TRANSFAC

TRANSFAC

TRANSFAC® release 2019.3 is out now!

The gold standard in the area of transcriptional regulation.

TRANSFAC® is the database of eukaryotic transcription factors, their genomic binding sites and DNA-binding profiles. Dating back to a very early compilation, it has been carefully maintained and curated since then and became the gold standard in the field, which can be made use of when applying the geneXplain platform.

In particular its library of positional weight matrices is a unique collection of DNA-binding models, suitable for a comprehensive analysis of genomic sequences for potential transcription factor binding sites (TFBSs).

You can use TRANSFAC® as encyclopedia of transcriptional regulation, or as a tool to identify potential TFBSs. The latter can be done with the proven tool MatchTM, or with any of the respective modules in the geneXplain platform.

Structure

The core of TRANSFAC® comprises contents of two domains: One documents TF binding sites, usually in promoters or enhancers. The other describes the binding proteins (TFs).

Transfac conceptOn top of each of these two domains, an abstract view on its contents is provided:

Binding sites referring to the same TF are merged into a positional weight matrix. Such a matrix reflects the frequency with which each nucleotide is found in each position of this TF’s binding sites and, thus, the base preference in each position.

Transcription factors are subsumed to classes, based on the general properties of their DNA-binding domains. This early attempt has been expanded to a comprehensive TF classification, the latest version of which can be found here.

Key features

Interlinked reports connecting transcription factors, their experimentally-characterized binding sites and regulated genes, as well as promoter reports with mapped annotated TF-binding sites and high-throughput data (ChIP-seq etc.)

More than 70,000  site reports containing details from the primary literature for more than 300 species, with a focus on human, mouse, rat, yeast, and plants

More than 23,000 transcription factor (and 1,200 miRNA) reports, a subset of which provide GO functional assignments, disease associations and expression pattern assignments

More than 67,000 manually annotated transcription factor–site interactions; plus more than 57,000 miRNA-target site interactions

More than 2,000 high-throughput TFBS ChIP experiment reports  comprising 27M transcription factor bound fragments/intervals, many of which have been annotated with the best-scoring binding site and neighboring genes, as well as 161 DNase hypersensitivity ChIP-Seq experiments comprising 15M fragments and 1M histone modification fragments

More than 7,000 positional weight matrices, to be used by MatchTM, FMatch, CMsearch and a number of geneXplain bricks to predict TF binding sites.

More than 360,000 promoter reports for human and nine other organisms, including transcription start sites, CpG islands, single nucleotide polymorphisms (SNPs) and various other annotations

Including tools for TF-binding site prediction, de novo motif identification, matrix comparison and miRNA regulator identification.

Further, including a pathway visualization tool for building custom regulatory networks out of experimentally demonstrated factor-DNA and factor-factor interactions, as well as a functional analysis tool for identification of shared attributes in an analyzed gene set.

More detailed statistics can be obtained here.

Benefits

Quickly access detailed reports for transcription factors, their experimentally-characterized binding sites and regulated genes, and ChIP experiments without tedious and time consuming literature searches.

Predict transcription factor binding sites within a DNA sequence using your own or TRANSFAC’s positional weight matrices.

Build custom transcription regulatory networks.

Use TRANSFAC®‘s positional weight matrices as an integral part of the geneXplain platform.

New release TRANSFAC 2019.2

TRANSFAC® release 2019.2

The TRANSFAC® database on transcription factors, their genomic binding sites and DNA-binding motifs
(PWMs), contains these new data features:

  • Matrices for methylated binding motifs
    1,785 matrices for human transcription factors from Methyl-HT-SELEX and HT-SELEX experiments
    were added. They were published in “Yin, Y. et al, Impact of cytosine methylation on DNA binding
    specificities of human transcription factors, Science 2017 356(6337):eaaj2239, Pubmed PMID:
    28473536″.
    Based on the selection of representative methylated motifs, a specific matrix profile has been
    added to be used with MATCH.
  • Drosophila melanogaster promoters added
    19,152 promoters for fruit fly (Drosophila melanogaster) have been included based on data from
    Ensembl version 95 and our established virtual transcription start site calculation. Experimentally
    verified transcription factor binding sites and 3.6 million SNPs from the dbSNP database have
    been mapped to the promoter sequences.
  • Annotation of transcription factor binding sites based on sequence conservation
    Known transcription factor binding sites located in human, mouse, rat, or pig genomes
    were extracted from TRANSFAC® and highly conserved sites were retained. Given high
    conservation as a prerequisite, binding sites were annotated for the three other species in
    respective genomic location if not more than one mismatch was observed in the
    sequence alignment with the primary species. This resulted in 1,007 new binding site
    entries.
  • Integration of new human and fruit fly ChIP-Seq experiments from ENCODE, modENCODE and
    modERN
    21 new human transcription factor binding site ChIP-Seq experiments released by the ENCODE
    phase 3 and 4 project between July 2018 and December 2018 have been integrated. The data sets
    comprise 424,961 fragments bound by 15 distinct transcription factors.
    For Drosophila melanogaster, 470 transcription factor binding site ChIP-Seq experiments released
    by the modENCODE and modERN were added. The data sets include 2,050,835 fragments bound
    by 411 distinct transcription factors, all of which were not yet covered by TRANSFAC ChIP-Seq
    data.
    For 183 of the sets, an existing positional weight matrix for the respective transcription factor was
    used together with the MATCH tool to predict altogether 953,185 best binding sites inside the
    fragments.
  • Ensembl version update
    Genomic information for genes, promoters, and ChIP fragments for the species human, mouse,
    rat, pig, macaque, Drosophila, and Arabidopsis is now based on Ensembl release 95.

