HMM-based detection of polymorphic regions in genomes of Arabidopsis ecotypes
Michael Seifert

Abstract:

Arabidopsis thaliana is a model organism in plant biology with a broad geographic distribution including ecotypes from Europe, Asia, and Africa. The natural variation between ecotypes is expected to be reflected to a substantial degree in their genome sequences. To quantify this variation at the DNA level, array comparative genomic hybridization (Array-CGH) has been applied to the ecotypes C24 and Cvi in comparison to the reference genome of ecotype Col.

The focus of this talk is on a Hidden Markov Model (HMM) approach for analyzing the resulting Array-CGH data. Based on that, polymorphic regions in the genomes of ecotypes C24 and Cvi are predicted with respect to the genome of ecotype Col. In addition to this, the HMM approach will be compared to several other methods for analyzing Array-CGH data. To find out what is behind these regions where the genomes of ecotypes differ, the polymorphic regions are further investigated in the context of the genome annotation of ecotype Col. Finally, the detected polymorphic regions are also compared with results coming from resequencing microarrays.

Further information to our initial study can be found here.

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