Evidence map›Paper›PMID 39075082›Full record

ArticleNature communications2024

Exploring the roles of RNAs in chromatin architecture using deep learning.

Shuzhen Kuang, Katherine S Pollard

Abstract read
In one paragraph

Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
–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

21 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Machine learning reveals sequence and genomic context features underlyingbioRxiv : the preprint server for biology · 2026
    Article
  4. Article
  5. mNature reviews. Neuroscience · 2026
    Review
  6. Review
  7. Article
  8. Review
  9. Article
  10. Direct roles of long non-coding RNAs in transcription activation.Nature structural & molecular biology · 2026
    Review
  11. Article
  12. Article
  13. Article
  14. Review
  15. Role of RNA in genome folding: It's all about affinity.Current opinion in structural biology · 2025
    Review
  16. Article
  17. Recipes and ingredients for deep learning models of 3D genome folding.Current opinion in genetics & development · 2025
    Review
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Shuzhen KuangGladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA.ORCID 0009-0008-3844-3647
Katherine S PollardGladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA. katherine.pollard@gladstone.ucsf.edu.ORCID 0000-0002-9870-6196

Funding

Genetic determinants of 4D genome folding in human cardiac developmentU01HL157989 · NHLBI · J. DAVID GLADSTONE INSTITUTES · PI BRUNEAU, BENOIT GAETAN, POLLARD, KATHERINE S. · 2020 to 2024
$3.8M
NHLBI NIH HHS U01 HL157989U.S. Department of Health & Human Services | National Institutes of Health (NIH) U01HL157989
6 · The paper itself

Abstract

Recent studies have highlighted the impact of both transcription and transcripts on 3D genome organization, particularly its dynamics. Here, we propose a deep learning framework, called AkitaR, that leverages both genome sequences and genome-wide RNA-DNA interactions to investigate the roles of chromatin-associated RNAs (caRNAs) on genome folding in HFFc6 cells. In order to disentangle the cis- and trans-regulatory roles of caRNAs, we have compared models with nascent transcripts, trans-located caRNAs, open chromatin data, or DNA sequence alone. Both nascent transcripts and trans-located caRNAs improve the models' predictions, especially at cell-type-specific genomic regions. Analyses of feature importance scores reveal the contribution of caRNAs at TAD boundaries, chromatin loops and nuclear sub-structures such as nuclear speckles and nucleoli to the models' predictions. Furthermore, we identify non-coding RNAs (ncRNAs) known to regulate chromatin structures, such as MALAT1 and NEAT1, as well as several new RNAs, RNY5, RPPH1, POLG-DT and THBS1-IT1, that might modulate chromatin architecture through trans-interactions in HFFc6. Our modeling also suggests that transcripts from Alus and other repetitive elements may facilitate chromatin interactions through trans R-loop formation. Our findings provide insights and generate testable hypotheses about the roles of caRNAs in shaping chromatin organization.

Indexed as

ChromatinDeep LearningCell LineDNAHumansRNARNA, Long NoncodingChromatinDNAMALAT1 long non-coding RNA, humanNEAT1 long non-coding RNA, humanRNARNA, Long Noncoding

Identifiers

PMID39075082
PMCPMC11286850

What OpenQuestion holds

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Read underepoch 390

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.