0
Save
Cite
Share
Author(s)
Content
Video Abstract (AI generated) (01:12)
Paper
Preprint
Nature Biotechnology
Predicting how transcription factors (TFs) interpret regulatory sequences to control gene expression remains a major challenge. Past studies have primarily focused on native or engineered sequences, and thus remained limited in scale. Here, we use random sequences as an alternative, measuring the expression output of over 100 million synthetic yeast promoters comprised of random DNA. Random sequences yield a broad range of reproducible expression levels, indicating that the fortuitous binding sites in random DNA are functional. From these data we learn models of transcriptional regulation that explain over 94% of expression variation of test data, recapitulate the organization of native chromatin in yeast, characterize the activity of TFs, and help refine cis-regulatory motifs. We find that strand, position, and helical face preferences of TFs are widespread and depend on interactions with neighboring chromatin. Such high-throughput regulatory assays of random DNA provide the large-scale data necessary to learn complex models of cis-regulatory logic.
More Projects
