geneXplain-platform

geneXplain platform

The comprehensive bioinformatics platform, also available via API

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The geneXplain platform is an online toolbox and workflow management system for a broad range of bioinformatic and systems biology applications. The individual modules, or Bricks, are unified under a standardized interface, with a consistent look-and-feel and can flexibly be put together to comprehensive workflows. The workflow management is intuitively handled through a simple drag-and-drop system. With this system, you can edit the predefined workflows or compose your own workflows from scratch.

 

Own Bricks can easily be added as scripts or plug-ins and can be used in combination with pre-existing analyses.
GeneXplain GmbH provides a number of state-of-the-art bricks; some of them can be obtained free of charge, while others require licensing for small fee in order to guarantee active maintenance and dynamic adaptation to the rapidly developing know-how in this field.

geneXplain platform appearance

The start page provides an easy access to a number of application areas.

Key features

Integrated databases and analysis tools
The platform provides an integrated view on several databases and analysis tools, public domain as well as commercial ones. They can be combined in a highly flexible way to design customized analyses.
Ready-made workflows for an easy start
A rapidly growing number of proven workflows facilitates a quick and easy access to the platform and its complex analysis functions. Input forms are simple and user-friendly. Workflows can be easily customized to specific needs. Experienced users can create their own workflows.
Fully integrated upstream analysis
The platform provides a fully integrated upstream analysis, which combines state-of-the-art analysis of regulatory genome regions with sophisticated pathway analyses.
Knowledge-based data analysis
The platform uses a number of renowned high-quality databases for the data analysis. TRANSFAC® and TRANSPATH® are expert-curated databases. GeneWays is generated by an NLP-based text-mining approach, providing a helpful complement for manually curated data. Well-known public-domain databases like Reactome and HumanCyc are integrated and applied as well.
JavaScript and R scripts
User-specific scripts in JavaScript and in R can be added directly into the platform, and immediately executed. They can be combined with pre-existing analyses, and can be part of the workflows.
NGS data analysis
NGS data analysis is supported by the platform. ChIP-seq data sets containing in vivo transcription factor binding sites or methylation results can be analyzed with the help of ready-made workflows. Galaxy tools are integrated, supporting RNA-seq data analysis, and many functions more.
Simulation engine inside
The platform contains a simulation engine that executes differential equation systems and visualizes the results. Parameter optimization, parameter fitting (based on expression data), and hierarchical modeling are supported.
Group project work including chat function
Share your data and results with other members of the project. Discuss what you are doing while working together on a dataset.

 

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

Insights

RNA-seq data analysis
From raw reads to full integrated & advanced gene analysis of your experimental data.
Transcription factor identification
Find enriched transcription factor binding sites and corresponding factors/enhancers.
Networks and key signaling molecules
Upstream analysis to discover novel master regulators and underlying mechanisms.
Next generation sequencing
Gene expression profiling, detection of genetics changes and molecular analysis.
Drug target assessment
Integrated promoter and pathway analysis to find prospective therapeutic targets.
Pathway enrichment
Identify key nodes and inferred activity in canonical pathways or reconstructed networks.
ChIP-seq data analysis
Calling peaks, find regulatory regions, classify and analyze target genes.
Single Nucleotide Polymorphisms
Identify affected regulatory DNA motifs and find damaged signal proteins.
Gene Ontology
Map, reduce and visualize GO terms to get a functional classification.
Single Sequences in Genome browser
Work with human, mouse, rat, zebrafish and arabidopsis genome builds.
miRNA characterization
Target identification, get binding site enrichment and tissue specificity.
Genomic variants verification
Predict variant effects and get a molecular tumor board report.

