Predictive features of gene expression variation reveal mechanistic link with differential expression.

ABSTRACT: For most biological processes, organisms must respond to extrinsic cues, while maintaining essential gene expression programmes. Although studied extensively in single cells, it is still unclear how variation is controlled in multicellular organisms. Here, we used a machine-learning approach to identify genomic features that are predictive of genes with high versus low variation in their expression across individuals, using bulk data to remove stochastic cell-to-cell variation. Using embryonic gene expression across 75 Drosophila isogenic lines, we identify features predictive of expression variation (controlling for expression level), many of which are promoter-related. Genes with low variation fall into two classes reflecting different mechanisms to maintain robust expression, while gene

SUBMITTER: Sigalova OM 

PROVIDER: S-EPMC7411568 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature