioinformatics

In Silico Prediction and Functional Characterization of Genes Related to Abiotic and Biotic Stresses in Chickpea (Cicer arietinum)

Sukhdeep Kaur, Satendra Singh, Gitanjali Tandon, Sarika Jaiswal, Mir Asif Iquebal, Anil Rai and Dinesh Kumar

Current Research in Bioinformatics

Abstract

Chickpea (Cicer arietinum L.) is second largest grown legumes worldwide contributing 75% of total pulse production. It is a cool season legume crop and grown in tropical and subtropical areas. Due to drastic climatic changes, chickpea suffers from many biotic (blight and wilt) and abiotic (salinity, drought, cold) stresses that directly impact the growth and yield. In our study, we predicted and annotated the genes related to biotic and abiotic stresses. Total 20162 ESTs for salinity, 34346 for drought and 191 for cold stress were downloaded. For biotic stresses, viz., wilt and blight disease, 7866 and 56 ESTs were collected, respectively from public domain. All these ESTs were assembled into contigs and blast against protein non-redundant database. Each blast results were mapped to get the corresponding GO terms. Total 1631, 3133 and 13 contigs for salinity, drought and cold stress showed 1333, 2693 and 7 GO terms respectively, while 1144 contigs for Fusarium wilt and 6 contigs for Ascochyta blight disease showed 955 and 4 GO terms. These GO terms describe biological process, molecular function and cellular components of corresponding stresses. Remaining 298 (salinity), 440 (drought), 6 (cold), 189 (wilt) and 2 (blight) contigs were mapped to reference genome and further used for annotation using gene prediction methods and promoter analysis. This study provide insight to novel gene related to abiotic and biotic stress mechanism that can be further analyzed in molecular biology studies for breeding programs.

Copyright

© 2017 Sukhdeep Kaur, Satendra Singh, Gitanjali Tandon, Sarika Jaiswal, Mir Asif Iquebal, Anil Rai and Dinesh Kumar. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.