Revealing sequence variation patterns in rice with machine learning methods

Motivation The major breakthrough at the turn of the millennium was the completion of genome sequences for individuals from many species, including human, worm and rice. More recently, it has also been important to describe sequence variation within one species, providing the first step towards the linkage of genetic variation to traits. Today, rice is the most important source for human caloric intake, making up 20% of the calorie supply and feeding millions of people daily. The more detailed understanding and findings on the molecular assembly of phenotypic rice varieties will therefore be essential for future improvement in rice cultivation and breeding. In order to reveal patterns of sequence variation in Oryza sativa (rice), the non-repetitive portion of the genomes of 20 diverse rice cultivars was resequenced, in collaboration with Perlegen Sciences, Inc., using a high-density oligonucleotide microarray technology. Methods Based on experience gained in polymorphism studies for Arabidopsis thaliana [1] we developed a method for identifying single nucleotide polymorphisms (SNPs) from the array data using Support Vector Machines (SVMs). In a two-layered approach we trained SVMs to discriminate between SNP and non-SNP positions using information from each cultivar and, in a second step, across all cultivars. Wherever several SNPs or deletion/insertion polymorphisms occur in close vicinity, the hybridisation is suppressed and SNP calling in these regions becomes infeasible. We therefore adapted a machine learning method for sequence segmentation [2, 3] to predict highly polymorphic regions in O. sativa (cf. Figure 1). These regions can then be analysed in more detail using alternative experimental techniques