ArticleNucleic acids research2017
TFBSbank: a platform to dissect the big data of protein-DNA interaction in human and model species.
Article in Nucleic acids research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Peak Scores Significantly Depend on the Relationships between Contextual Signals in ChIP-Seq Peaks.International journal of molecular sciences · 2024Article
- Mechanisms and biotechnological applications of transcription factors.Synthetic and systems biotechnology · 2023Review
- A new method to synthesize multiple gRNA libraries and functional mapping of mammalian H3K4me3 regions.Nucleic acids research · 2023Article
- SPEED: Single-cell Pan-species atlas in the light of Ecology and Evolution for Development and Diseases.Nucleic acids research · 2023Article
- hTFtarget: A Comprehensive Database for Regulations of Human Transcription Factors and Their Targets.Genomics, proteomics & bioinformatics · 2020Article
- ChIPSummitDB: a ChIP-seq-based database of human transcription factor binding sites and the topological arrangements of the proteins bound to them.Database : the journal of biological databases and curation · 2020Article
- Single-cell RNA-seq reveals dynamic transcriptome profiling in human early neural differentiation.GigaScience · 2018Article
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4 authors.
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Abstract
Genome-wide transcription factors (TFs) binding data has been extensively generated in the past few years, which poses a great challenge to data interpretation. Therefore, comprehensive and dedicated functional annotation databases for TF-DNA interaction are in great demands to manage, explore and utilize those invaluable data resources. Here, we constructed a platform 'TFBSbank' which houses the annotation of 1870 chromatin immunoprecipitation (ChIP) datasets of 585 TFs in five species (human, mouse, fly, worm and yeast). There are mainly five functional modules in TFBSbank aimed at characterizing ChIP peaks, identifying putative targets, predicting TF responsive enhancers, revealing potential cofactors/collaborators and discovering enriched TF motifs. TFBSbank has two distinctive features compared to the existing databases. Firstly, we provided putative cofactors/collaborators analysis (for Drosophila melanogaster), as they are crucial for the in vivo functions of TFs. Additionally, this database predicted the enrichment of both known and de novo motifs based on ChIP data. TFBSbank is freely accessible at http://tfbsbank.co.uk.
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