ArticleBMC research notes2015
A property-based analysis of human transcription factors.
Article in BMC research notes, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Human-specific lncRNAs contributed critically to human evolution by distinctly regulating gene expression.eLife · 2026Article
- Article
- Cancer-driving mutations are enriched in genic regions intolerant to germline variation.Science advances · 2022Article
- Extended transcriptome analysis reveals genome-wide lncRNA-mediated epigenetic dysregulation in colorectal cancer.Computational and structural biotechnology journal · 2020Article
- Predicting transcription factor binding motifs from DNA-binding domains, chromatin accessibility and gene expression data.Nucleic acids research · 2017Article
- Feature-based classification of human transcription factors into hypothetical sub-classes related to regulatory function.BMC bioinformatics · 2016Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTranscription factors are essential proteins for regulating gene expression. This regulation depends upon specific features of the transcription factors, including how they interact with DNA, how they interact with each other, and how they are post-translationally modified. Reliable information about key properties associated with transcription factors will therefore be useful for data analysis, in particular of data from high-throughput experiments.
resultsWe have used an existing list of 1978 human proteins described as transcription factors to make a well-annotated data set, which includes information on Pfam domains, DNA-binding domains, post-translational modifications and protein-protein interactions. We have then used this data set for enrichment analysis. We have investigated correlations within this set of features, and between the features and more general protein properties. We have also used the data set to analyze previously published gene lists associated with cell differentiation, cancer, and tissue distribution.
conclusionsThe study shows that well-annotated feature list for transcription factors is a useful resource for extensive data analysis; both of transcription factor properties in general and of properties associated with specific processes. However, the study also shows that such analyses are easily biased by incomplete coverage in experimental data, and by how gene sets are defined.
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Registered trials
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