Analyzing large biological datasets with association networks

• Published in: Nucleic acids research Vol. 40; no. 17; p. e131
• Main Authors: Karpinets, Tatiana V, Park, Byung H, Uberbacher, Edward C
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01-09-2012
• Subjects: Algorithms; BASIC BIOLOGICAL SCIENCES; Cluster Analysis; Computer applications; Computer programs; Data processing; Databases, Genetic; Gene Regulatory Networks; Genome, Bacterial; Genomes; Genomics - methods; Internet; Methods Online; Molecular Sequence Annotation; Monte Carlo Method; Pathogens; Symbionts; Vocabulary, Controlled
• ISSN: 0305-1048; 1362-4962
• DOI: 10.1093/nar/gks403

Due to advances in high-throughput biotechnologies biological information is being collected in databases at an amazing rate, requiring novel computational approaches that process collected data into new knowledge in a timely manner. In this study, we propose a computational framework for discovering modular structure, relationships and regularities in complex data. The framework utilizes a semantic-preserving vocabulary to convert records of biological annotations of an object, such as an organism, gene, chemical or sequence, into networks (Anets) of the associated annotations. An association between a pair of annotations in an Anet is determined by the similarity of their co-occurrence pattern with all other annotations in the data. This feature captures associations between annotations that do not necessarily co-occur with each other and facilitates discovery of the most significant relationships in the collected data through clustering and visualization of the Anet. To demonstrate this approach, we applied the framework to the analysis of metadata from the Genomes OnLine Database and produced a biological map of sequenced prokaryotic organisms with three major clusters of metadata that represent pathogens, environmental isolates and plant symbionts.