From proteome lists to biological impact--tools and strategies for the analysis of large MS data sets.
MS has become a method-of-choice for proteome analysis, generating large data sets, which reflect proteome-scale protein-protein interaction and PTM networks. However, while a rapid growth in large-scale proteomics data can be observed, the sound biological interpretation of these results clearly lags behind. Therefore, combined efforts of bioinformaticians and biologists have been made to develop strategies and applications to help experimentalists perform this crucial task. This review presents an overview of currently available analytical strategies and tools to extract biologically relevant information from large protein lists. Moreover, we also present current research publications making use of these tools as examples of how the presented strategies may be incorporated into proteomic workflows. Emphasis is placed on the analysis of Gene Ontology terms, interaction networks, biological pathways and PTMs. In addition, topics including domain analysis and text mining are reviewed in the context of computational analysis of proteomic results. We expect that these types of analyses will significantly contribute to a deeper understanding of the role of individual proteins, protein networks and pathways in complex systems.
Malik
Rainer
R
Max Planck Institute of Biochemistry, Department of Cell Biology, Martinsried, Germany.
Dulla
Kalyan
K
Nigg
Erich A
EA
Körner
Roman
R
D019295:Computational Biology / Q000379:methods; D057225:Data Mining / Q000379:methods*; D030541:Databases, Genetic; D030562:Databases, Protein; D025941:Protein Interaction Mapping; D017434:Protein Structure, Tertiary; D020543:Proteome / Q000032:analysis; D040901:Proteomics / Q000379:methods*; D053719:Tandem Mass Spectrometry; D018875:Vocabulary, Controlled
D020543:Proteome