4.7 Article

Phylogenetic-based propagation of functional annotations within the Gene Ontology consortium

Journal

BRIEFINGS IN BIOINFORMATICS
Volume 12, Issue 5, Pages 449-462

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/bib/bbr042

Keywords

gene ontology; genome annotation; reference genome; gene function prediction; phylogenetics

Funding

  1. National Institute of General Medical Sciences [R01-GM081084]
  2. National Institute of Human Genome Research [P41-HG002273]
  3. Gene Ontology Consortium

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The goal of the Gene Ontology (GO) project is to provide a uniform way to describe the functions of gene products from organisms across all kingdoms of life and thereby enable analysis of genomic data. Protein annotations are either based on experiments or predicted from protein sequences. Since most sequences have not been experimentally characterized, most available annotations need to be based on predictions. To make as accurate inferences as possible, the GO Consortium's Reference Genome Project is using an explicit evolutionary framework to infer annotations of proteins from a broad set of genomes from experimental annotations in a semi-automated manner. Most components in the pipeline, such as selection of sequences, building multiple sequence alignments and phylogenetic trees, retrieving experimental annotations and depositing inferred annotations, are fully automated. However, the most crucial step in our pipeline relies on software-assisted curation by an expert biologist. This curation tool, Phylogenetic Annotation and INference Tool (PAINT) helps curators to infer annotations among members of a protein family. PAINT allows curators to make precise assertions as to when functions were gained and lost during evolution and record the evidence (e.g. experimentally supported GO annotations and phylogenetic information including orthology) for those assertions. In this article, we describe how we use PAINT to infer protein function in a phylogenetic context with emphasis on its strengths, limitations and guidelines. We also discuss specific examples showing how PAINT annotations compare with those generated by other highly used homology-based methods.

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