Application of machine learning in SNP discovery

Background: Single nucleotide polymorphisms (SNP) constitute more than 90% of the genetic variation, and hence can account for most trait differences among individuals in a given species. Polymorphism detection software PolyBayes and PolyPhred give high false positive SNP predictions even with stringent parameter values. We developed a machine learning (ML) method to augment PolyBayes to improve its prediction accuracy. ML methods have also been successfully applied to other bioinformatics problems in predicting genes, promoters, transcription factor binding sites and protein structures. Results: The ML program C4.5 was applied to a set of features in order to build a SNP classifier from training data based on human expert decisions (True/False). The training data were 27,275 candidate SNP generated by sequencing 1973 STS (sequence tag sites) (12 Mb) in both directions from 6 diverse homozygous soybean cultivars and PolyBayes analysis. Test data of 18,390 candidate SNP were generated ...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Lakshmi K. Matukumalli, John J. Grefenstette, David L. Hyten, Ik-Young Choi, Perry B. Cregan, Curtis P. Van Tassell
• Keywords: BMCBI 2006, Candidate Snp, Polymorphism Detection Software, SNP Predictions