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Discovering Common Sequence Variation in Arabidopsis thaliana

Discovering Common Sequence Variation in Arabidopsis thaliana

This video was recorded at 1st International Workshop on Machine Learning in Systems Biology (MLSB), Evry 2007. In order to characterize natural sequence variation in 20 strains of the model plant Arabidopsis thaliana, whole-genome resequencing with high-density oligonucleotide arrays was performed in collaboration with Perlegen Sciences Inc. Array data were analyzed with a combination of existing model-based (MB; Hinds et al., Science, 2005) and novel machine learning (ML) methods. For the identification of single nucleotide polymorphisms (SNPs) we developed an algorithm based on support vector machines. Training and evaluation was done on published alignments (Nordborg et al., PLoS Biology, 2005). At the same false discovery rates (FDR) as MB, the ML algorithm identifies significantly... Show More
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