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Machine Learning Algorithms for Polymorphism Detection

MPS-Authors
/persons/resource/persons229087

Zeller,  G
Friedrich Miescher Laboratory, Max Planck Society;

/persons/resource/persons84204

Schweikert,  G
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

Clark,  R
Max Planck Institute for Developmental Biology, Max Planck Society;

Ossowski,  S
Max Planck Institute for Developmental Biology, Max Planck Society;

Warthmann,  N
Max Planck Institute for Developmental Biology, Max Planck Society;

Weigel,  D
Max Planck Institute for Developmental Biology, Max Planck Society;

/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84153

Rätsch,  G
Friedrich Miescher Laboratory, Max Planck Society;

External Resource

https://www.iscb.org/cms_addon/conferences/ismb2006/archive/ismb2006.cbi.cnptia.embrapa.br/posters_list.php
(Abstract)

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Citation

Zeller, G., Schweikert, G., Clark, R., Ossowski, S., Warthmann, N., Shinn, P., et al. (2006). Machine Learning Algorithms for Polymorphism Detection. Poster presented at 14th International Conference on Intelligent Systems for Molecular Biology (ISMB 2006), Fortaleza, Brazil. Retrieved from http://www.iscbsc.org/scs2.htm.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-D0C3-0
Abstract
Analyzing resequencing array data using machine learning, we obtain a genome-wide inventory of
polymorphisms in 20 wild strains of Arabidopsis thaliana, including 750,000 single nucleotide poly-
morphisms (SNPs) and thousands of highly polymorphic regions and deletions. We thus provide an
unprecedented resource for the study of natural variation in plants.