Current sequence-based models capture gene expression determinants in promoters but mostly ignore distal enhancers.

ABSTRACT:

Background

The largest sequence-based models of transcription control to date are obtained by predicting genome-wide gene regulatory assays across the human genome. This setting is fundamentally correlative, as those models are exposed during training solely to the sequence variation between human genes that arose through evolution, questioning the extent to which those models capture genuine causal signals.

Results

Here we confront predictions of state-of-the-art models of transcription regulation against data from two large-scale observational studies and five deep perturbation assays. The most advanced of these sequence-based models, Enformer, by and large, captures causal determinants of human promoters. However, models fail to capture the causal effects of enhancers on

SUBMITTER: Karollus A 

PROVIDER: S-EPMC10045630 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature