English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Book Chapter

Context-Specific Independence Mixture Modelling for Protein Families.

MPS-Authors

Georgi,  Benjamin
Max Planck Society;

/persons/resource/persons50523

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

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

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.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0010-817C-D
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.