This Article ACCEPTED PREPRINT OPEN ACCESS ARTICLE Full Text (PDF) Supplemental Research Data All Versions of this Article: gr.070169.107v1 gr.070169.107v2    most recent Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Google Scholar Articles by Zeller, G. Articles by Ratsch, G. PubMed PubMed Citation Articles by Zeller, G. Articles by Ratsch, G. Social Bookmarking                   What's this?

Methods and Resources

Detecting Polymorphic Regions in the Arabidopsis thaliana Genome with Resequencing Microarrays

¹ Friedrich Miescher Laboratory of the Max Planck Society; ² Max Planck Institute for Developmental Biology

Whole-genome, oligonucleotide resequencing arrays have allowed the comprehensive discovery of single nucleotide polymorphisms (SNPs) in eukaryotic genomes of moderate to large size. With this technology, the detection rate for isolated SNPs is typically high. However, it is greatly reduced when other polymorphisms are located near a SNP as multiple mismatches inhibit hybridization to arrayed oligonucleotides. Contiguous tracts of suppressed hybridization therefore typify polymorphic regions such as clusters of SNPs or deletions. We developed a machine learning method, designated margin-based Prediction of Polymorphic Regions (mPPR), to predict Polymorphic Regions (PRs) from resequencing array data. Conceptually similar to Hidden Markov Models, the method is trained with discriminative learning techniques related to Support Vector Machines, and accurately identifies even very short polymorphic tracts (<10 bp). We applied this method to resequencing array data previously generated for the euchromatic genomes of 20 strains (accessions) of the best characterized plant, Arabidopsis thaliana. Non-redundantly, 27% of the genome was included within the boundaries of PRs predicted at high specificity ( 97%). The resulting data set provides a fine-scale view of polymorphic sequences in A. thaliana; patterns of polymorphism not apparent in SNP data were readily detected, especially for noncoding regions. Our predictions provide a valuable resource for evolutionary genetic and functional studies in A. thaliana, and our method is applicable to similar data sets in other species. More broadly, our computational approach can be applied to other segmentation tasks related to the analysis of genomic variation.

CiteULike    Connotea    Del.icio.us    Digg    Reddit    Technorati    What's this?