Evidence map›Paper›PMID 25378309›Full record

ArticleNucleic acids research2014

Optimization of transcription factor binding map accuracy utilizing knockout-mouse models.

Wolfgang Krebs, Susanne V Schmidt, Alon Goren, Dominic De Nardo, Larisa Labzin, Anton Bovier, Thomas Ulas, Heidi Theis, Michael Kraut, Eicke Latz and 2 more

Open access · goldAbstract 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 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
1.7field-weighted citation impact, top 16% 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

16 citing papers in PubMed, 27 citations in OpenAlex.

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  7. The Genome-Wide Binding Profile for Human RE1 Silencing Transcription Factor Unveils a Unique Genetic Circuitry in Hippocampus.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021
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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.

Wolfgang KrebsGenomics and Immunoregulation, LIMES-Institute, University of Bonn, 53115 Bonn, Germany.
Susanne V SchmidtGenomics and Immunoregulation, LIMES-Institute, University of Bonn, 53115 Bonn, Germany.
Alon GorenBroad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Dominic De NardoInstitute of Innate Immunity, University Hospitals, University of Bonn, 53127 Bonn, Germany.
Larisa LabzinInstitute of Innate Immunity, University Hospitals, University of Bonn, 53127 Bonn, Germany.
Anton BovierInstitute for Applied Mathematics, University of Bonn, 53115 Bonn, Germany.
Thomas UlasGenomics and Immunoregulation, LIMES-Institute, University of Bonn, 53115 Bonn, Germany.
Heidi TheisGenomics and Immunoregulation, LIMES-Institute, University of Bonn, 53115 Bonn, Germany.
Michael KrautGenomics and Immunoregulation, LIMES-Institute, University of Bonn, 53115 Bonn, Germany.
Eicke LatzInstitute of Innate Immunity, University Hospitals, University of Bonn, 53127 Bonn, Germany Division of Infectious Diseases and Immunology, UMass Medical School, Worcester, MA 01605, USA German Center of Neurodegenerative Diseases (DZNE), 53175 Bonn, Germany.
Marc BeyerGenomics and Immunoregulation, LIMES-Institute, University of Bonn, 53115 Bonn, Germany.
Joachim L SchultzeGenomics and Immunoregulation, LIMES-Institute, University of Bonn, 53115 Bonn, Germany j.schultze@uni-bonn.de.
University of Bonn · DEBroad Institute · USGerman Center for Neurodegenerative Diseases · DE

Funding

Inflammasome activation by cholesterol crystalsR01HL093262 · NHLBI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI LATZ, EICKE · 2009 to 2013
$2.0M
NHLBI NIH HHS 1R01HL093262
6 · The paper itself

Abstract

Genome-wide assessment of protein-DNA interaction by chromatin immunoprecipitation followed by massive parallel sequencing (ChIP-seq) is a key technology for studying transcription factor (TF) localization and regulation of gene expression. Signal-to-noise-ratio and signal specificity in ChIP-seq studies depend on many variables, including antibody affinity and specificity. Thus far, efforts to improve antibody reagents for ChIP-seq experiments have focused mainly on generating higher quality antibodies. Here we introduce KOIN (knockout implemented normalization) as a novel strategy to increase signal specificity and reduce noise by using TF knockout mice as a critical control for ChIP-seq data experiments. Additionally, KOIN can identify 'hyper ChIPable regions' as another source of false-positive signals. As the use of the KOIN algorithm reduces false-positive results and thereby prevents misinterpretation of ChIP-seq data, it should be considered as the gold standard for future ChIP-seq analyses, particularly when developing ChIP-assays with novel antibody reagents.

Indexed as

AlgorithmsAnimalsBinding SitesChromatin ImmunoprecipitationHigh-Throughput Nucleotide SequencingMice, Inbred C57BLMice, KnockoutModels, AnimalNucleotide MotifsSequence Analysis, DNATranscription FactorsTranscription Factors

Identifiers

PMID25378309
PMCPMC4245947
OpenAlexW2132383989

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.