IntLIM: integration using linear models of metabolomics and gene expression data.

ABSTRACT:

Background

Integration of transcriptomic and metabolomic data improves functional interpretation of disease-related metabolomic phenotypes, and facilitates discovery of putative metabolite biomarkers and gene targets. For this reason, these data are increasingly collected in large (> 100 participants) cohorts, thereby driving a need for the development of user-friendly and open-source methods/tools for their integration. Of note, clinical/translational studies typically provide snapshot (e.g. one time point) gene and metabolite profiles and, oftentimes, most metabolites measured are not identified. Thus, in these types of studies, pathway/network approaches that take into account the complexity of transcript-metabolite relationships may neither be applicable nor readily uncover nov

SUBMITTER: Siddiqui JK 

PROVIDER: S-EPMC5838881 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature