title: Quantification of protein group coherence and pathway assignment using functional association.
abstract: BACKGROUND
  Genomics and proteomics experiments produce a large amount of data that are awaiting functional elucidation. An important step in analyzing such data is to identify functional units, which consist of proteins that play coherent roles to carry out the function. Importantly, functional coherence is not identical with functional similarity. For example, proteins in the same pathway may not share the same Gene Ontology (GO) terms, but they work in a coordinated fashion so that the aimed function can be performed. Thus, simply applying existing functional similarity measures might not be the best solution to identify functional units in omics data.
  
  
  RESULTS
  We have designed two scores for quantifying the functional coherence by considering association of GO terms observed in two biological contexts, co-occurrences in protein annotations and co-mentions in literature in the PubMed database. The counted co-occurrences of GO terms were normalized in a similar fashion as the statistical amino acid contact potential is computed in the protein structure prediction field. We demonstrate that the developed scores can identify functionally coherent protein sets, i.e. proteins in the same pathways, co-localized proteins, and protein complexes, with statistically significant score values showing a better accuracy than existing functional similarity scores. The scores are also capable of detecting protein pairs that interact with each other. It is further shown that the functional coherence scores can accurately assign proteins to their respective pathways.
  
  
  CONCLUSIONS
  We have developed two scores which quantify the functional coherence of sets of proteins. The scores reflect the actual associations of GO terms observed either in protein annotations or in literature. It has been shown that they have the ability to accurately distinguish biologically relevant groups of proteins from random ones as well as a good discriminative power for detecting interacting pairs of proteins. The scores were further successfully applied for assigning proteins to pathways.
authors.affiliation: Department of Computer Science, Purdue University, 305 N, University Street, West Lafayette, Indiana 47907, USA.
authors.forename: Meghana
authors.initials: M
authors.lastname: Chitale
authors.forename: Shriphani
authors.initials: S
authors.lastname: Palakodety
authors.forename: Daisuke
authors.initials: D
authors.lastname: Kihara
mesh_terms: D019295:Computational Biology; D023281:Genomics; D011336:Probability; D011506:Proteins / Q000737:chemistry / Q000235:genetics* / Q000378:metabolism*; D040901:Proteomics; D039781:PubMed; D018875:Vocabulary, Controlled
chemical_list: D011506:Proteins