Computational protocol: New perspectives: systems medicine in cardiovascular disease

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Protocol publication

[…] Using existing and/or newly generated data and prior knowledge resources as a basis for further analysis is a fundamental aspect to incrementally increase the scope of systems medicine research and will be a major aspect for data integration challenges in the years to come. Many of the algorithms and approaches mentioned below have already been proposed and/or applied in the field of cardiovascular diseases e.g. [–].Large consortia have collected and maintained cohorts of matching clinical patient, animal model and omics data (TCGA []; GEO [], and ArrayExpress []) and of cell line profiles (CCLE [], and LINCS []). Available data include sequencing data for genomics and transcriptomics, microarray mRNA and miRNA data as well as mass spectrometry data for proteomic analyses as well as many other “omics” data types. Table  lists a number of well-known public omics data repositories.Vast amounts of biomedical knowledge are available from online databases. This ranges from genetic sequence information on GenBank [] to protein information on UniProt [] to various sites of knowledge on molecular interactions, many of which are collected on the website [], which currently lists over 600 resources related to biological pathway and interaction knowledge. The most prominent pathway databases are Reactome [], the Network Data Exchange (NDEx) [], the Kyoto Encyclopedia of Genes and Genomes (KEGG) [], and WikiPathways [] as well as disease-specific databases such as the Online Mendelian Inheritance in Man (OMIM) resource []. Databases focusing on molecular interactions include BioGRID [], IntAct [] and STRING []. Further knowledge sources include drug-target databases, which connect therapeutica and targeted proteins and disease-target databases, which contain diseases known to be associated with specific mutations. Table  lists a number of well-known databases containing knowledge about molecular interactions.Many of these databases can be integrated into the R Framework for Statistical Computing. Examples for software packages which offer functionality to integrate database knowledge include the BioPAX-ontology [], Systems Biology Markup Language (SBML) [], the Human Proteome Organization (HUPO) Proteomics Standards Initiative standard for Molecular Interactions (HUPO PSI-MI) [] and NDEx []. In combination with mapping services, for example BioMart [], these packages enable the integration and merging of prior knowledge for further analyses. Table  includes a number of standards for encoding pathway knowledge and corresponding software for the integration of this knowledge. […]

Pipeline specifications

Software tools WikiPathways, BioMart
Databases OMIM BioGRID Reactome KEGG Pathguide NDEx HUPO-PSI
Application Genome annotation
Diseases Cardiovascular Diseases