ArticlePloS one2010
Predicting DNA-binding specificities of eukaryotic transcription factors.
Article in PloS one, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.
- Improved linking of motifs to their TFs using domain information.Bioinformatics (Oxford, England) · 2020Pooled it
- Predicting transcription factor binding motifs from DNA-binding domains, chromatin accessibility and gene expression data.Nucleic acids research · 2017Article
- An overview of the prediction of protein DNA-binding sites.International journal of molecular sciences · 2015Review
- Article
- TFpredict and SABINE: sequence-based prediction of structural and functional characteristics of transcription factors.PloS one · 2013Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Today, annotated amino acid sequences of more and more transcription factors (TFs) are readily available. Quantitative information about their DNA-binding specificities, however, are hard to obtain. Position frequency matrices (PFMs), the most widely used models to represent binding specificities, are experimentally characterized only for a small fraction of all TFs. Even for some of the most intensively studied eukaryotic organisms (i.e., human, rat and mouse), roughly one-sixth of all proteins with annotated DNA-binding domain have been characterized experimentally. Here, we present a new method based on support vector regression for predicting quantitative DNA-binding specificities of TFs in different eukaryotic species. This approach estimates a quantitative measure for the PFM similarity of two proteins, based on various features derived from their protein sequences. The method is trained and tested on a dataset containing 1 239 TFs with known DNA-binding specificity, and used to predict specific DNA target motifs for 645 TFs with high accuracy.
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