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Antonov, A.V. ; Dietmann, S. ; Rodchenkov, I. ; Mewes, H.-W.

PPI spider: A tool for the interpretation of proteomics data in the context of protein-protein interaction networks.

Proteomics 9, 2740-2749 (2009)
DOI PMC
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Recent advances in experimental technologies allow for the detection of a complete cell proteome. Proteins that are expressed at a particular cell state or in a particular compartment as well as proteins with differential expression between various cells states are commonly delivered by many proteomics studies. Once a list of proteins is derived, a major challenge is to interpret the identified set of proteins in the biological context. Protein-protein interaction (PPI) data represents abundant information that can be employed for this purpose. However, these data have not yet been fully exploited due to the absence of a methodological framework that can integrate this type of information. Here, we propose to infer a network model from an experimentally identified protein list based on the available information about the topology of the global PPI network. We propose to use a Monte Carlo simulation procedure to compute the statistical significance of the inferred models. The method has been implemented as a freely available web-based tool, PPI spider (http://mips.helmholtz-muenchen.de/proj/ppispider). To support the practical significance of PPI spider, we collected several hundreds of recently published experimental proteomics studies that reported lists of proteins in various biological contexts. We reanalyzed them using PPI spider and demonstrated that in most cases PPI spider could provide statistically significant hypotheses that are helpful for understanding of the protein list.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Enrichment analyses; Inference of molecular mechanisms that are relevant to a given protein list; Protein-protein interaction networks; high-throughput data; cancer-cells; biotechnology-information; liquid-chromatography; complex functionality; mass-spectrometry; prostate-cancer; national-center; expression; resources
ISSN (print) / ISBN 1615-9853
e-ISSN 1615-9861
Zeitschrift Proteomics
Quellenangaben Band: 9, Heft: 10, Seiten: 2740-2749 Artikelnummer: , Supplement: ,
Verlag Wiley
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Bioinformatics and Systems Biology (IBIS)