This article is part of the supplement: Statistical mass spectrometry-based proteomics

Open Access Open Badges Review

Knowledge-based analysis of proteomics data

Marina Bessarabova, Alexander Ishkin, Lellean JeBailey, Tatiana Nikolskaya and Yuri Nikolsky*

Author Affiliations

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

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BMC Bioinformatics 2012, 13(Suppl 16):S13  doi:10.1186/1471-2105-13-S16-S13

Published: 5 November 2012


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.