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PreprintCited by 1,122Genomics and Chromatin DynamicsGene Regulatory Network AnalysisBioinformatics and Genomic Networks

Benchmark and integration of resources for the estimation of human transcription factor activities

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Luz García‐Alonso, Christian H. Holland, Mahmoud M. Ibrahim, Dénes Türei, Julio Sáez-Rodríguez·2019·Genome Research
DOI 10.1101/gr.240663.118PMC PMC6673718

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Abstract

The prediction of transcription factor (TF) activities from the gene expression of their targets (i.e., TF regulon) is becoming a widely used approach to characterize the functional status of transcriptional regulatory circuits. Several strategies and data sets have been proposed to link the target genes likely regulated by a TF, each one providing a different level of evidence. The most established ones are (1) manually curated repositories, (2) interactions derived from ChIP-seq binding data, (3) in silico prediction of TF binding on gene promoters, and (4) reverse-engineered regulons from large gene expression data sets. However, it is not known how these different sources of regulons affect the TF activity estimations and, thereby, downstream analysis and interpretation. Here we compared the accuracy and biases of these strategies to define human TF regulons by means of their ability to predict changes in TF activities in three reference benchmark data sets. We assembled a collection of TF–target interactions for 1541 human TFs and evaluated how different molecular and regulatory properties of the TFs, such as the DNA-binding domain, specificities, or mode of interaction with the chromatin, affect the predictions of TF activity. We assessed their coverage and found little overlap on the regulons derived from each strategy and better performance by literature-curated information followed by ChIP-seq data. We provide an integrated resource of all TF–target interactions derived through these strategies, with confidence scores, as a resource for enhanced prediction of TF activities.

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Authors
García‐Alonso, L., Holland, C. H., Ibrahim, M. M., Türei, D., & Sáez-Rodríguez, J.
Affiliations
  • European Bioinformatics Institute (GB)
  • Heidelberg University (DE)
  • RWTH Aachen University (DE)
ORCID
Venue
Genome Research
Publisher
Cold Spring Harbor Laboratory
Published
2019-07-24
Volume
29
Issue
8
Pages
1363-1375
ISSN
  • 1088-9051 (ISSN-L)
  • 1549-5469
DOI
10.1101/gr.240663.118
PMC
PMC6673718
OpenAlex
W2964315448
Cited by
1,122
References
9
Citations by year
2026: 221 · 2025: 192 · 2024: 240 · 2023: 180 · 2022: 146 · 2021: 88 · 2020: 43 · 2019: 11
Type
posted content
Language
en
Open access
Licence
other-oa
Locations
  • Genome Research (publishedVersion, other-oa)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • HighWire Press Open Archive (submittedVersion)
  • RWTH Publications (RWTH Aachen) (submittedVersion, other-oa)
  • Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
  • RWTH Publications (RWTH Aachen)
Funders
  • Open Targets (OTAR016)
  • Germany Federal Ministry of Education and Research (FKZ 031L0041)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords
transcription factor regulons, human transcription factors, confidence scores
MeSH terms
Binding Sites, Chromatin, DNA, Neoplasm, Humans, Neoplasm Proteins, Neoplasms, Promoter Regions, Genetic, Protein Binding, Transcription Factors, Transcription, Genetic, Regulon, Computational Biology, Benchmarking, Chromatin Immunoprecipitation, Gene Regulatory Networks, Datasets as Topic

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