StanDep: Capturing transcriptomic variability improves context-specific metabolic models.

ABSTRACT: Diverse algorithms can integrate transcriptomics with genome-scale metabolic models (GEMs) to build context-specific metabolic models. These algorithms require identification of a list of high confidence (core) reactions from transcriptomics, but parameters related to identification of core reactions, such as thresholding of expression profiles, can significantly change model content. Importantly, current thresholding approaches are burdened with setting singular arbitrary thresholds for all genes; thus, resulting in removal of enzymes needed in small amounts and even many housekeeping genes. Here, we describe StanDep, a novel heuristic method for using transcriptomics to identify core reactions prior to building context-specific metabolic models. StanDep clusters gene expression data base

SUBMITTER: Joshi CJ 

PROVIDER: S-EPMC7244210 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature