TRANSFAC 2.0

TRANSFAC WorkspaceThe gold standard of transcriptional regulation

The most comprehensive transcription-factor resource in eukaryotes — now a single, complete workspace solution for gene regulation, signaling networks and disease & drug discovery.

  • Manually curated
  • Used by researchers around the globe
  • Integrated with more than 200 bioinformatics tools and pipelines

Design your workspace — tell us your needs (biological system, type of experiments, data type, length of your project) and we’ll build the package around your requirements.

Ns Art Workspace V2
Key statistics

Data manually curated from scientific publications

>10,000Positional weight matrices, collected as well as constructed by our team
>1MExperimentally proven TF sites
>1.2MCurated reactions
>1,500Pathways
>140,000Biomarkers
>55,000Drug targets
Why researchers need it

From a list of genes to the mechanisms, targets and treatments behind them

Regulation is hidden in the genome

Transcription-factor binding sites, their combinations, the promoters and enhancers that control your genes are scattered across the genome and buried in the literature.

Signals span whole networks

Understanding why a gene acts as it does means reconstructing the signaling and metabolic cascades upstream of its regulators — across millions of reactions.

Translation demands integration

Turning findings into drug targets, biomarkers and treatment options requires linking mechanisms to diseases, drugs and clinical trials — and doing it with or without coding.

One workspace for the complete regulatory journey

  • Which transcription factors regulate them?
  • Where are the relevant binding sites?
  • Which promoters and enhancers are involved?
  • Which combinations of transcription factors may be active?
  • Which signaling pathways control those transcription factors?
A list of genes is rarely the answer.You may need to understand:
  • Which upstream molecules act as master regulators?
  • How do different omics layers fit together?
  • Which molecular mechanisms may be driving a disease?
  • Which biomarkers or therapeutic targets should be investigated next?

TRANSFAC Workspace connects these layers in one integrated research environment. Start with a gene, a regulatory sequence, a list of differentially expressed genes, genomic variants or multi-omics data — then move through the biology, step by step.

  1. Gene regulation
  2. Transcription factors
  3. Signaling and metabolic pathways
  4. Master regulators
  5. Disease mechanisms
  6. Biomarkers and prospective drug targets
Video · 1:49

From motif analysis to drug targets

Watch Prof. Dr. Alexander Kel take one enriched motif all the way to disease mechanisms and prospective drug targets — in under two minutes.

You decide where to start, how deeply to analyze and how to build your workflow.

You set the variables —

  • experiments
  • seats
  • storage
  • usage time

— we’ll shape a package that fits.

What is TRANSFAC Workspace

An integrated environment where your data and our knowledge, tools and workflows work together

TRANSFAC Workspace unites the TRANSFAC knowledge base with the geneXplain platform and the full analysis suite: discover transcription-factor binding sites, reconstruct signal-transduction and metabolic networks, and identify disease mechanisms, drug targets and treatments — from raw multi-omics data through to detailed result tables and a publication-ready report.

Includes

Knowledge base

Gx Icon Transfac V2TRANSFAC DatabaseGx Icon Transpath V2TRANSPATH DatabaseGx Icon Humanpsd V2HumanPSD Database

Platform

Gx Icon Platform V2geneXplain platform

Analysis suite

Gx Icon Match SuiteMATCH SuiteGx Icon Pathway Omics SuitePathway Omics SuiteGx Icon Genome Enhancer V2Disease Omics Suite (Genome Enhancer)Gx Icon PathfinderPathFinderProtein Genome Map
Key capabilities

Every capability below is included in TRANSFAC Workspace. They build on one another — from binding sites, to the networks around them, to the diseases and drugs they drive.

Three domains of analysis, one seamless workflow

01 · Gene regulation & TFBS — discover TF binding sites in the promoters and enhancers of your genes

A comprehensive solution for gene-regulation analysis, powered by the world’s largest curated encyclopedia of transcriptional regulation.

Other motif tools ask, “Which TF could bind here?” TRANSFAC Workspace also asks, “Which TF is likely to be active here, in this cell type, under this disease condition — and what upstream mechanism activates it?”

Predict transcription-factor binding sites and their combinations across genomic sequences using more than 10,000 positional weight matrices, advanced Machine Learning methods, and matrix-specific quality thresholds that help control false-positive and false-negative predictions.

Access experimentally characterized binding sites, large-scale ChIP data, regulated genes, and detailed promoter, enhancer and silencer annotations. Identify tissue- and cell-type-specific regulatory units and combinations of transcription factors that may control your genes.

Use a complete set of promoter-analysis tools — from custom pipelines for bioinformaticians to automated, no-code workflows for biomedical researchers — integrated with multi-omics analysis in the geneXplain Platform.

02 · Pathways & networks — reconstruct the signal-transduction network controlling your genes

A comprehensive knowledge base on signal transduction and metabolic pathways — with the master-regulator analysis that turns patterns into causes.

Predict the pathways targeting the genes of your interest and build customized regulatory and metabolic networks based on over one million reactions extracted from the original scientific literature and evaluated by experts.

Apply integrated pathway visualization and modification, and reconstruct master regulators upstream of your transcription factors using a modern graph algorithm.

Perform Upstream Analysis — integrated promoter and pathway analysis — for the identification of prospective drug targets and biomarkers, within the geneXplain platform.

03 · Disease & drug discovery — identify drug targets and disease biomarkers, even for complex clinical cases

Treatments for complex clinical cases with pathology unresponsive to conventional therapies.

No bioinformatics skills needed — run complicated omics data-processing pipelines with just a few clicks in a user-friendly interface, and get drug-repurposing suggestions driven by networks and chemoinformatics.

Automatically process multi-omics raw patient data and identify prospective drug targets and treatments predicted to be effective for the studied clinical case.

Reconstruct disease molecular mechanisms in full, based on integrated promoter and pathway analysis for over 2,700 human diseases.

Use expert curated disease-biomarker knowledge to distinguish causal, correlative and mechanism-of-action biomarkers, place them in their biological and clinical context, and connect them to supporting evidence, molecular pathways, drugs and clinical studies.

  • PWMsover 12,000 for animals, plants, insects and fungi
  • TF sitesexperimentally proven functional sites and the world-wide most comprehensive collection of ChIP-seq sites
  • Promoters & enhancerstissue and cell-type specific (Hi-C, CAGE, single cell)
  • Structural, functional features and expression of transcription factors
  • Unique classification of transcription factors by their DNA-binding domains
  • Pathwaysover 1,600, signaling and metabolic
  • Reaction networksover 1,200,000 experimentally proven reactions, with experimental details
  • Protein–protein interactions, protein complexes
  • miRNA regulatory network
  • Post-translational protein modifications (PTMs)
  • Upstream of transcription factorslinks between transcription factors and signaling cascades
  • Mechanistic details and semantic overview
  • Disease mechanism reconstruction with upstream analysis for over 2,700 human diseases
  • Gene to Disease relationships, biomarkersover 140,000 (causal, correlative, mechanism of disease, therapeutic targets) with over 2,000,000 individual annotations
  • Drugs & targetsover 10,000 drugs and over 55,000 targets
  • Clinical trialsover 1,100,000 trial–disease pairs integrated from public sources
  • Site search & enrichmentautomatic cut-off optimization (Match, FMatch)
  • Tissue-specific TFBS in promoters and enhancers (MATCH Suite)
  • Machine Learningreveal TF site combinatorics (CMA, MEALR)
  • Omics analysisRNA-seq, ChIP-seq, ATAC-seq, CUT&RUN, whole genome sequencing, whole exon sequencing, single cell data
  • Easy GUI for no-coding bioinformatics · 200+ tools & pipelines (genes, FASTA, FASTQ, VCF, BED)
  • Flexible cloud storage · APIrun our tools from local Python and R if you prefer to work without a GUI
  • A gene-regulation solution covering 300+ speciesfocus on human, mouse, rat, drosophila, zebrafish, yeast and plants
  • Interactive search engine into the world’s largest manually curated encyclopedia of transcriptional regulation
  • Master regulatorssearch upstream of TFs with a modern graph algorithm
  • Upstream analysiscombined promoter + network analysis
  • SBGN viewerweb-based, editable pathway/network diagrams
  • ODE modelingthe fastest and most robust simulation engine
  • End-to-end multi-omics workflowupload, integrate and analyze genomics, transcriptomics, epigenomics, proteomics and metabolomics
  • Multi-omics integrationOmics Suite “All Five”: transcriptomics, genomics, epigenomics, proteomics, metabolomics
  • Drug repurposingusing networks and chemoinformatics
  • Fully automaticone-click solution
  • Detailed reportwith everything needed for your paper, plus a Molecular Tumor Board report for a number of neoplasm diseases
  • Deep understanding of the molecular mechanisms underlying the studied pathology
  • Prospective treatment suggestionsknown and repurposed drugs — for complex cases where no conventional therapies are effective
How it works

From raw data to a publication-ready report, in one platform

  1. Bring your data

    Upload genes, FASTA, FASTQ, VCF or BED, and omics data from RNA-seq, ChIP-seq, ATAC-seq, CUT&RUN, WGS, WES and single cell.

  2. Analyze in the platform

    Run 200+ tools and pipelines in the geneXplain Platform through an easy no-code GUI, or drive everything via the API from Python and R.

  3. Find binding sites

    Predict TF binding sites and their combinations with the MATCH Suite, over 10,000 PWMs and built-in ML.

  4. Reconstruct networks

    Build regulatory and metabolic networks, search master regulators upstream of your TFs, and model them with SBGN diagrams and ODE simulation.

  5. Translate to disease

    Discover putative disease mechanisms, biomarkers and drug targets, and identify known or repurposed treatments.

Genome Enhancer

Automated enhancement of genomic and multi-omics data turned into mechanistic and therapeutic insight. In one workflow it elevates raw molecular data to an understanding of disease mechanisms, key transcription factors, master regulators, pathways, biomarkers and drug targets, and generates mechanism-based treatment hypotheses. Results are delivered in a publication-ready report and, for supported cancers, a Molecular Tumor Board report.

Use cases

One resource, from basic research to translational discovery

Map gene regulation

Discover the transcription-factor binding sites — and their combinations — in the promoters and enhancers of your genes.

Reconstruct networks

Rebuild the signal-transduction and metabolic networks controlling your genes and pinpoint their master regulators.

Find targets & treatments

Identify drug targets and disease biomarkers, and surface known or repurposed treatments for complex clinical cases.

Featured case study — How cancer cells outsmart therapy

HER2+ breast-cancer trastuzumab resistance. See how the Pathway Omics Suite reconstructs resistance mechanisms and points to actionable targets — a worked example of TRANSFAC Workspace on a real clinical question.

Read the case study

Ready to size it for your project?

Tell us the biological system, the experiments and the data — we will build the package around them.

Who it’s for

Built for the whole lab, from the bench to the command line

No coding required — bench biologists & biomedical researchers

Run complicated omics pipelines from a few clicks in a user-friendly GUI. Fully automated, one-click solutions take you from raw data to a comprehensive report — no bioinformatics skills needed.

Full control & the API — core bioinformaticians

Build customized analysis pipelines across 200+ tools, integrate five omics layers, and run everything from your local Python and R via the API — with flexible cloud workspace sizes.

The gold standard

The reference knowledge base for eukaryotic gene regulation

TRANSFAC Workspace is the gold-standard resource for deep analysis of eukaryotic gene regulation.

Knowledge is curated by trained experts from full primary publications, linked to source articles and assigned evidence-based quality levels for binding sites and molecular reactions.

This enables researchers to focus analyses on the most reliable evidence.

30,000+scientific publications

Cited in more than 30,000 scientific publications, TRANSFAC also provides a distinctive PWM quality framework, with matrix-specific thresholds evaluated to reduce false-positive and false-negative TF-site predictions.

It supports tissue- and cell-type-specific TF regulatory units and goes beyond motif scanning by connecting regulatory sites and TF combinations to signaling pathways, master regulators, diseases, biomarkers and prospective drug targets.

Why it’s trusted

The gold standard, by construction

Manually curated

Make better decisions with biological knowledge you can trust

High-quality, manually curated evidence helps you filter out noise, identify the most relevant mechanisms, prioritize stronger targets and biomarkers, and generate hypotheses worth validating experimentally.

Every curated fact is traced to the original scientific publication and supported by evidence-based quality assessment — so you can understand not only what is known, but how strongly it is supported.

This depth of evidence, curation and biological context is simply not available from free resources alone.

