de.mpg.escidoc.pubman.appbase.FacesBean
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Buchkapitel

Context-Specific Independence Mixture Modelling for Protein Families.

MPG-Autoren

Georgi,  Benjamin
Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50523

Schliep,  Alexander
Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Zitation

Georgi, B., Schultz, J., & Schliep, A. (2007). Context-Specific Independence Mixture Modelling for Protein Families. In J. Kok, J. Koronacki, R. Lopez de Mantaras, S. Matwin, D. Mladenic, & A. Skowron (Eds.), Knowledge Discovery in Databases: PKDD 2007 (pp. 79-90). Berlin/Heidelberg: Springer.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0010-817C-D
Zusammenfassung
Protein families can be divided into subgroups with functional differences. The analysis of these subgroups and the determination of which residues convey substrate specificity is a central question in the study of these families. We present a clustering procedure using the context-specific independence mixture framework using a Dirichlet mixture prior for simultaneous inference of subgroups and prediction of specificity determining residues based on multiple sequence alignments of protein families. Application of the method on several well studied families revealed a good clustering performance and ample biological support for the predicted positions. The software we developed to carry out this analysis PyMix - the Python mixture package is available from http://www.algorithmics.molgen.mpg.de/pymix.html.