Knowledge-based analysis of proteomics data

BMC Bioinformatics

Review

Marina Bessarabova¹  Tatiana Nikolskaya¹  Alexander Ishkin¹  Lellean JeBailey¹  Yuri Nikolsky¹ 

[1] Thomson Reuters, IP & Science, 5901 Priestly Dr., #200, 92008, Carlsbad, CA, USA;

关键词: Gene Ontology;    Short Path;    Enrichment Analysis;    Cluster Coefficient;    Proteomics Profile;   

DOI  :  10.1186/1471-2105-13-S16-S13

来源: Springer

【 摘 要 】

As it is the case with any OMICs technology, the value of proteomics data is defined by the degree of its functional interpretation in the context of phenotype. Functional analysis of proteomics profiles is inherently complex, as each of hundreds of detected proteins can belong to dozens of pathways, be connected in different context-specific groups by protein interactions and regulated by a variety of one-step and remote regulators. Knowledge-based approach deals with this complexity by creating a structured database of protein interactions, pathways and protein-disease associations from experimental literature and a set of statistical tools to compare the proteomics profiles with this rich source of accumulated knowledge. Here we describe the main methods of ontology enrichment, interactome topology and network analysis applied on a comprehensive, manually curated and semantically consistent knowledge source MetaBase and demonstrate several case studies in different disease areas.

【 授权许可】

Unknown   
© Bessarabova et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.