ArticleBioinformatics (Oxford, England)2020
T-Gene: improved target gene prediction.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Article
- Article
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- Persistent open chromatin state in early-life stress-activated cells of the VTA.Scientific reports · 2025Article
- Prediction of target genes and functional types of cis-regulatory modules in the human genome reveals their distinct properties.BMC biology · 2025Article
- Deciphering lineage-relevant gene regulatory networks during endoderm formation by InPheRNo-ChIP.Briefings in bioinformatics · 2024Article
- DeepCBA: A deep learning framework for gene expression prediction in maize based on DNA sequences and chromatin interactions.Plant communications · 2024Article
- Systematic dissection of sequence features affecting binding specificity of a pioneer factor reveals binding synergy between FOXA1 and AP-1.Molecular cell · 2024Article
- PRDM6 promotes medulloblastoma by repressing chromatin accessibility and altering gene expression.Scientific reports · 2024Article
- Epithelial cells maintain memory of prior infection with Streptococcus pneumoniae through di-methylation of histone H3.Nature communications · 2024Article
- Approaches for Benchmarking Single-Cell Gene Regulatory Network Methods.Bioinformatics and biology insights · 2024Review
- Systematic Dissection of Sequence Features Affecting the Binding Specificity of a Pioneer Factor Reveals Binding Synergy Between FOXA1 and AP-1.bioRxiv : the preprint server for biology · 2023Article
- Target Finder of Transcription Factor (TFoTF): a novel tool to predict transcription factor-targeted genes in cancer.Molecular oncology · 2023Article
- Whole-genome sequencing reveals an association between small genomic deletions and an increased risk of developing Parkinson's disease.Experimental & molecular medicine · 2023Article
- Machine Learning-Based Epigenetic Classifiers for Axillary Staging of Patients with ER-Positive Early-Stage Breast Cancer.Annals of surgical oncology · 2022Article
- proChIPdb: a chromatin immunoprecipitation database for prokaryotic organisms.Nucleic acids research · 2022Article
- Epigenetic encoding, heritability and plasticity of glioma transcriptional cell states.Nature genetics · 2021Article
- Profiling chromatin accessibility responses in human neutrophils with sensitive pathogen detection.Life science alliance · 2021Article
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Authors and funding
4 authors.
Funding
Abstract
motivationIdentifying the genes regulated by a given transcription factor (TF) (its 'target genes') is a key step in developing a comprehensive understanding of gene regulation. Previously, we developed a method (CisMapper) for predicting the target genes of a TF based solely on the correlation between a histone modification at the TF's binding site and the expression of the gene across a set of tissues or cell lines. That approach is limited to organisms for which extensive histone and expression data are available, and does not explicitly incorporate the genomic distance between the TF and the gene.
resultsWe present the T-Gene algorithm, which overcomes these limitations. It can be used to predict which genes are most likely to be regulated by a TF, and which of the TF's binding sites are most likely involved in regulating particular genes. T-Gene calculates a novel score that combines distance and histone/expression correlation, and we show that this score accurately predicts when a regulatory element bound by a TF is in contact with a gene's promoter, achieving median precision above 60%. T-Gene is easy to use via its web server or as a command-line tool, and can also make accurate predictions (median precision above 40%) based on distance alone when extensive histone/expression data is not available for the organism. T-Gene provides an estimate of the statistical significance of each of its predictions. AVAILABILITY AND IMPLEMENTATION: The T-Gene web server, source code, histone/expression data and genome annotation files are provided at http://meme-suite.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.