Evidence map›Paper›PMID 35288750›Full record

ArticleNucleic acids research2022

DNAcycP: a deep learning tool for DNA cyclizability prediction.

Keren Li, Matthew Carroll, Reza Vafabakhsh, Xiaozhong A Wang, Ji-Ping Wang

Open access · goldAbstract read
In one paragraph

Article in Nucleic acids research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
3.7field-weighted citation impact, top 5% of its field
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

28 citing papers in PubMed, 45 citations in OpenAlex.

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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

5 authors at 1 institution in 1 country.

Keren LiDepartment of Statistics, Northwestern University, 633 Clark Street, Evanston, IL 60208, USA.
Matthew CarrollWeinberg College IT Solutions (WITS), Northwestern University, 633 Clark Street, Evanston, IL 60208, USA.
Reza VafabakhshDepartment of Molecular Biosciences, Northwestern University, Evanston, IL 60208, USA.
Xiaozhong A WangDepartment of Molecular Biosciences, Northwestern University, Evanston, IL 60208, USA.
Ji-Ping WangDepartment of Statistics, Northwestern University, 633 Clark Street, Evanston, IL 60208, USA.ORCID 0000-0002-8398-939X
Northwestern University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

DNA mechanical properties play a critical role in every aspect of DNA-dependent biological processes. Recently a high throughput assay named loop-seq has been developed to quantify the intrinsic bendability of a massive number of DNA fragments simultaneously. Using the loop-seq data, we develop a software tool, DNAcycP, based on a deep-learning approach for intrinsic DNA cyclizability prediction. We demonstrate DNAcycP predicts intrinsic DNA cyclizability with high fidelity compared to the experimental data. Using an independent dataset from in vitro selection for enrichment of loopable sequences, we further verified the predicted cyclizability score, termed C-score, can well distinguish DNA fragments with different loopability. We applied DNAcycP to multiple species and compared the C-scores with available high-resolution chemical nucleosome maps. Our analyses showed that both yeast and mouse genomes share a conserved feature of high DNA bendability spanning nucleosome dyads. Additionally, we extended our analysis to transcription factor binding sites and surprisingly found that the cyclizability is substantially elevated at CTCF binding sites in the mouse genome. We further demonstrate this distinct mechanical property is conserved across mammalian species and is inherent to CTCF binding DNA motif.

Indexed as

Deep LearningSoftwareAnimalsBinding SitesChromatinCyclizationDNAMammalsMiceNucleosomesSaccharomyces cerevisiaeChromatinDNANucleosomes

Identifiers

PMID35288750
PMCPMC8989542
OpenAlexW4213420903

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.