TRANSFAC

TRANSFAC 2.0

The gold standard in the area of transcriptional regulation.

Appreciated by millions of TRANSFAC® users, the MATCH utility with significantly extended functionality now comes in a bundle with TRANSFAC® 2.0 as the new MATCH Suite tool.

 

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 MATCH Suite tool or 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 MATCH Suite tool 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.)

Enhancer reports displaying genes with which promoters the enhancer interacts, tissues and cell types/lines the enhancer is active in, and genomic regions such as histone modification sequences, DNase I hypersensitivity sites, and transcription factor binding sites that overlap with the enhancer.

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 48,000 transcription factor (and 1,700 miRNA) reports, a subset of which provide GO functional assignments, disease associations and expression pattern assignments.

More than 68,000 manually annotated transcription factor–site interactions; plus more than 74,000 miRNA-target site interactions.

More than 2,000 high-throughput TFBS ChIP experiment reports comprising 91M transcription factor bound fragments/intervals, many of which have been annotated with the best-scoring binding site and neighbouring genes, as well as 161 DNase hypersensitivity ChIP-Seq experiments comprising 15M fragments and 1M histone modification fragments.

More than 10,000 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 enhancers, 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.

Perform automatized analysis of the MATCH Suite to identify the transcription factors regulating the genes of your interest.

Videos

TRANSFAC® intro video – this video is a general introduction to the online TRANSFAC® database. It shows how to operate in the basic interface and perform searches in the TRANSFAC® database.

Introduction to Match™ video – this video shows how to perform search for putative transcription factor binding sites in the TRANSFAC® database by using the Match™ tool.

Match™ Tool in TRANSFAC® Interface video – this video demonstrates how Match™ tool for transcription factor binding site prediction can be launched from the TRANSFAC® interface.

The Match™ tool can be launched starting from:

– a list of genes

– a list of transcripts

– DNA sequences in a FASTA format

– genomic intervals in BED format

FMatch Tool in TRANSFAC® Interface video – this video demonstrates how FMatch analysis can be launched from TRANSFAC® interface.

Fmatch is a tool that searches for enriched binding sites in a set of promoters versus a background set.

The analysis can be launched starting from:

– a list of genes

– a list of transcripts

– DNA sequences in a FASTA format

– genomic intervals in BED format

 

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    Current TRANSFAC® release

    TRANSFAC® release 2022.1

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

    • MATCH Suite: Single gene analysis

    In its release 2.0 MATCH Suite now supports gene regulation analysis for single genes in addition to the previously introduced gene set studies. Specify the gene of your interest using its gene symbol, Entrez or Ensembl ID, and find the transcription factors responsible for regulation of your gene via its promoter and enhancers/silencers.

    You can optionally select the tissue of your interest, the promoter type and region to be used for the analysis, or the GO terms to narrow down the identified transcription factors to the ones belonging to the chosen biological processes.

    The analysis results will provide you with lists transcription factors (TFs) and respective binding sites that were predicted to regulate the specified gene in the selected conditions. Interactive visualization in genome browser will allow to observe the predicted and experimentally proven TF binding sites in the promoter and enhancers/silencers of your gene in the specified tissue (if any was selected). Analysis report will provide you with comprehensive information on the performed analysis and the obtained results.

    • Extended options to use search results as input for further queries

    Transcription factors that bind to regulatory regions of specific genes in ChIP-Seq experiments, can now be queried directly from a gene/protein search result. This extends the possibility to identify regulatory factors for a gene beyond the ones found through DNA binding sites from low-throughput experiments.

    • Integration of new human ChIP-Seq experiments from ENCODE

    17 new human transcription factor binding site ChIP-Seq experiments released by the ENCODE phase 4 project have been integrated. The data sets comprise 38,319 fragments bound by 17 distinct transcription factors, of which 10 factors were not yet covered by ChIP-Seq data in TRANSFAC.
    For 14 of the sets, an existing positional weight matrix for the respective transcription factor was used together with the MATCH tool to predict altogether 28,204 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.

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        Promoter analysis

        Learn how to perform promoter analysis with TRANSFAC® in the geneXplain platform or investigate the MATCH Suite for fully automatized search for transcription factors regulating the gene set of your interest.

        Videos

        TRANSFAC intro video.mp4

        MATCH intro_ video.mp4

        FMatch_Tool_in_TRANSFAC_Interface_video

        Match_Tool_in_TRANSFAC_Interface_video

        TRANSFAC® database and its applications

        TRANSFAC 2.0: fresh look at the gold standard of gene regulation

        Transcription factors and their classification

        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, 1340007. 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 (download)
        TRANSFAC® Release (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.

        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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