Evidence map›Paper›PMID 24523353›Full record

ArticleNucleic acids research2014

An improved predictive recognition model for Cys(2)-His(2) zinc finger proteins.

Ankit Gupta, Ryan G Christensen, Heather A Bell, Mathew Goodwin, Ronak Y Patel, Manishi Pandey, Metewo Selase Enuameh, Amy L Rayla, Cong Zhu, Stacey Thibodeau-Beganny and 4 more

Abstract read
In one paragraph

Article in Nucleic acids research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.

0numbers the graph read from it
0cells of the map it votes in
48citing 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

48 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Neuroepigenetic Editing.Methods in molecular biology (Clifton, N.J.) · 2024
    Review
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  6. Two Sweet Sorghum (International journal of molecular sciences · 2023
    Article
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  8. Article
  9. Article
  10. Review
  11. Article
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  17. Review
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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

14 authors.

Ankit GuptaProgram in Gene Function and Expression, University of Massachusetts Medical School, Worcester, MA 01605, USA, Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, MA 01605, USA, Department of Genetics, Washington University School of Medicine, St Louis, MO 63108, USA, Department of Biochemistry and Biology and Biotechnology, Worcester Polytechnic Institute, Worcester, MA 01609, USA, Molecular Pathology Unit, Center for Computational and Integrative Biology, and Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA 02129, USA, Department of Molecular Medicine, University of Massachusetts Medical School, Worcester, MA 01605, USA and Department of Pathology, Harvard Medical School, Boston, MA 02115, USA.
Ryan G Christensen
Heather A Bell
Mathew Goodwin
Ronak Y Patel
Manishi Pandey
Metewo Selase Enuameh
Amy L Rayla
Cong Zhu
Stacey Thibodeau-Beganny
Michael H Brodsky
J Keith Joung
Scot A Wolfe
Gary D Stormo

Funding

DNA PATTERN IDENTIFICATION AND ANALYSISR01HG000249 · NHGRI · WASHINGTON UNIVERSITY · PI STORMO, GARY D · 1990 to 2017
$5.6M
Dimeric Cys2His2 Zinc Finger Proteins for Gene TargetingR01GM068110 · NIGMS · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI WOLFE, SCOT A · 2004 to 2013
$2.7M
Systematic Analysis of Drosophila transcription factor binding specificitiesR01HG004744 · NHGRI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI BRODSKY, MICHAEL H, WOLFE, SCOT A · 2008 to 2010
$1.9M
DNA-binding specificities of Cys2His2 zinc fingersR24GM078369 · NIGMS · MASSACHUSETTS GENERAL HOSPITAL · PI JOUNG, J. KEITH · 2007 to 2010
$1.5M
NHGRI NIH HHS HG000249NHGRI NIH HHS HG004744NHGRI NIH HHS R01 HG000249NIGMS NIH HHS GM068110NIGMS NIH HHS GM078369
6 · The paper itself

Abstract

Cys(2)-His(2) zinc finger proteins (ZFPs) are the largest family of transcription factors in higher metazoans. They also represent the most diverse family with regards to the composition of their recognition sequences. Although there are a number of ZFPs with characterized DNA-binding preferences, the specificity of the vast majority of ZFPs is unknown and cannot be directly inferred by homology due to the diversity of recognition residues present within individual fingers. Given the large number of unique zinc fingers and assemblies present across eukaryotes, a comprehensive predictive recognition model that could accurately estimate the DNA-binding specificity of any ZFP based on its amino acid sequence would have great utility. Toward this goal, we have used the DNA-binding specificities of 678 two-finger modules from both natural and artificial sources to construct a random forest-based predictive model for ZFP recognition. We find that our recognition model outperforms previously described determinant-based recognition models for ZFPs, and can successfully estimate the specificity of naturally occurring ZFPs with previously defined specificities.

Indexed as

Regulatory Elements, TranscriptionalZinc FingersArtificial IntelligenceBinding SitesDNADNA-Binding ProteinsModels, BiologicalNucleotide MotifsTranscription FactorsDNADNA-Binding ProteinsTranscription Factors

Identifiers

PMID24523353
PMCPMC4005693

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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.