Evidence map›Paper›PMID 42203861›Full record

ArticleNature methods2026

TADShop: systematic benchmarking and identification of topologically associating domains.

Pumin Li, Andras Hatos, Miljan Petrovic, Gian Marco Franceschini, Luca Nanni, Daniele Tavernari, Giovanni Ciriello

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Article in Nature methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Pumin Li *Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
Andras Hatos *Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.ORCID http://orcid.org/0000-0001-9224-9820
Miljan PetrovicDepartment of Computational Biology, University of Lausanne, Lausanne, Switzerland.ORCID http://orcid.org/0000-0001-8259-4399
Gian Marco FranceschiniDepartment of Computational Biology, University of Lausanne, Lausanne, Switzerland.ORCID http://orcid.org/0000-0002-7391-3535
Luca NanniDepartment of Computational Biology, University of Lausanne, Lausanne, Switzerland.
Daniele TavernariDepartment of Computational Biology, University of Lausanne, Lausanne, Switzerland.ORCID http://orcid.org/0000-0001-5981-7363
Giovanni CirielloDepartment of Computational Biology, University of Lausanne, Lausanne, Switzerland. giovanni.ciriello@unil.ch.ORCID http://orcid.org/0000-0003-2021-8683

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Topologically associating domains (TADs) are structural units of chromatin organization. Their definition and identification rely on computational analyses of chromosome conformation capture data. Here we systematically assessed and compared 43 TAD identification strategies, none of which excelled in all tests, often displaying conflicting results. To benefit from the strengths and overcome the weaknesses of individual tools, we developed a dynamic programming framework to integrate the results of top-performing methods into a consensus list of TADs (ConsensusTAD). ConsensusTAD outperformed individual tools, and it can be applied to integrate results from any set of current or future TAD callers. To facilitate TAD identification and benchmarking, we developed TADShop ( https://tadshop.unil.ch/ ), a web service that enables the comparison and use of TAD callers through an intuitive graphic user interface. By facilitating rigorous benchmarking and adoption of best performing tools, TADShop provides a standardized platform to foster robust and reproducible analyses of chromatin organization.

Indexed as

ChromatinComputational BiologySoftwareAlgorithmsAnimalsBenchmarkingHumansChromatin

Identifiers

What OpenQuestion holds

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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.