The Fitnome Catalog: a resource for physical exercise genetics data mining

Abstract

Physical exercise (PE) in regularity is a well-characterized non-pharmaceutical intervention for good health and welfare. Molecular mechanisms regulated in response to PE can be scrutinized, with molecular biology, genomics, transcriptomics, and bioinformatics being inserted into exercise physiology studies. From a biotechnological perspective, omic datasets about physical exercise gene expression help identify phenotypic, genetic variance for different physical training phenotypes. Extensive lists of genes regulated by PE were dispersed within the literature, and the Fitnome Catalog (FitC) was created to reach some systematization of this information. Manual and online text-mining tools generated this dataset in PE human gene expression articles (2003-2014) with microarray, RNA-Seq, RT-PCR, and genotyping methods. Spreadsheets were developed with information on exercise protocol, experimental design, gender, age, number of individuals, analytical approach, gene ID, fold change and statistical data, and genetic architecture, encompassing 21 columns. The produced dataset (with 5,147 genes and 101,343 data points) provides experimental design, gene expression information, gene attributes, and references. Functional categorization of the FitC dataset and standardized information on PE-expressed genes were presented.