Evidence map›Paper›PMID 34996354›Full record

ArticleBMC genomics2022

Computational modeling of chromatin accessibility identified important epigenomic regulators.

Yanding Zhao, Yadong Dong, Wei Hong, Chongming Jiang, Kevin Yao, Chao Cheng

Abstract read
In one paragraph

Article in BMC genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Yanding ZhaoDepartment of Medicine, Baylor College of Medicine, Room ICTR 100D, One Baylor Plaza, Baylor College of Medicine, Houston, TX, 77030, USA.
Yadong DongDepartment of Medicine, Baylor College of Medicine, Room ICTR 100D, One Baylor Plaza, Baylor College of Medicine, Houston, TX, 77030, USA.
Wei HongDepartment of Medicine, Baylor College of Medicine, Room ICTR 100D, One Baylor Plaza, Baylor College of Medicine, Houston, TX, 77030, USA.
Chongming JiangDepartment of Medicine, Baylor College of Medicine, Room ICTR 100D, One Baylor Plaza, Baylor College of Medicine, Houston, TX, 77030, USA.
Kevin YaoDepartment of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA.
Chao ChengDepartment of Medicine, Baylor College of Medicine, Room ICTR 100D, One Baylor Plaza, Baylor College of Medicine, Houston, TX, 77030, USA. chao.cheng@bcm.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chromatin accessibility is essential for transcriptional activation of genomic regions. It is well established that transcription factors (TFs) and histone modifications (HMs) play critical roles in chromatin accessibility regulation. However, there is a lack of studies that quantify these relationships. Here we constructed a two-layer model to predict chromatin accessibility by integrating DNA sequence, TF binding, and HM signals. By applying the model to two human cell lines (GM12878 and HepG2), we found that DNA sequences had limited power for accessibility prediction, while both TF binding and HM signals predicted chromatin accessibility with high accuracy. According to the HM model, HM features determined chromatin accessibility in a cell line shared manner, with the prediction power attributing to five core HM types. Results from the TF model indicated that chromatin accessibility was determined by a subset of informative TFs including both cell line-specific and generic TFs. The combined model of both TF and HM signals did not further improve the prediction accuracy, indicating that they provide redundant information in terms of chromatin accessibility prediction. The TFs and HM models can also distinguish the chromatin accessibility of proximal versus distal transcription start sites with high accuracy.

Indexed as

ChromatinEpigenomicsBinding SitesChromatin ImmunoprecipitationComputer SimulationHumansProtein BindingChromatinChromatin accessibilityENCODEHistone modificationsMachine learningTranscription factor

Identifiers

PMID34996354
PMCPMC8742372

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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.