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The Cardiovascular Disease (CVD) database integrates multi-omic data for cardiovascular traits, especially coronary artery disease (CAD). It aids research by linking clinical information with molecular data from diverse studies.

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Area of Science:

  • Bioinformatics and Computational Biology
  • Genomics and Systems Biology
  • Cardiovascular Research

Background:

  • Coronary Artery Disease (CAD) is a leading cause of death globally, influenced by genetic and environmental factors.
  • Understanding CAD's complex etiology requires integrating multi-omic data (microRNA, genomics, proteomics, metabolomics).
  • Existing resources often lack comprehensive integration of diverse omic data and clinical information for cardiovascular diseases.

Purpose of the Study:

  • To develop an integrated data repository for cardiovascular diseases (CVDs), focusing on coronary artery disease (CAD).
  • To harmonize and link multi-omic data (genomics, transcriptomics, proteomics, metabolomics) with clinical information.
  • To provide a user-accessible platform for querying and analyzing CVD-related molecular data.

Main Methods:

  • Literature mining and public database curation to build the CVD database.
  • Application of a specific ontology, including Clustered Systems Ontology (CluSO) and Orthologous Mapped genes (OMAP), for data integration across platforms and species.
  • Manual biocuration and development of an interactive web interface with a multi-parametric search engine.

Main Results:

  • The CVD database integrates data from 92 studies, comprising 13,945 molecular entries (4,353 unique molecules).
  • Data includes experimental setup, study design, sample sizes, and differential gene expression data (p-value < 0.05).
  • Demonstrated utility through an integrative analysis of CAD-associated studies.

Conclusions:

  • The developed CVD database provides a valuable resource for researchers studying cardiovascular diseases, particularly CAD.
  • The integrated multi-omic data and analytical tools facilitate systems-level understanding of disease mechanisms.
  • The platform enables efficient querying and analysis of complex molecular and clinical data for CVD research.