standards > terminology artifact > DOI:10.25504/FAIRsharing.XykycZ
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ready Transcription Factor Class

Abbreviation: TFClass


General Information
TFClass is a resource that classifies eukaryotic transcription factors (TFs) according to their DNA-binding domains. Combining information from different resources, manually checking the retrieved mammalian TF sequences and applying extensive phylogenetic analyses, >39,000 TFs from up to 41 mammalian species were assigned to the Superclasses, Classes, Families and Subfamilies of TFClass. TFClass provides corresponding sequence collections in FASTA format; sequence logos and phylogenetic trees at different classification levels; predicted TF binding sites for human, mouse, dog and cow genomes; and links to several external databases.



How to cite this record FAIRsharing.org: TFClass; Transcription Factor Class; DOI: https://doi.org/10.25504/FAIRsharing.XykycZ; Last edited: Dec. 19, 2019, 11:07 a.m.; Last accessed: Jun 01 2020 1:35 p.m.

Publication for citation  TFClass: expanding the classification of human transcription factors to their mammalian orthologs. Wingender E,Schoeps T,Haubrock M,Krull M,Donitz J; Nucleic Acids Res ; 2017; 10.1093/nar/gkx987;


This record is maintained by edgar.wingender

Record added: Nov. 7, 2019, 12:41 p.m.
Record updated: Nov. 7, 2019, 12:49 p.m. by The FAIRsharing Team.

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TFClass: expanding the classification of human transcription factors to their mammalian orthologs.

Wingender E,Schoeps T,Haubrock M,Krull M,Donitz J
Nucleic Acids Res 2017

View Paper (PubMed) View Publication


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Related Databases (2)
Human Protein Atlas
The Human Protein Atlas (HPA) portal is a publicly available database with millions of high-resolution images showing the spatial distribution of proteins in a number of different wild-type tissues, cancer types and human cell lines.

Signaling Pathways Project
The Signaling Pathways Project is an integrated 'omics knowledgebase based upon public, manually curated transcriptomic and cistromic (ChIP-Seq) datasets involving genetic and small molecule manipulations of cellular receptors, enzymes and transcription factors. Our goal is to create a resource where scientists can routinely generate research hypotheses or validate bench data relevant to cellular signaling pathways.

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