MACHOS: Markov clusters of homologous subsequences

Wong, Simon and Ragan, Mark A. (2008) MACHOS: Markov clusters of homologous subsequences. Bioinformatics, 24 13: i77-i85. doi:10.1093/bioinformatics/btn144

Author Wong, Simon
Ragan, Mark A.
Title MACHOS: Markov clusters of homologous subsequences
Journal name Bioinformatics   Check publisher's open access policy
ISSN 1367-4803
Publication date 2008-07-01
Year available 2008
Sub-type Article (original research)
DOI 10.1093/bioinformatics/btn144
Volume 24
Issue 13
Start page i77
End page i85
Total pages 8
Editor Alex Bateman
Place of publication Oxford, United Kingdom
Publisher Oxford University Press
Collection year 2009
Language eng
Subject C1
970106 Expanding Knowledge in the Biological Sciences
069999 Biological Sciences not elsewhere classified
Formatted abstract
The classification of proteins into homologous groups (families) allows their structure and function to be analysed and compared in an evolutionary context. The modular nature of eukaryotic proteins presents a considerable challenge to the delineation of families, as different local regions within a single protein may share common ancestry with distinct, even mutually exclusive, sets of homologs, thereby creating an intricate web of homologous relationships if full-length sequences are taken as the unit of evolution. We attempt to disentangle this web by developing a fully automated pipeline to delineate protein subsequences that represent sensible units for homology inference, and clustering them into putatively homologous families using the Markov clustering algorithm.

Using six eukaryotic proteomes as input, we clustered 162 349 protein sequences into 19 697–77 415 subsequence families depending on granularity of clustering. We validated these Markov clusters of homologous subsequences (MACHOS) against the manually curated Pfam domain families, using a quality measure to assess overlap. Our subsequence families correspond well to known domain families and achieve higher quality scores than do groups generated by fully automated domain family classification methods. We illustrate our approach by analysis of a group of proteins that contains the glutamyl/glutaminyl-tRNA synthetase domain, and conclude that our method can produce high-coverage decomposition of protein sequence space into precise homologous families in a way that takes the modularity of eukaryotic proteins into account. This approach allows for a fine-scale examination of evolutionary histories of proteins encoded in eukaryotic genomes.
Q-Index Code C1
Q-Index Status Provisional Code

Document type: Journal Article
Sub-type: Article (original research)
Collections: Excellence in Research Australia (ERA) - Collection
Institute for Molecular Bioscience - Publications
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Citation counts: TR Web of Science Citation Count  Cited 6 times in Thomson Reuters Web of Science Article | Citations
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Created: Thu, 11 Jun 2009, 15:32:44 EST by Jennifer Greder on behalf of Institute for Molecular Bioscience