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  Machine Learning Algorithms for Polymorphism Detection

Zeller, G., Schweikert, G., Clark, R., Ossowski, S., Shin, P., Frazer, K., et al. (2007). Machine Learning Algorithms for Polymorphism Detection. Talk presented at NIPS 2007 Workshop on Machine Learning in Computational Biology (MLCB 2007). Whistler, Canada. 2007-12-07 - 2007-12-08.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-CAFB-0 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-4473-E
Genre: Talk

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 Creators:
Zeller, G1, 2, Author              
Schweikert, G1, 2, Author              
Clark, R, Author
Ossowski, S, Author
Shin, P, Author
Frazer, K, Author
Ecker, J, Author
Weigel, D3, Author
Schölkopf, B1, 2, Author              
Rätsch, G4, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
3Max Planck Institute for Developmental Biology, Max Planck Society, Max-Planck-Ring 5, 72076 Tübingen, DE, ou_2421691              
4Friedrich Miescher Laboratory, Max Planck Society, Max-Planck-Ring 9, 72076 Tübingen, DE, ou_2575692              

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 Abstract: As extensive studies of natural variation require the identification of sequence differences among complete genomes, there exists a high demand for precise high-throughput sequencing techniques. While high-density oligo-nucleotide arrays are capable of rapid and comparatively cheap genomic scans, the resulting data is typically much noisier than dideoxy sequencing data. Therefore algorithmic approaches for the accurate identification of sequence polymorphisms from oligo-nucleotide array data remain a challenge [Gresham et al., 2006]. We present machine learning based methods tackling the problem of identifying Single Nucleotide Polymorphisms (SNPs) as well as deletions and highly polymorphic regions. Here we describe polymorphism discovery in 20 wild strains of the model plant Arabidopsis thaliana, which has a genome of about 125 Mb. A huge set of array hybridization data comprising nearly 19.2 billion measurements has been collected at Perlegen Sciences Inc. (four 25 nt probes for each base on each genomic strand and strain.

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 Dates: 2007-12
 Publication Status: Published online
 Pages: -
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 Rev. Method: -
 Identifiers: BibTex Citekey: 5402
 Degree: -

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Title: NIPS 2007 Workshop on Machine Learning in Computational Biology (MLCB 2007)
Place of Event: Whistler, Canada
Start-/End Date: 2007-12-07 - 2007-12-08
Invited: Yes

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