Deciphering eukaryotic gene-regulatory logic with 100 million random promoters.

ABSTRACT: How transcription factors (TFs) interpret cis-regulatory DNA sequence to control gene expression remains unclear, largely because past studies using native and engineered sequences had insufficient scale. Here, we measure the expression output of >100 million synthetic yeast promoter sequences that are fully random. These sequences yield diverse, reproducible expression levels that can be explained by their chance inclusion of functional TF binding sites. We use machine learning to build interpretable models of transcriptional regulation that predict ~94% of the expression driven from independent test promoters and ~89% of the expression driven from native yeast promoter fragments. These models allow us to characterize each TF's specificity, activity and interactions with chromatin. TF act

SUBMITTER: de Boer CG 

PROVIDER: S-EPMC6954276 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature