In-Cardiome: integrated knowledgebase for coronary artery disease enabling translational research

Abstract Coronary artery disease (CAD) is a leading cause of death worldwide. Prevention, diagnosis and clinical interventions are dependent on the conventional risk factors like hypertension, diabetes and obesity. However, these conventional risk factors do not completely identify high risk individuals. One major hurdle in the improvement of diagnosis and treatment for CAD is the lack of integration of knowledge from different areas of research like molecular, clinical and drug development. In order to provide comprehensive information from hitherto dispersed data, we developed an integrative knowledgebase called “In-Cardiome or Integrated Cardiome” for all the stake holders in healthcare such as scientists, clinicians and pharmaceutical companies. It is created by integrating 16 different data sources, 995 curated genes classified into 12 different functional categories associated with disease, 1204 completed clinical trials, 12 therapy or drug classifications with 62 approved drugs and drug target networks. This knowledgebase gives the most needed opportunity to understand the disease process and therapeutic impact along with gene expression data from both animal models and patients. The data is classified into three different search categories functional groups, risk factors and therapy/drug based classes. One more unique aspect of In-Cardiome is integration of clinical data of 10,217 subject data from our ongoing Indian Atherosclerosis Research Study (IARS) (6357 unaffected and 3860 CAD affected). IARS data showing demographics and associations of individual and combinations of risk factors in Indian population along with molecular information will enable better translational and drug development research. Database URL www.tri-incardiome.org

[1]  Wen-Lian Hsu,et al.  T-HOD: a literature-based candidate gene database for hypertension, obesity and diabetes , 2013, Database J. Biol. Databases Curation.

[2]  Brad T. Sherman,et al.  DAVID: Database for Annotation, Visualization, and Integrated Discovery , 2003, Genome Biology.

[3]  V. Kakkar,et al.  Understanding the progression of atherosclerosis through gene profiling and co-expression network analysis in Apob(tm2Sgy)Ldlr(tm1Her) double knockout mice. , 2016, Genomics.

[4]  Martin Vingron,et al.  IntAct: an open source molecular interaction database , 2004, Nucleic Acids Res..

[5]  Judith A. Blake,et al.  MGD: the Mouse Genome Database , 2003, Nucleic Acids Res..

[6]  Ankit Sharma,et al.  Association of γ-glutamyl transferase with premature coronary artery disease , 2016, Biomedical reports.

[7]  Vicente Hernández,et al.  NeuPAT: An intranet database supporting translational research in neuroblastic tumors , 2013, Comput. Biol. Medicine.

[8]  Suzanne M. Paley,et al.  Beyond the genome (BTG) is a (PGDB) pathway genome database: HumanCyc , 2010, Genome Biology.

[9]  Hui Liu,et al.  CADgene: a comprehensive database for coronary artery disease genes , 2010, Nucleic Acids Res..

[10]  Cathy H. Wu,et al.  UniProt: the Universal Protein knowledgebase , 2004, Nucleic Acids Res..

[11]  Elizabeth M. Smigielski,et al.  dbSNP: the NCBI database of genetic variation , 2001, Nucleic Acids Res..

[12]  Lincoln Stein,et al.  Reactome knowledgebase of human biological pathways and processes , 2008, Nucleic Acids Res..

[13]  V. Kakkar,et al.  Rationale, design & preliminary findings of the Indian Atherosclerosis Research Study. , 2010, Indian heart journal.

[14]  Jun Gao,et al.  DW4TR: A Data Warehouse for Translational Research , 2011, J. Biomed. Informatics.

[15]  Eric J Topol,et al.  Prevalence of conventional risk factors in patients with coronary heart disease. , 2003, JAMA.

[16]  David S. Wishart,et al.  SMPDB: The Small Molecule Pathway Database , 2009, Nucleic Acids Res..

[17]  Damian Szklarczyk,et al.  The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored , 2010, Nucleic Acids Res..

[18]  R. Murugesan,et al.  CardioGenBase: A Literature Based Multi-Omics Database for Major Cardiovascular Diseases , 2015, PloS one.

[19]  Hiroyuki Ogata,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 1999, Nucleic Acids Res..

[20]  Alex E. Lash,et al.  Gene Expression Omnibus: NCBI gene expression and hybridization array data repository , 2002, Nucleic Acids Res..

[21]  Christian Stolte,et al.  COMPARTMENTS: unification and visualization of protein subcellular localization evidence , 2014, Database J. Biol. Databases Curation.

[22]  H. Son,et al.  Categorizer: a tool to categorize genes into user-defined biological groups based on semantic similarity , 2014, BMC Genomics.

[23]  Weisong Liu,et al.  The Rat Genome Database 2015: genomic, phenotypic and environmental variations and disease , 2014, Nucleic Acids Res..

[24]  David S. Wishart,et al.  DrugBank: a comprehensive resource for in silico drug discovery and exploration , 2005, Nucleic Acids Res..

[25]  R. Altman,et al.  Pharmacogenomics Knowledge for Personalized Medicine , 2012, Clinical pharmacology and therapeutics.

[26]  David S. Wishart,et al.  Nucleic Acids Research Polysearch: a Web-based Text Mining System for Extracting Relationships between Human Diseases, Genes, Mutations, Drugs Polysearch: a Web-based Text Mining System for Extracting Relationships between Human Diseases, Genes, Mutations, Drugs and Metabolites , 2008 .

[27]  Nicholas C. Ide,et al.  The ClinicalTrials.gov results database--update and key issues. , 2011, The New England journal of medicine.

[28]  Dietrich Rebholz-Schuhmann,et al.  EBIMed - text crunching to gather facts for proteins from Medline , 2007, Bioinform..

[29]  Philip Lijnzaad,et al.  The Ensembl genome database project , 2002, Nucleic Acids Res..

[30]  V. Kakkar,et al.  Understanding gene expression in coronary artery disease through global profiling, network analysis and independent validation of key candidate genes , 2015, Journal of Genetics.

[31]  Ulf Leser,et al.  ALIBABA: PubMed as a graph , 2006, Bioinform..

[32]  Ankit Sharma,et al.  Translational informatics approach for identifying the functional molecular communicators linking coronary artery disease, infection and inflammation , 2016, Molecular medicine reports.

[33]  V. Kakkar,et al.  Application of cardiovascular disease risk prediction models and the relevance of novel biomarkers to risk stratification in Asian Indians , 2008, Vascular health and risk management.