Functional abstraction as a method to discover knowledge in gene ontologies

  • Computational analyses of functions of gene sets obtained in microarray analyses or by topical database searches are increasingly important in biology. To understand their functions, the sets are usually mapped to Gene Ontology knowledge bases by means of over-representation analysis (ORA). Its result represents the specific knowledge of the functionality of the gene set. However, the specific ontology typically consists of many terms and relationships, hindering the understanding of the ‘main story’. We developed a methodology to identify a comprehensibly small number of GO terms as “headlines” of the specific ontology allowing to understand all central aspects of the roles of the involved genes. The Functional Abstraction method finds a set of headlines that is specific enough to cover all details of a specific ontology and is abstract enough for human comprehension. This method exceeds the classical approaches at ORA abstraction and by focusing on information rather than decorrelation of GO terms, it directly targets human comprehension. Functional abstraction provides, with a maximum of certainty, information value, coverage and conciseness, a representation of the biological functions in a gene set plays a role. This is the necessary means to interpret complex Gene Ontology results thus strengthening the role of functional genomics in biomarker and drug discovery.

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Author:Alfred UltschGND, Jörn LötschORCiDGND
Pubmed Id:
Parent Title (English):PLoS One
Place of publication:Lawrence, Kan.
Document Type:Article
Date of Publication (online):2014/02/25
Date of first Publication:2014/02/25
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2014/02/26
Page Number:8
Copyright: © 2014 Ultsch, Lötsch. This is an open-access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Institutes:Medizin / Medizin
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie
Licence (German):License LogoCreative Commons - Namensnennung 4.0