TRANSFAC® release 2023.1
The TRANSFAC® database on transcription factors, their genomic binding sites and DNA-binding motifs (PWMs), contains these new data features:
· JASPAR 2022 matrix library integration
New position frequency matrices from the JASPAR 2022 release either added as matrix entries (375 cases) or hyperlinked to existing counterparts in the TRANSFAC matrix library.
· Additional interactions between human transcription factors
13,100 new human transcription factor interactions have been included from BIOGRID.
· Integration of new human ChIP-Seq experiments from ENCODE
162 new human transcription factor binding site ChIP-Seq experiments released by the ENCODE phase 4 project have been integrated. The data sets comprise 1,700,273 fragments bound by 143 distinct transcription factors, of which 39 factors were not yet covered by ChIP-Seq data in TRANSFAC.
For 134 of the sets, an existing positional weight matrix for the respective transcription factor was used together with the MATCH tool to predict altogether 1,316,751 best binding sites inside the fragments.
Predicted best binding sites as well as complete fragments are available in FASTA and BED format via the ChIP Experiment Reports, as are lists of genes in a distance range to the fragments as specified by the user.
· New matrices derived from ENCODE ChIP-Seq data
12 new positional weight matrices for yet uncovered human transcription factors have been generated from new ENCODE phase 4 ChIP-Seq data and integrated into the TRANSFAC matrix library.
Most transcription factors (TFs) possess a DNA-binding domain (DBD), which mediates the recognition of specific, short DNA sequence elements in promoter, enhancer, etc. In order to approach the problem of deciphering the underlying DNA-protein recognition code, we have completely revised an earlier TF classification scheme (1,2) by adapting it to the wealth of data that were reported during the last ten years (TFClass; 3-5). TFClass has been implemented at the Dept. of Bioinformatics at the University Medical Center Göttingen (3,6).
Part of this work was done in the context of the Syscol
project, where our partner at the Karolinska institute (Prof. J. Taipale and his team) have characterized the DNA-binding profiles of more than 400 mammalian TFs (7). It will be tempting to compare the similarities of their matrices with the DBD classification reported here, and with our own approaches to classify DNA-binding profiles (8).
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- Wingender, E., Schoeps, T., Haubrock, M., Dönitz, J. (2015) TFClass: a classification of human transcription factors and their rodent orthologs. Nucleic Acids Res. 43, D97-D102. Link
- Stegmaier, P., Kel, A., Wingender, E., Borlak, J. (2013) A discriminative approach for unsupervised clustering of DNA sequence motifs. PLoS Comput. Biol. 9, e1002958.
- Jolma, A., et al. (2013) DNA-Binding Specificities of Human Transcription Factors. Cell 152, 327–339. Link
- Wingender, E. (2013) Criteria for an updated classification of human transcription factor DNA-binding domains. J. Bioinform. Comput. Biol. 11, 1340007. Link
- Wingender, E., Schoeps, T., Dönitz, J. (2013) TFClass: An expandable hierarchical classification of human transcription factors. Nucleic Acids Res. 41, D165-D170. Link
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- Wingender, E. (1997) Classification scheme of eukaryotic transcription factors. Mol. Biol. Engl. Tr. 31, 498-512. Link
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