Dealing with different conceptions of pollution in the Gene Regulation Knowledge Commons

Chatterjee, Anamika; Kuiper, Martin; Swierstra, Tsjalling

doi:10.1016/j.bbagrm.2021.194779

Cited by 3 publications

(1 citation statement)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such curation efforts are usually incorporated in databases such as IntAct ( 22 ), SIGNOR ( 23 ) and TRRUST ( 24 ). While being very attractive for their high quality, manual curations are hard to come by since curation is a generally cumbersome task, and curated databases rarely overlap due to the different curation standards and protocols ( 15 , 25 ). For that, curation efforts can be significantly enhanced with the aid of text-mining for the identification of TF–gene interactions, as previously shown ( 26 ).…”

Section: Introductionmentioning

confidence: 99%

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

Müller-Dott,

Tsirvouli,

Vazquez

et al. 2023

Nucleic Acids Research

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Müller-Dott,

Tsirvouli,

Vazquez

et al. 2023

Nucleic Acids Research

View full text Add to dashboard Cite

show abstract

TFCheckpoint database update, a cross-referencing system for transcription factors from human, mouse and rat

Acencio,

Vazquez,

Chawla

et al. 2023

Nucleic Acids Research

View full text Add to dashboard Cite

Prior knowledge about DNA-binding transcription factors (dbTFs), transcription co-regulators (coTFs) and general transcriptional factors (GTFs) is crucial for the study and understanding of the regulation of transcription. This is reflected by the many publications and database resources describing knowledge about TFs. We previously launched the TFCheckpoint database, an integrated resource focused on human, mouse and rat dbTFs, providing users access to a comprehensive overview of these proteins. Here, we describe TFCheckpoint 2.0 (https://www.tfcheckpoint.org/index.php), comprising 13 collections of dbTFs, coTFs and GTFs. TFCheckpoint 2.0 provides an easy and versatile cross-referencing system for users to view and download collections that may otherwise be cumbersome to find, compare and retrieve.

show abstract

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

Müller‐Dott

Tsirvouli

Vázquez

et al. 2023

Preprint

View full text Add to dashboard Cite

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 1,183 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 investigating hallmarks of TF activity profiles inferred from the transcriptomes of three different cancer types. Overall, the CollecTRI-derived TF regulons enable the accurate and comprehensive estimation of TF activities and thereby help to interpret transcriptomics data.

show abstract

Dealing with different conceptions of pollution in the Gene Regulation Knowledge Commons

Cited by 3 publications

References 45 publications

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

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

TFCheckpoint database update, a cross-referencing system for transcription factors from human, mouse and rat

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

Contact Info

Product

Resources

About