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Bioinformatics. 2013 Jan 15;29(2):215-22. doi: 10.1093/bioinformatics/bts653. Epub 2012 Nov 9.

Density parameter estimation for finding clusters of homologous proteins--tracing actinobacterial pathogenicity lifestyles.

Author information

1
Max Planck Institute for Informatics, Saarland University, 66123 Saarbrücken, Germany. roettger@mpi-inf.mpg.de

Abstract

MOTIVATION:

Homology detection is a long-standing challenge in computational biology. To tackle this problem, typically all-versus-all BLAST results are coupled with data partitioning approaches resulting in clusters of putative homologous proteins. One of the main problems, however, has been widely neglected: all clustering tools need a density parameter that adjusts the number and size of the clusters. This parameter is crucial but hard to estimate without gold standard data at hand. Developing a gold standard, however, is a difficult and time consuming task. Having a reliable method for detecting clusters of homologous proteins between a huge set of species would open opportunities for better understanding the genetic repertoire of bacteria with different lifestyles.

RESULTS:

Our main contribution is a method for identifying a suitable and robust density parameter for protein homology detection without a given gold standard. Therefore, we study the core genome of 89 actinobacteria. This allows us to incorporate background knowledge, i.e. the assumption that a set of evolutionarily closely related species should share a comparably high number of evolutionarily conserved proteins (emerging from phylum-specific housekeeping genes). We apply our strategy to find genes/proteins that are specific for certain actinobacterial lifestyles, i.e. different types of pathogenicity. The whole study was performed with transitivity clustering, as it only requires a single intuitive density parameter and has been shown to be well applicable for the task of protein sequence clustering. Note, however, that the presented strategy generally does not depend on our clustering method but can easily be adapted to other clustering approaches.

AVAILABILITY:

All results are publicly available at http://transclust.mmci.uni-saarland.de/actino_core/ or as Supplementary Material of this article.

CONTACT:

roettger@mpi-inf.mpg.de

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
23142964
DOI:
10.1093/bioinformatics/bts653
[Indexed for MEDLINE]

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