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Book Chapter
Context-Specific Independence Mixture Modelling for Protein Families
Book Series
Lecture Notes in Computer Science
Publisher
Springer Berlin / Heidelberg
ISSN
0302-9743 (Print) 1611-3349 (Online)
Volume
Volume 4702/2007
Book
Knowledge Discovery in Databases: PKDD 2007
DOI
10.1007/978-3-540-74976-9
Copyright
2007
ISBN
978-3-540-74975-2
DOI
10.1007/978-3-540-74976-9_11
Pages
79-90
Subject Collection
Computer Science
SpringerLink Date
Thursday, August 30, 2007
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Context-Specific Independence Mixture Modelling for Protein Families
Benjamin Georgi
1
, Jörg Schultz
2
and Alexander Schliep
1
(1)
Max Planck Institute for Molecular Genetics, Dept. of Computational Molecular Biology, Ihnestrasse 73, 14195 Berlin, Germany
(2)
Universität Würzburg, Dept. of Bioinformatics, 97074 Wuerzburg, Germany
Abstract
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
.
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