EFFICIENT ALLELE MINING IN A MODEL SEQUENCED CROP VIA THE COMPRESSED SEQUENCING APPROACH
2013 Annual Report
1a.Objectives (from AD-416):
1. To validate if compressed sensing (CS) strategy can be used to analyze next generation sequencing data to identify known mutations in the sorghum library.
2. To explore CS strategy for allele mining in a few genes involved in drought tolerance.
1b.Approach (from AD-416):
The Co-PI (Xin) has created a pedigreed collection of 3,400 sorhgum mutant lines displaying many stress response and developmental phonotypes. The PI, Noami Shental, from the Computer Science Department, the Open University of Israel developed the compressed mutations. Pools of genomic DNA from the sorghum mutation library will be sequenced and analyzed with compressed sensing strategy to identify known mutations and new mutations in genes involved in drought tolerance.
In collaboration with scientists from Israel, scientists at Lubbock, Texas, tested a generic approach that enables identifying rare mutations and their carriers in very large cohorts, based on a combination of Next Generation Sequencing technology and the mathematical field of Compressed Sensing. The basic idea is to create a small set of combinatorial pools of different DNA samples and sequence each pool as if these were single DNA samples. We later apply the algorithmic tools of Compressed Sensing in order to detect a set of SNPs and reconstruct the identity of their specific carriers. The approach can identify several carriers at each locus and is highly robust to noise. This approach, dubbed Compressed Sequencing (ComSeq), was experimentally tested for the first time as part of this research, while mining a TILLING population originating from the Sorghum bicolor line Tx623