title: MACHOS: Markov clusters of homologous subsequences.
abstract: BACKGROUND
  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.
  
  
  RESULTS
  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.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online. MACHOS for the six proteomes are available as FASTA-formatted files: http://research1t.imb.uq.edu.au/ragan/machos.
authors.affiliation: ARC Centre of Excellence in Bioinformatics and Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia.
authors.forename: Simon
authors.initials: S
authors.lastname: Wong
authors.forename: Mark A
authors.initials: MA
authors.lastname: Ragan
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D016000:Cluster Analysis; D003198:Computer Simulation; D008390:Markov Chains; D008956:Models, Chemical; D015233:Models, Statistical; D008969:Molecular Sequence Data; D017434:Protein Structure, Tertiary; D020543:Proteome / Q000737:chemistry*; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid
chemical_list: D020543:Proteome