Evidence map›Paper›PMID 35664802›Full record

ArticleNAR genomics and bioinformatics2022

ePeak: from replicated chromatin profiling data to epigenomic dynamics.

Maëlle Daunesse, Rachel Legendre, Hugo Varet, Adrien Pain, Claudia Chica

Abstract read
In one paragraph

Article in NAR genomics and bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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.

Maëlle DaunesseBioinformatics and Biostatistics Hub, Institut Pasteur, Université de Paris, Paris F-75015, France.
Rachel LegendreBioinformatics and Biostatistics Hub, Institut Pasteur, Université de Paris, Paris F-75015, France.
Hugo VaretBioinformatics and Biostatistics Hub, Institut Pasteur, Université de Paris, Paris F-75015, France.
Adrien PainBioinformatics and Biostatistics Hub, Institut Pasteur, Université de Paris, Paris F-75015, France.
Claudia ChicaBioinformatics and Biostatistics Hub, Institut Pasteur, Université de Paris, Paris F-75015, France.ORCID https://orcid.org/0000-0002-3758-640X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We present ePeak, a Snakemake-based pipeline for the identification and quantification of reproducible peaks from raw ChIP-seq, CUT&RUN and CUT&Tag epigenomic profiling techniques. It also includes a statistical module to perform tailored differential marking and binding analysis with state of the art methods. ePeak streamlines critical steps like the quality assessment of the immunoprecipitation, spike-in calibration and the selection of reproducible peaks between replicates for both narrow and broad peaks. It generates complete reports for data quality control assessment and optimal interpretation of the results. We advocate for a differential analysis that accounts for the biological dynamics of each chromatin factor. Thus, ePeak provides linear and nonlinear methods for normalisation as well as conservative and stringent models for variance estimation and significance testing of the observed marking/binding differences. Using a published ChIP-seq dataset, we show that distinct populations of differentially marked/bound peaks can be identified. We study their dynamics in terms of read coverage and summit position, as well as the expression of the neighbouring genes. We propose that ePeak can be used to measure the richness of the epigenomic landscape underlying a biological process by identifying diverse regulatory regimes.

Identifiers

PMID35664802
PMCPMC9154330

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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.