Analyzing and interpreting genome data at the network level with ConsensusPathDB.
ConsensusPathDB consists of a comprehensive collection of human (as well as mouse and yeast) molecular interaction data integrated from 32 different public repositories and a web interface featuring a set of computational methods and visualization tools to explore these data. This protocol describes the use of ConsensusPathDB (http://consensuspathdb.org) with respect to the functional and network-based characterization of biomolecules (genes, proteins and metabolites) that are submitted to the system either as a priority list or together with associated experimental data such as RNA-seq. The tool reports interaction network modules, biochemical pathways and functional information that are significantly enriched by the user's input, applying computational methods for statistical over-representation, enrichment and graph analysis. The results of this protocol can be observed within a few minutes, even with genome-wide data. The resulting network associations can be used to interpret high-throughput data mechanistically, to characterize and prioritize biomarkers, to integrate different omics levels, to design follow-up functional assay experiments and to generate topology for kinetic models at different scales.
Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany.
Ralf
0000-0002-9335-1760
R
Herwig
Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany.
Christopher
C
Hardt
Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany.
Matthias
M
Lienhard
Department of Pathology and Cancer Center, Massachusetts General Hospital, Boston, Massachusetts, USA.|Harvard Medical School, Boston, Massachusetts, USA.|Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
Atanas
A
Kamburov
D000465:Algorithms; D000818:Animals; D030541:Databases, Genetic; D063990:Gene Ontology; D016678:Genome; D023281:Genomics / Q000379:methods*; D006801:Humans; D020407:Internet; D053858:Metabolic Networks and Pathways; D055432:Metabolomics / Q000379:methods; D051379:Mice; D060066:Protein Interaction Maps; D011506:Proteins / Q000235:genetics* / Q000378:metabolism*; D012984:Software; D014584:User-Computer Interface; D015003:Yeasts
D011506:Proteins