Free trial

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Please contact us and you will be provided with your free trial version.

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Promoter analysis

Learn more about promoter analysis with TRANSFAC® in the geneXplain platform.

Videos

TRANSFAC intro video.mp4

MATCH intro_ video.mp4

Transcription Factor Classification

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).

References

  1. Wingender, E., Schoeps, T., Haubrock, M., Krull, M. and Dönitz, J. (2018) TFClass: expanding the classification of human transcription factors to their mammalian orthologs. Nucleic Acids Res. 46, D343-D347. Link
  2. 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
  3. Stegmaier, P., Kel, A., Wingender, E., Borlak, J. (2013) A discriminative approach for unsupervised clustering of DNA sequence motifs. PLoS Comput. Biol. 9, e1002958.
  4. Jolma, A., et al. (2013) DNA-Binding Specificities of Human Transcription Factors. Cell, 152, 327–339. Link
  5. http://tfclass.bioinf.med.uni-goettingen.de
  6. http://www.edgar-wingender.de/huTF_classification.html
  7. Wingender, E. (2013) Criteria for an updated classification of human transcription factor DNA-binding domains. J. Bioinform. Comput. Biol. 11, in press. Link
  8. Wingender, E., Schoeps, T., Dönitz, J. (2013) TFClass: An expandable hierarchical classification of human transcription factors. Nucleic Acids Res. 41, D165-D170. Link
  9. Heinemeyer, T., Chen, X., Karas, H., Kel, A.E., Kel, O.V., Liebich, I., Meinhardt, T., Reuter, I., Schacherer, F., Wingender,E. (1999) Expanding the TRANSFAC database towards an expert system of regulatory molecular mechanisms. Nucleic Acids Res., 27, 318–322. Link
  10. Wingender, E. (1997) Classification scheme of eukaryotic transcription factors. Mol. Biol. Engl. Tr. 31, 498-512. Link

Information downloads

TRANSFAC® Statistics 2019.3 (download)
TRANSFAC® Release 2019.3 (download)
TRANSFAC® Statistics 2019.2 (download)
TRANSFAC® Release 2019.2 (download)
TRANSFAC® Statistics 2019.1 (download)
TRANSFAC® Release 2019.1 (download)
TRANSFAC® Flyer (download)
TRANSFAC® Documentation (download)
TRANSFAC® Video (at YouTube)
See also the TRANSFAC® entry at Wikipedia.
More about TRANSFAC as a scientific project and its history on the pages of Edgar Wingender.
TRANSFAC® is a registered trademark of QIAGEN.

Recent publications

Wingender, E., Schoeps, T., Haubrock, M., Krull, M. and Dönitz, J. (2018) TFClass: expanding the classification of human transcription factors to their mammalian orthologs. Nucleic Acids Res. 46, D343-D347. PubMed.

Kaplun, A., Krull, M., Lakshman, K., Matys, V., Lewicki, B., Hogan, J.D. (2016) Establishing and validating regulatory regions for variant annotation and expression analysis. BMC Genomics 17 (Suppl. 2):393. PubMed.

Wingender, E. (2008) The TRANSFAC project as an example of framework technology that supports the analysis of genomic regulation. Brief. Bioinform. 9:326-332. PubMed.

Matys, V., Kel-Margoulis, O.V., Fricke, E., Liebich, I., Land, S., Barre-Dirrie, A., Reuter, I., Chekmenev, D., Krull, M., Hornischer, K., Voss, N., Stegmaier, P., Lewicki-Potapov, B., Saxel, H., Kel, A.E., Wingender, E. (2006) TRANSFAC and its module TRANSCompel: transcriptional gene regulation in eukaryotes. Nucleic Acids Res. 34:D108-D110. PubMed.

Kel, A.E., Gössling, E., Reuter, I., Cheremushkin, E., Kel-Margoulis, O.V., Wingender, E. (2003) MATCH: A tool for searching transcription factor binding sites in DNA sequences. Nucleic Acids Res. 31:3576-3579. PubMed

Wingender, E., Dietze, P., Karas, H., Knüppel, R. (1996) TRANSFAC: a database on transcription factors and their DNA binding sites. Nucleic Acids Res. 24:238-241. PubMed

Knüppel, R., Dietze, P., Lehnberg, W., Frech, K., Wingender, E. (1994) TRANSFAC retrieval program: a network model database of eukaryotic transcription regulating sequences and proteins. J. Comput. Biol. 1:191-198. PubMed

Wingender, E. (1988) Compilation of transcription regulating proteins. Nucleic Acids Res. 16:1879-1902. PubMed
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