A foundation model of transcription across human cell types

Transcriptional regulation, which involves a complex interplay between regulatory sequences and proteins, directs all biological processes. Computational models of transcription lack generalizability to accurately extrapolate to unseen cell types and conditions. Here we introduce GET (general expression transformer), an interpretable foundation model designed to uncover regulatory grammars across 213 human fetal and adult cell types1,2. Relying exclusively on chromatin accessibility data and sequence information, GET achieves experimental-level accuracy in predicting gene expression even in previously unseen cell types3. GET also shows remarkable adaptability across new sequencing platforms and assays,…

• NI
  National Institutes of Health

  Awards: P30CA013696, CA174653, R35 CA253126, P01 CA174653, R01 HL159377, U01 CA243073
• SU
  Stand Up To Cancer