Evidence map›Paper›PMID 42641603›Full record

ArticleCell genomics2026

Cooperativity enables widespread role of low-affinity motifs in chromatin accessibility and increases regulatory potential.

Melanie Weilert, Kaelan J Brennan, Khyati Dalal, Sabrina Krueger, Haining Jiang, Rosa Martinez-Corral, Julia Zeitlinger

Abstract read
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In one paragraph

Article in Cell genomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. AlphaGenome Atlas:medRxiv : the preprint server for health sciences · 2026
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Melanie WeilertStowers Institute for Medical Research, Kansas City, MO, USA.
Kaelan J BrennanStowers Institute for Medical Research, Kansas City, MO, USA.
Khyati DalalStowers Institute for Medical Research, Kansas City, MO, USA; Department of Pathology & Laboratory Medicine, The University of Kansas Medical Center, Kansas City, KS, USA.
Sabrina KruegerStowers Institute for Medical Research, Kansas City, MO, USA.
Haining JiangStowers Institute for Medical Research, Kansas City, MO, USA.
Rosa Martinez-CorralCRG (Barcelona Collaboratorium for Modelling and Predictive Biology), Barcelona, Spain.
Julia ZeitlingerStowers Institute for Medical Research, Kansas City, MO, USA; Department of Pathology & Laboratory Medicine, The University of Kansas Medical Center, Kansas City, KS, USA. Electronic address: jbz@stowers.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Low-affinity transcription-factor (TF) motifs are an important element of the cis-regulatory code, yet they are notoriously difficult to map and mechanistically incompletely understood, limiting our ability to interpret non-coding variation in development, evolution, and disease. Here, we investigate their role in pioneering and leverage sequence-to-profile models of chromatin accessibility in mouse embryonic stem cells to reliably map and interpret low-affinity motifs across the genome. We find that low-affinity motifs have outsized effects by cooperating with nearby motifs through intra-nucleosomal soft syntax. By modeling nucleosome-mediated cooperativity with a kinetic model, we discover and validate that pioneer cooperativity makes a motif operate at higher pioneering ranges across changing TF concentrations, thereby raising the regulatory potential. These results show that low-affinity motifs can be accurately mapped, shape the properties of developmental enhancers, and likely play a widespread role in fine-tuning enhancers during evolution.

Indexed as

chromatin accessibilitycis-regulatory encodinggene regulationinterpretable deep learninglow-affinity motifsmathematical modelingmouse embryonic stem cellspioneer transcription factors

Identifiers

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.