Work

Deep learning uncovers hidden power of low-affinity motifs

Year: 2026

Type: article

Abstract: Low-affinity transcription factor binding sites are functionally important but difficult to map and mechanistically poorly understood. In this issue of Cell Genomics, Weilert et al. leverage sequence-to-function deep learning models to systematically map low-affinity pioneer factor binding sites in mouse embryonic stem cells and reveal how these sites contribute to chromatin opening through soft-syntax nucleosomal cooperativity.

Source: Cell Genomics

DOI: 10.1016/j.xgen.2026.101392

Authors: Grace Bower, Evgeny Z. Kvon

Institution: University of California, Irvine

Language: English

Topic: Genomics and Chromatin Dynamics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $8,900

Funders: National Institutes of Health, NIH Office of the Director, National Institute of General Medical Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development