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.

 

Coffee break with TRANSFAC online sessions

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.
Site reports
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.
Transcription factor reports
More than 49,000 transcription factor reports (and 1,700 miRNA), a subset of which provide GO functional assignments, disease associations and expression pattern assignments.
Transcription factor–site interactions
More than 68,000 manually annotated transcription factor–site interactions; plus more than 74,000 miRNA-target site interactions.
TFBS ChIP-seq reports
More than 2,000 high-throughput experiments comprising 94M 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.
Positional weight matrices (PWMs)
More than 11,000 PWMs, to be used by MatchTM, FMatch, CMsearch and a number of geneXplain bricks to predict TF binding sites.
Promoter reports
More than 422,000 promoter reports for human and eleven other organisms, including transcription start sites, CpG islands, single nucleotide polymorphisms (SNPs) and various other annotations.
Transcription factor binding sites
Including tools for prediction, de novo motif identification, matrix comparison and miRNA regulator identification.
MATCH Suite – an integrated tool for TFBS search
With Match Suite you can identify transcription factor binding sites (TFBSs) enriched in the human, mouse, or rat promoters in focus, as well as in a single sequence for human genes. Additionally, Match Suite  enables to analyze functional enrichment of your gene set, find only transcription factors (TFs) expressed in the tissue of your interest, identify TFs belonging to certain functional categories, filter the predicted TFs by those intersecting with conservative regions of the genome and much more.

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
Out of experimentally demonstrated factor-DNA and factor-factor interactions.
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.

 

In addition to that, all our customers enjoy the following advantages or our products:

–       Technical and scientific support or your research: rapid answers to your questions from our professional support team will be always provided

–       Secure cloud space connected with online licenses that can be accessed from any location

–       Extensive manuals, documentation, examples, and tutorials available for all our products

–       Frequent releases and updates of our databases contents and software functionality

–       Ability to request personal training sessions

–       All our servers are running on CO2-neutral water or wind power

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

 

