AnimalTFDB: a comprehensive animal transcription factor database

By Hong-Mei Zhang, Hu Chen, Wei Liu, Hui Liu, Jing Gong, Huili Wang, An-Yuan Guo. Nucleic Acids Res., 2011

295 citations recorded by the source corpus.

Transcription factors (TFs) are proteins that bind to specific DNA sequences, thereby playing crucial roles in gene-expression regulation through controlling the transcription of genetic information from DNA to RNA. Transcription cofactors and chromatin remodeling factors are also essential in the gene transcriptional regulation. Identifying and annotating all the TFs are primary and crucial steps for illustrating their functions and understanding the transcriptional regulation. In this study, based on manual literature reviews, we collected and curated 72 TF families for animals, which is currently the most complete list of TF families in animals. Then, we systematically characterized all the TFs in 50 animal species and constructed a comprehensive animal TF database, AnimalTFDB. To better serve the community, we provided detailed annotations for each TF, including basic information, gene structure, functional domain, 3D structure hit, Gene Ontology, pathway, protein–protein interaction, paralogs, orthologs, potential TF-binding sites and targets. In addition, we collected and annotated transcription cofactors and chromatin remodeling factors. AnimalTFDB has a user-friendly web interface with multiple browse and search functions, as well as data downloading. It is freely available at http://www.bioguo.org/AnimalTFDB/.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b275907df3b9908624858bbd8e54018bcb2e1c71

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

High-throughput prediction of RNA, DNA and protein binding regions mediated by intrinsic disorder (2015, methodology): “We further assessed these predictions by quantifying an overlap between the disordered RNA- and DNAbinding proteins predicted by DisoRDPbind and the native RNA- and DNA-binding proteins from Gene Ontology (53) (GO RNA and GO DNA sets), RBPDB(54), animalTFDB(55) (Figure 3A) and binding proteins colle”
An RNA-Sequencing Transcriptome and Splicing Database of Glia, Neurons, and Vascular Cells of the Cerebral Cortex (2014, methodology): “To identify cell type-enriched transcription factors, we filtered our data with a previously annotated transcription factor database (Zhang et al., 2012), and top enriched transcription factors of each cell type were identified as described above.”
Single-Cell Transcriptomics Reveals a Population of Dormant Neural Stem Cells that Become Activated upon Brain Injury. (2015, unknown): “Transcriptional Control of NSC Activation To uncover putative transcriptional regulators of the quiescent and active states, we subjected the 92 NSC transcriptomes to PCA using only the expression values of transcription factors (TFs) (Zhang et al., 2012).”
Single cell transcriptomes from human kidneys reveal the cellular identity of renal tumors (2018, unknown): “S1 to S19 Tables S1 to S12 References (10–46) Data S1 1 February 2018; accepted 2 July 2018 10.1126/science.aat1699 Young et al., Science 361, 594–599 (2018) 10 August 2018 6 of 6”
Single-cell RNA-seq with Waterfall Reveals Molecular Cascades underlying Adult Neurogenesis (2015, unknown): “We identified TFs using public databases (Zhang et al., 2012).”
AnimalTFDB 3.0: a comprehensive resource for annotation and prediction of animal transcription factors (2018, methodology): “The AnimalTFDB was firstly built in 2011 (7) and in 2015 it was updated to AnimalTFDB v2.”