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Benchmark and integration of resources for the estimation of human transcription factor activities.
- Source :
-
Genome research [Genome Res] 2019 Aug; Vol. 29 (8), pp. 1363-1375. Date of Electronic Publication: 2019 Jul 24. - Publication Year :
- 2019
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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.<br /> (© 2019 Garcia-Alonso et al.; Published by Cold Spring Harbor Laboratory Press.)
- Subjects :
- Binding Sites
Chromatin chemistry
Chromatin metabolism
Chromatin Immunoprecipitation
Computational Biology methods
DNA, Neoplasm metabolism
Datasets as Topic
Gene Regulatory Networks
Humans
Neoplasm Proteins metabolism
Neoplasms classification
Neoplasms metabolism
Neoplasms pathology
Promoter Regions, Genetic
Protein Binding
Regulon
Transcription Factors metabolism
Benchmarking
DNA, Neoplasm genetics
Neoplasm Proteins genetics
Neoplasms genetics
Transcription Factors genetics
Transcription, Genetic
Subjects
Details
- Language :
- English
- ISSN :
- 1549-5469
- Volume :
- 29
- Issue :
- 8
- Database :
- MEDLINE
- Journal :
- Genome research
- Publication Type :
- Academic Journal
- Accession number :
- 31340985
- Full Text :
- https://doi.org/10.1101/gr.240663.118