Location: Genomics and Bioinformatics Research2013 Annual Report
1a. Objectives (from AD-416):
The objective of this project is to utilize computational biology and comparative genomics approaches to explore genes/alleles involved in resistance/susceptibility of cotton species/cultivars to cotton leaf curl virus (CLCuV). The project will leverage the complementary strengths of the USDA ARS Genomics and Bioinformatics Research Unit (GBRU) and Mississippi State University’s Institute for Genomics, Biocomputing & Biotechnology (IGBB).
1b. Approach (from AD-416):
An Biocomputing & Biotechnology (IGBB) postdoctoral associate will use data mining and in silico comparative biomolecule approaches to identify and characterize genes and/or gene regions involved in cotton leaf curl virus (CLCuV) resistance/susceptibility. Data from a wide array of sources will be analyzed and new sequence, molecular mapping, and phenotypic information generated at the Genomics and Bioinformatics Research Unit (GBRU), the IGBB, and elsewhere will be integrated into the computational analyses as it becomes available. The postdoctoral associate will work closely with both GBRU and IGBB research groups and their directors. The postdoctoral associate will utilize Mississippi State University’s supercomputing facility (i.e., the High Performance Computing Collaboratory) to conduct computationally intensive analyses. All of these activities will be considered true collaborations and thus, by definition, each party will be considered to have provided a true intellectual contribution consistent with authorship.
3. Progress Report:
The funding for this project originates from project 6402-21310-003-22R where additional details can also be found. Genomic analysis, including bioinformatic analysis, is being performed under the overall project to see if any genes can be associated with resistance to cotton leaf curl virus. Other partners are screening germplasm for resistance and these accessions are being submitted for genomic analysis using whole genome sequencing and a technique called RNASeq which measures gene expression.