Recent applications

Selection of articles reporting about TRANSFAC applications:
  • Zhou L., Yang Y., Ye Y., Qiao Q., Mi Y., Liu H., Zheng Y., Wang Y., Liu M., Zhou Y. (2024). Notch1 signaling pathway promotes growth and metastasis of gastric cancer via modulating CDH5. Aging (Albany NY). 16(16):11893–11903. Link
  • Li F., Wang J., Li M., Zhang X., Tang Y., Song X., Zhang Y., Pei L., Liu J., Zhang C., Li X., Xu Y., Zhang Y. (2024). Identifying cell type-specific transcription factor-mediated activity immune modules reveal implications for immunotherapy and molecular classification of pan-cancer. Brief Bioinform. 25(5):bbae368. Link
  • Lleshi E., Milne-Clark T., Yu H.L., Martin H.W., Hanson R., Lach R., Rossi S.H., Riediger A.L., Görtz M., Sültmann H., Flewitt A., Lynch A.G., Gnanapragasam V.J., Massie C.E., Dev H.S. (2024). Prostate cancer detection through unbiased capture of methylated cell-free DNA. iScience. 27(7):110330. Link
  • Ceroni F., Cicekdal M.B., Holt R., Sorokina E., Chassaing N., Clokie S., Naert T., Talbot L.V., Muheisen S., Bax D.A., Kesim Y., Kivuva E.C., Vincent-Delorme C., Lienkamp S.S., Plaisancié J., De Baere E., Calvas P., Vleminckx K., Semina E.V., Ragge N.K. (2024). Deletion upstream of MAB21L2 highlights the importance of evolutionarily conserved non-coding sequences for eye development. Nat Commun. 15(1):9245. Link
  • De Freitas J.T., Thakur V., LaPorte K.M., Thakur V.S., Flores B., Caicedo V., Ajaegbu C.G.E., Ingrasci G., Lipman Z.M., Zhang K., Qiu H., Malek T.R., Bedogni B. (2024). Notch1 blockade by a novel, selective anti-Notch1 neutralizing antibody improves immunotherapy efficacy in melanoma by promoting an inflamed TME. J Exp Clin Cancer Res. 43:295. Link
  • Batuhan Kisakol., Anna Matveeva., Manuela Salvucci., Alexander Kel., Elizabeth McDonough., Fiona Ginty., Daniel B Longley., Jochen H M Prehn. (2024) Identification of unique rectal cancer-specific subtypes. British J of Cancer.130,1809–1818. Link
  • Dig B. Mahat., Nathaniel D. Tippens., Jorge D. Martin-Rufino., Sean K. Waterton., Jiayu Fu., Sarah E. Blatt & Phillip A. Sharp. (2024) Single-cell nascent RNA sequencing unveils coordinated global transcription. Nature. 631, 216–223. Link
  • Sung-Joon Park., Kenta Nakai. (2024) A computational approach for deciphering the interactions between proximal and distal gene regulators in GC B-cell response. NAR Genomics & Bioiformatics. Volume 6, issue 2. Link
  • Farrim M.I., Gomes A., Milenkovic D., Menezes R. (2024) Gene expression analysis reveals diabetes-related gene signatures. Hum Genomics 18, 16. Link
  • Eni-Aganga I., Lanaghan ZM., Ismail F., Korolkova O., Goodwin JS., Balasubramaniam M., Dash C., Pandhare J. (2024). KLF6 activates Sp1-mediated prolidase transcription during TGF-β1 signaling. J Biol Chem. 2024 300(2):105605. Link
  • Hasegawa K., Tamaki M., Sakamaki Y., Wakino S. (2024) Nmnat1  Deficiency Causes Mitoribosome Excess in Diabetic Nephropathy Mediated by Transcriptional Repressor HIC1. Int J Mol Sci. 25(12):6384. Link
  • Abrar M., Ali S., Hussain I., Khatoon H., Batool F., Ghazanfar S., Corcoran D., Kawakami Y., Abbasi AA. (2024). Cis-regulatory control of mammalian Trps1 gene expression. J Exp Zool B Mol Dev Evol. 342(2):85-100. Link
  • Scaramuzzo RT., Crucitta S., Del Re M., Cammalleri M., Bagnoli P., Dal Monte M., Pini A., Filippi L.. (2024) β3-adREnoceptor Analysis in CORD Blood of Neonates (β3 RECORD): Study Protocol of a Pilot Clinical Investigation. Life (Basel). 14(6):776. Link
  • Cene Skubic., Hana Trček., Petra Nassib., Tinkara Kreft., Andrew Walakira., Katka Pohar., Sara Petek., zadeja Režen., Alojz Ihan., Damjana Rozman. (2024) Knockouts of CYP51A1, DHCR24, or SC5D from cholesterol synthesis reveal pathways modulated by sterol intermediates. iScience, Volume 27, Issue 9, 110651. Link
  • Jamil M.A., Al-Rifai R., Nuesgen N., Altmüller J., Oldenburg J., El-Maarri O. (2024) The role of microRNAs in defining LSECs cellular identity and in regulating F8 gene expression. Front Genet. 15: 1302685 Link
  • Coatti G.C., Vaghela N., Gillurkar P., Leir S., Harris A. (2024) A promoter-dependent upstream activator augments CFTR expression in diverse epithelial cell types. Biochim Biophys Acta Gene Regul Mech. 1867(2):195031 Link
  • Novikova S., Tolstova T., Kurbatov L., Farafonova T., Tikhonova O., Soloveva N., Rusanov A., Zgoda V. (2024) Systems Biology for Drug Target Discovery in Acute Myeloid Leukemia. Int. J. Mol. Sci.  25(9), 4618 Link
  • Satsu H., Gondo Y., Shimanaka H., Imae M., Murakami S., Watari K., Wakabayashi S., Park S.J., Nakai K., Shimizu M. (2022) Signaling Pathway of Taurine-Induced Upregulation of TXNIP. Metabolites. 12(7),636. Link
  • Deepti P., Pasha A., Kumbhakar D.V., Doneti R., Heena S.K., Bhanoth S., Poleboyina P.K., Yadala R., Anapurna S.D., Pawar S.C. (2022) Overexpression of Secreted Phosphoprotein 1 (SPP1) predicts poor survival in HPV positive cervical cancer. Gene. 824,146381. Link
  • Song Q., Bian Q., Liang T., Zhang Y., Zhang K. (2021) Identification of immune-related genes and susceptible population of pulmonary tuberculosis by constructing TF-miRNA-mRNA regulatory network. Tuberculosis (Edinb). 131,102139. Link
  • Thompson B., Chen Y., Davidson E.A., Garcia-Milian R., Golla J.P., Apostolopoulos N., Orlicky D.J., Schey K., Thompson D.C., Vasiliou V. (2021) Impaired GSH biosynthesis disrupts eye development, lens morphogenesis and PAX6 function. Ocul Surf. 22,190-203. Link

Get TRANSFAC

Current TRANSFAC® release

TRANSFAC® release 2024.2

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

·       JASPAR 2024 matrix library integration

New position frequency matrices from the JASPAR 2024 release either added as matrix entries (446 cases) or hyperlinked to existing counterparts in the TRANSFAC matrix library.

·       New enhancer data

2,045 new human and mouse enhancers have been integrated together with 2,693 high confidence enhancer – promoter interactions (EPI).

·       Enhanced human SNP content

The 2023 dbSNP release 156 data for human has been integrated and increases the number of SNPs mapped to promoter, enhancer, and silencer sequences by more than 13,800,000 new single nucleotide variatons compared to the previously used version 155.

·       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 112.

·       Rat genome assembly update

TRANSFAC now uses rat genome assembly mRatBN7.2.

·       Additional interactions between human transcription factors

1,299 new human transcription factor interactions have been included from recent publications.

·       New TRANSFAC analysis tool

In its new 3.1 release, the MATCH Suite toolbox of TRANSFAC 2.0 was updated with the functionality of extended model organisms’ gene regulation analysis. Now, gene sets coming from Zebrafish, Arabidopsis, Nematoda, Fruit fly or Baker’s yeast can be analyzed based on functional categorization. You can narrow down the site search by selected transcription factors, or you can select the functional categories of your interest and perform search only for transcription factors belonging to those GO terms. As usual, a comprehensive report will be automatically generated with detailed description of the performed analysis steps, and the interactive results visualization mode will allow you to fine-tune the obtained results by applying additional filters.

    Price request TRANSFAC

    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

    TRANSFAC download: PWMs, Transcription factor binding site prediction

    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® 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 geneXplain GmbH.

    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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