Evidence map›Paper›PMID 30137428›Full record

ArticleNucleic acids research2018

Differential analysis of chromatin accessibility and histone modifications for predicting mouse developmental enhancers.

Shaliu Fu, Qin Wang, Jill E Moore, Michael J Purcaro, Henry E Pratt, Kaili Fan, Cuihua Gu, Cizhong Jiang, Ruixin Zhu, Anshul Kundaje and 2 more

Open access · goldAbstract read
In one paragraph

Article in Nucleic acids research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
2.2field-weighted citation impact, top 11% 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

27 citing papers in PubMed, 47 citations in OpenAlex.

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  5. The genetic regulatory architecture and epigenomic basis for age-related changes in rattlesnake venom.Proceedings of the National Academy of Sciences of the United States of America · 2024
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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

12 authors at 3 institutions in 2 countries.

Shaliu FuClinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China.
Qin WangClinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China.
Jill E MooreProgram in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA.
Michael J PurcaroProgram in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA.
Henry E PrattProgram in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA.
Kaili FanClinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China.
Cuihua GuClinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China.
Cizhong JiangClinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China.
Ruixin ZhuClinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China.
Anshul KundajeDepartment of Genetics, School of Medicine, Department of Computer Science, Stanford University, Stanford, CA 94305, USA.
Aiping LuClinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China.
Zhiping WengClinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China.
Tongji University · CNUniversity of Massachusetts Chan Medical School · USStanford University · US

Funding

EDAC: ENCODE Data Analysis CenterU24HG009446 · NHGRI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI GERSTEIN, MARK BENDER, WENG, ZHIPING · 2017 to 2022
$10.4M
EDAC: ENCODE Data Analysis CenterU41HG007000 · NHGRI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI WENG, ZHIPING · 2012 to 2016
$9.8M
Medical Scientist Training at UMMS Administrative SupplementT32GM107000 · NIGMS · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI KIEFE, CATARINA I. · 2013 to 2022
$4.9M
NHGRI NIH HHS U24 HG009446NHGRI NIH HHS U41 HG007000NIGMS NIH HHS T32 GM107000
6 · The paper itself

Abstract

Enhancers are distal cis-regulatory elements that modulate gene expression. They are depleted of nucleosomes and enriched in specific histone modifications; thus, calling DNase-seq and histone mark ChIP-seq peaks can predict enhancers. We evaluated nine peak-calling algorithms for predicting enhancers validated by transgenic mouse assays. DNase and H3K27ac peaks were consistently more predictive than H3K4me1/2/3 and H3K9ac peaks. DFilter and Hotspot2 were the best DNase peak callers, while HOMER, MUSIC, MACS2, DFilter and F-seq were the best H3K27ac peak callers. We observed that the differential DNase or H3K27ac signals between two distant tissues increased the area under the precision-recall curve (PR-AUC) of DNase peaks by 17.5-166.7% and that of H3K27ac peaks by 7.1-22.2%. We further improved this differential signal method using multiple contrast tissues. Evaluated using a blind test, the differential H3K27ac signal method substantially improved PR-AUC from 0.48 to 0.75 for predicting heart enhancers. We further validated our approach using postnatal retina and cerebral cortex enhancers identified by massively parallel reporter assays, and observed improvements for both tissues. In summary, we compared nine peak callers and devised a superior method for predicting tissue-specific mouse developmental enhancers by reranking the called peaks.

Indexed as

AlgorithmsAnimalsBinding SitesChromatinComputational BiologyEnhancer Elements, GeneticHistone CodeHistonesMice, TransgenicOrgan SpecificityProtein Processing, Post-TranslationalTranscription FactorsChromatinHistonesTranscription Factors

Identifiers

PMID30137428
PMCPMC6265487
OpenAlexW2888470397

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.