A benchmark of RNA-seq data normalization methods for transcriptome mapping on human genome-scale metabolic networks.

ABSTRACT: Genome-scale metabolic models (GEMs) cover the entire list of metabolic genes in an organism and associated reactions, in a tissue/condition non-specific manner. RNA-seq provides crucial information to make the GEMs condition-specific. Integrative Metabolic Analysis Tool (iMAT) and Integrative Network Inference for Tissues (INIT) are the two most popular algorithms to create condition-specific GEMs from human transcriptome data. The normalization method of choice for raw RNA-seq count data affects the model content produced by these algorithms and their predictive accuracy. However, a benchmark of the RNA-seq normalization methods on the performance of iMAT and INIT algorithms is missing in the literature. Another important phenomenon is covariates such as age and gender in a dataset, and

SUBMITTER: Luleci HB 

PROVIDER: S-EPMC11502818 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature