Evidence map›Paper›PMID 33347581›Full record

ArticleNucleic acids research2021

To mock or not: a comprehensive comparison of mock IP and DNA input for ChIP-seq.

Jinrui Xu, Michelle M Kudron, Alec Victorsen, Jiahao Gao, Haneen N Ammouri, Fabio C P Navarro, Louis Gevirtzman, Robert H Waterston, Kevin P White, Valerie Reinke and 1 more

Abstract readComparative Study
In one paragraph

Article in Nucleic acids research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
  2. Binding profiles for 961Genome research · 2024
    Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Differential Analysis of Protein-DNA Binding Using ChIP-Seq Data.Methods in molecular biology (Clifton, N.J.) · 2024
    Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Review
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

11 authors.

Jinrui XuProgram in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.
Michelle M KudronDepartment of Genetics, Yale University, New Haven, CT 06520, USA.
Alec VictorsenInstitute for Genomics and Systems Biology, Department of Human Genetics, University of Chicago, IL 60637, USA.
Jiahao GaoProgram in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.
Haneen N AmmouriInstitute for Genomics and Systems Biology, Department of Human Genetics, University of Chicago, IL 60637, USA.
Fabio C P NavarroProgram in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.
Louis GevirtzmanDepartment of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA.
Robert H WaterstonDepartment of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA.
Kevin P WhiteInstitute for Genomics and Systems Biology, Department of Human Genetics, University of Chicago, IL 60637, USA.
Valerie ReinkeDepartment of Genetics, Yale University, New Haven, CT 06520, USA.
Mark GersteinProgram in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.

Funding

Resource ProjectU41HG007355 · NHGRI · UNIVERSITY OF WASHINGTON · PI WATERSTON, ROBERT H · 2013 to 2021
$20.8M
NHGRI NIH HHS U41 HG007355
6 · The paper itself

Abstract

Chromatin immunoprecipitation (IP) followed by sequencing (ChIP-seq) is the gold standard to detect transcription-factor (TF) binding sites in the genome. Its success depends on appropriate controls removing systematic biases. The predominantly used controls, i.e. DNA input, correct for uneven sonication, but not for nonspecific interactions of the IP antibody. Another type of controls, 'mock' IP, corrects for both of the issues, but is not widely used because it is considered susceptible to technical noise. The tradeoff between the two control types has not been investigated systematically. Therefore, we generated comparable DNA input and mock IP experiments. Because mock IPs contain only nonspecific interactions, the sites predicted from them using DNA input indicate the spurious-site abundance. This abundance is highly correlated with the 'genomic activity' (e.g. chromatin openness). In particular, compared to cell lines, complex samples such as whole organisms have more spurious sites-probably because they contain multiple cell types, resulting in more expressed genes and more open chromatin. Consequently, DNA input and mock IP controls performed similarly for cell lines, whereas for complex samples, mock IP substantially reduced the number of spurious sites. However, DNA input is still informative; thus, we developed a simple framework integrating both controls, improving binding site detection.

Indexed as

AntibodiesBinding SitesCell LineChromatin Immunoprecipitation SequencingDNAHumansTranscription FactorsAntibodiesDNATranscription Factors

Identifiers

PMID33347581
PMCPMC7897498

What OpenQuestion holds

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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.