The generation of big data has enabled systems-level dissections into the mechanisms of cardiovascular pathology. Integration of genetic, proteomic, and pathophysiological variables across platforms and laboratories fosters discoveries through multidisciplinary investigations and minimizes unnecessary redundancy in research efforts. The Mouse Heart Attack Research Tool (mHART) consolidates a large data set of over 10 yr of experiments from a single laboratory for cardiovascular investigators to generate novel hypotheses and identify new predictive markers of progressive left ventricular remodeling after myocardial infarction (MI) in mice. We designed the mHART REDCap database using our own data to integrate cardiovascular community participation. We generated physiological, biochemical, cellular, and proteomic outputs from plasma and left ventricles obtained from post-MI and no-MI (naïve) control groups. We included both male and female mice ranging in age from 3 to 36 mo old. After variable collection, data underwent quality assessment for data curation (e.g., eliminate technical errors, check for completeness, remove duplicates, and define terms). Currently, mHART 1.0 contains >888,000 data points and includes results from >2,100 unique mice. Database performance was tested, and an example is provided to illustrate database utility. This report explains how the first version of the mHART database was established and provides researchers with a standard framework to aid in the integration of their data into our database or in the development of a similar database. NEW & NOTEWORTHY The Mouse Heart Attack Research Tool combines >888,000 cardiovascular data points from >2,100 mice. We provide this large data set as a REDCap database to generate novel hypotheses and identify new predictive markers of adverse left ventricular remodeling following myocardial infarction in mice and provide examples of use. The Mouse Heart Attack Research Tool is the first database of this size that integrates data sets across platforms that include genomic, proteomic, histological, and physiological data.
Keywords: big data; bioinformatics; cardiovascular disease; myocardial infarction; proteomics.