TRANSFAC® curation from sites to DNA-binding models

Site & Region Curation
Expert CurationBinding sites and genomic regions from literature
Site DatasetsIndividual and sets of binding sites curated
Genome-wide DataPublished genome-wide binding datasets
PWM Generation
Literature PWMsPWM generation and curation directly from published papers
Site Selection & Opt• Expert site selection
• Alignment & optimization based on standard performance (e.g. AUC)
Expert CurationExpert curation of published position weight matrices (PWMs)
Matrix Evaluation
Matrix EvaluationSystematic quality assessment of generated matrices
Performance Assessment & ML RecommendationAssessment of PWM performance on genome-wide binding data combined with machine learning-based model recommendations
Model Library
MEALR Model LibraryIntegrated repository of robust biological models
Optimized Machine Learning ModelsPredictive machine learning models of context-specific combinatorial regulation optimized systematically using cross-validation accuracy metrics

TRANSPATH® curation from literature evidence to mechanistic reactions

Evidence & Context Curation
Expert Literature CurationPrimary papers are curated to capture experimentally supported protein interactions, pathways, and semantic reactions.
Molecule Identity ResolutionGene/protein names, species, isoforms, synonyms and complexes are verified before entry.
Experimental ContextEach molecular-evidence reaction is linked to the material and method information from the publication.
Reaction Structuring
Molecule HierarchyReactions are captured at isoform level when identifiable, otherwise at isogroup level; family-level molecules are used only when isogroup mapping is not possible.
Direct vs Indirect ReactionBinding or modification (PTM) interactions are captured as direct reactions; all others as indirect, with experimental evidence recorded in both cases.
Standard Reaction SyntaxIncoming/outcoming molecules, enzymes, complexes, modified forms and stoichiometry are captured consistently.
Modification Precision
PTM & Cleavage DetailsPhosphorylation, ubiquitination, acetylation, cleavage, and other modifications are captured with residue information when reported.
Amino-acid Position QCModified residues are checked against UniProtKB/Swiss-Prot sequences; uncertainty is retained instead of forcing false precision.
Pathway-ready Knowledge Base
Evidence ClassificationMethod hierarchy and material categories preserve assay strength and physiological relevance.
Mechanistic Network IntegrationCurated reactions connect molecules, complexes, PTMs and from which chains and pathways are created for a computable regulatory network.

Quality advantage: TRANSPATH captures who acts on whom, how, with what evidence, and in which biological context.

HumanPSD™ disease curation from literature evidence to structured disease mechanisms

Evidence & Context Curation
Gene–disease literature capturePapers are linked to protein–disease combinations using Nametagging, MeSH indexing and disease synonyms.
Reference validationCurators confirm species, gene identity and genuine disease relevance before creating an annotation.
Disease term resolutionMeSH definitions, synonyms and hierarchies guide term choice; non-MeSH diseases are captured with tracked substitute phrases.
Structured Disease Annotation
BioKnowledge LinGOControlled subject–link–object statements connect a gene to a disease using reusable phrase templates.
Delta ruleCuration focuses on change: altered gene/gene product state is linked to altered disease state whenever evidence allows.
Linking term precisionCausal, correlative, preventive and negative links subject and object phrases; predictive statements are explicitly marked with “may”.
Phenotype & Mechanism Specificity
Gene-side evidenceExpression, molecular function, localization, mutation, domain, modification and antibody evidence are captured separately.
Disease-side phenotypeSubtype/form, symptoms, tumor association, organ or cell dysfunction and defective GO-BP processes are resolved.
Context-sensitive interpretationCell-line, tissue, clinical and experimental evidence are worded conservatively according to author claims.
Research-ready Disease Knowledge
Evidence classificationAnnotations are assigned to Diagnostic Marker, Molecular Mechanism, Therapeutic Target or Negative Correlation categories.
Depth and consistency controlMini-reports, Lit List and prior annotations are reviewed to avoid missed evidence and redundant curation.
Disease view integrationDisease reports connect curated genes with biological processes, associated disorders and disease subtypes.

Quality advantage: HumanPSD™ disease curation captures not only whether a protein is linked to a disease, but how strongly the literature supports biomarker, mechanism or target relevance.

Experimentally validated

1.2M+
reactions from primary literature

Functional TF binding sites confirmed by multiple experiments and the most comprehensive collection of ChIP-seq sites; over 1.2M reactions extracted from primary literature and evaluated by experts.

Broad & deep

  • 300+species
  • >10,000PWMs
  • >2,700human diseases
  • >10,000drugs
  • >55,000targets

Continuously updated

~40
years of continuous updating

Almost 40 years of continuous updating, several times a year. Versioned release statistics published for TRANSFAC, TRANSPATH, Protein Genome Map and HumanPSD.

