Evidence map›Paper›PMID 37843125›Full record

ArticleNucleic acids research2023

Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities.

Sophia Müller-Dott, Eirini Tsirvouli, Miguel Vazquez, Ricardo O Ramirez Flores, Pau Badia-I-Mompel, Robin Fallegger, Dénes Türei, Astrid Lægreid, Julio Saez-Rodriguez

Open access · goldAbstract read
In one paragraph

Article in Nucleic acids research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 244 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
244citing papers in PubMed, 2 pooled it
44.3field-weighted citation impact, top 1% 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

244 citing papers in PubMed, 2 syntheses or guidelines pooled it, 288 citations in OpenAlex.

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184 more citing papers are in PubMed but not listed here.

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

9 authors at 3 institutions in 3 countries.

Sophia Müller-DottHeidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany.ORCID 0000-0002-9710-1865
Eirini TsirvouliDepartment of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway.
Miguel VazquezBarcelona Supercomputing Center, Barcelona, Spain.ORCID 0000-0002-5713-1058
Ricardo O Ramirez FloresHeidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany.ORCID 0000-0003-0087-371X
Pau Badia-I-MompelHeidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany.ORCID 0000-0002-1004-3923
Robin FalleggerHeidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany.ORCID 0000-0003-3264-6028
Dénes TüreiHeidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany.
Astrid LægreidDepartment of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway.ORCID 0000-0002-3941-4257
Julio Saez-RodriguezHeidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany.ORCID 0000-0002-8552-8976
Heidelberg University · DENorwegian University of Science and Technology · NOBarcelona Supercomputing Center · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gene regulation plays a critical role in the cellular processes that underlie human health and disease. The regulatory relationship between transcription factors (TFs), key regulators of gene expression, and their target genes, the so called TF regulons, can be coupled with computational algorithms to estimate the activity of TFs. However, to interpret these findings accurately, regulons of high reliability and coverage are needed. In this study, we present and evaluate a collection of regulons created using the CollecTRI meta-resource containing signed TF-gene interactions for 1186 TFs. In this context, we introduce a workflow to integrate information from multiple resources and assign the sign of regulation to TF-gene interactions that could be applied to other comprehensive knowledge bases. We find that the signed CollecTRI-derived regulons outperform other public collections of regulatory interactions in accurately inferring changes in TF activities in perturbation experiments. Furthermore, we showcase the value of the regulons by examining TF activity profiles in three different cancer types and exploring TF activities at the level of single-cells. Overall, the CollecTRI-derived TF regulons enable the accurate and comprehensive estimation of TF activities and thereby help to interpret transcriptomics data.

Indexed as

Gene Expression RegulationRegulonTranscription FactorsGene Expression ProfilingGene Regulatory NetworksHumansReproducibility of ResultsTranscription Factors

Identifiers

PMID37843125
PMCPMC10639077
OpenAlexW4387661643

What Socratic 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 Socratic graph.