Matrix evaluation

TRANSFAC matrices are systematically benchmarked on genome-wide binding data

TRANSFAC provides a distinctive PWM quality framework, with matrix-specific thresholds evaluated to reduce false-positive and false-negative TF-site predictions. Having introduced its matrix evaluation in 2018, TRANSFAC was the first database to implement and regularly carry out a systematic model comparison on genome-wide binding data.

What sets the TRANSFAC matrix evaluation apart
100%recommendation coverage of TFs
2018first database to implement and regularly carry out a systematic model comparison
Integration of all available genome-wide binding studies for a transcription factor
Built-in assessment of data quality
Internal validation of each study
Determination of the best general purpose model for a transcription factor
Machine learning-based PWM assessment
Ability to provide a recommendation for all transcription factors with or without known genome-wide binding data
The matrix evaluation workflow: enrichment analysis and weighted PWM ranking, with a machine-learning extension for factors without genome-wide binding data.

The matrix evaluation workflow: enrichment analysis and weighted PWM ranking, with a machine-learning extension for factors without genome-wide binding data. Tap to enlarge.

Matrix evaluation example: average rank of PWMs for c-Jun across experiments, dot size showing rank precision.

Matrix evaluation example: average rank of PWMs for c-Jun across experiments, dot size showing rank precision. Tap to enlarge.

Model diversity in context-specific gene regulation: why TRANSFAC curates context-specific PWMs rather than a single consensus model.

Model diversity in context-specific gene regulation: why TRANSFAC curates context-specific PWMs rather than a single consensus model. Tap to enlarge.

FAQ

Questions, answered

Everything in one package: the TRANSFAC, TRANSPATH and HumanPSD databases; the geneXplain Platform; and the full tool suite — MATCH Suite, Pathway Omics Suite, Disease Omics Suite (Genome Enhancer), PathFinder and the Protein Genome Map. Every subscription includes the complete functionality — nothing is held back.

No. TRANSFAC Workspace offers an easy GUI for no-coding bioinformatics and fully automated, one-click solutions that take bench biologists from raw data to a report. Core bioinformaticians can also build customized pipelines and run the tools from local Python and R via the API.

Genes, FASTA, FASTQ, VCF and BED files, plus omics data from RNA-seq, ChIP-seq, ATAC-seq, CUT&RUN, WGS, WES and single cell — with end-to-end integration of genomics, transcriptomics, epigenomics, proteomics and metabolomics.

More than 300 species, with a focus on human, mouse, rat, drosophila, zebrafish, yeast and plants. PWMs are provided for animals, plants and fungi.

TRANSFAC is the gold-standard, manually curated and experimentally validated resource for transcription factors, their binding sites and DNA-binding models. See our detailed TRANSFAC versus JASPAR comparison.

A promoter may contain potential binding sites for many transcription factors, but sequence matches alone do not show which TFs are likely to regulate the gene in your biological context. The pathway layer identifies upstream signals and master regulators that may activate groups of TFs in a coordinated way. The disease layer adds tissue, cell-type and disease context, helping narrow the analysis to TFs that are relevant to the studied system. This context-specific TF set can then be used to search for enriched motifs and regulatory combinations, reducing biologically irrelevant predictions and producing more testable hypotheses.

Yes. It supports disease-mechanism discovery with Machine Learning and upstream analysis for over 2,700 human diseases, over 140,000 biomarkers, over 10,000 drugs and over 55,000 targets, and over 1,100,000 clinical-trial–disease pairs — including drug repurposing and a Molecular Tumor Board report for supported neoplasm diseases.

Databases are updated on a release cycle of two releases per year. Release statistics are published for TRANSFAC, TRANSPATH, the Protein Genome Map and HumanPSD.

TRANSFAC Workspace is available by subscription as one complete package — you always get the full functionality, because that’s where the value lies. Rather than reduced editions, we right-size the package to you: the number of experiments, seats, storage and usage time. Design your workspace with our team and we’ll shape the configuration that fits.

Get started

Maximize your potential with the gold standard of transcriptional regulation

One package. Every database, tool and workflow. From a list of genes to the mechanisms, targets and treatments behind them.

One complete package — on purpose. Its full-scale functionality is where the value is: every database and tool works together, so you get more from your data — and better value for money — than assembling the pieces yourself. What we tailor to you is the scale, not the features.

You can tailor
  • Number of seats
  • Storage space
  • Usage time

Configure your TRANSFAC Workspace

Tell us about your research so we can recommend the right setup.

TRANSFAC Workspace configurator

Design your workspace — tell us your needs and we’ll shape the configuration that fits.