New applications

Out now !
Updated databases
  • TRANSFAC® 2023.1
  • TRANSPATH® 2023.1
  • HumanPSD™ 2023.1
New workflows and methods
  • The platform now provides a new section called “Combinatorial regulation”, which provides tools for analysis of combinations of TF binding sites in promoters, enhancers and silencers, and other regulatory genomic regions. In this section, in addition to our traditional Composite Module Analyst, we introduce a completely new branch of site search combinatorial analysis methods based on the sparse logistic regression (MEALR).
  • The new workflow Combinatorial regulation analysis of genomic or custom sequences scans input sequences for tissue and cell type- specific transcription factor binding regions using the newly created library of MEALR models delivered with the TRANSFAC® database.
  • The new method MEALR combinatorial regulation analysis applies combinatorial regulatory models (CRMs) based on the MEALR affinity score to classify or scan sequences for occurrences of combinations of transcription factor binding sites represented by TRANSFAC® PWMs.
  • The new method Extract TRANSFAC(R) PWMs from combinatorial regulation analysis extracts TRANSFAC® PWMs from a result table generated by the MEALR combinatorial regulation analysis. The PWMs represent transcription factor binding motifs for the TF in focus and its co-factors that constitute the combinatorial module predicted by the MEALR model.
New import possibility

 

Download full feature list

Videos

Here are videos about “RNA-seq preprocessing with the geneXplain platform”
Here is a playlist about “RNA-seq data analysis from FASTQ files to master regulators with geneXplain platform”
Find below a compilation of our introductory and tutorial videos
In English Language
This video is a general introduction to the geneXplain® platform. (3:21 min)
This video is about how to convert gene identifiers from Ensembl to others in the geneXplain platform. (3:02 min)
This video is about how to annotate a gene table with the geneXplain platform. (2:57 min)
In Chinese Language
This video is a general introduction to the geneXplain® platform; it introduces you to different workflows. (1:38 min)
It shows you how to register a free platform account and to login. The audio-visual also introduces you to the look and feel of the geneXplain® platform interface. (4:11 min)
This video demonstrates how to upload raw data from an experiment to the geneXplain® platform for further analysis. (2:46 min)
In this video microarray data is used as an example to show you how to further analyze data from high-throughput experiments on the geneXplain® platform. (6:45 min)

Recent applications

Selection of articles reporting about geneXplain platform applications:
  • Drake, C., Zobl W., Wehr M., Koschmann J., De Luca D., Kühne B. A. , Vrieling H. , Boei J. , Hansen T. , Escher S. E. (2023) Substantiate a read-across hypothesis by using transcriptome data—A case study on volatile diketones. Front. Toxicol. 5. Link
  • Rajavel A., Klees S., Hui Y., Schmitt A.O., Gültas M. (2022) Deciphering the Molecular Mechanism Underlying African Animal Trypanosomiasis by Means of the 1000 Bull Genomes Project Genomic Dataset. Biology (Basel). 11(5), 742. Link
  • Menck K., Wlochowitz D., Wachter A., Conradi L.C., Wolff A., Scheel A.H., Korf U., Wiemann S., Schildhaus H.U., Bohnenberger H., Wingender E., Pukrop T., Homayounfar K., Beißbarth T., Bleckmann A. (2022) High-Throughput Profiling of Colorectal Cancer Liver Metastases Reveals Intra- and Inter-Patient Heterogeneity in the EGFR and WNT Pathways Associated with Clinical Outcome. Cancers 14(9), 2084. Link
  • Myer P.A., Kim H., Blümel A.M., Finnegan E., Kel A., Thompson T.V., Greally J.M., Prehn J.H., O’Connor D.P., Friedman R.A., Floratos A., Das S. (2022) Master Transcription Regulators and Transcription Factors Regulate Immune-Associated Differences Between Patients of African and European Ancestry With Colorectal Cancer. Gastro Hep Adv. 1(3), 328–341. Link
  • Kawashima Y., Nagai H., Konno R., Ishikawa M., Nakajima D., Sato H., Nakamura R., Furuyashiki T., Ohara O. (2022) Single-Shot 10K Proteome Approach: Over 10,000 Protein Identifications by Data-Independent Acquisition-Based Single-Shot Proteomics with Ion Mobility Spectrometry. J Proteome Res. 21(6), 1418–1427. Link
  • Klees S., Schlüter J.S., Schellhorn J., Bertram H., Kurzweg A.C., Ramzan F., Schmitt A.O., Gültas M. (2022) Comparative Investigation of Gene Regulatory Processes Underlying Avian Influenza Viruses in Chicken and Duck. Biology (Basel). 11(2), 219. Link
  • Benjamin, S.J., Hawley, K.L., Vera-Licona, P., La Vake, C.J., Cervantes, J.L., Ruan, Y., Radolf, J.D., Salazar, J.C. (2021) Macrophage mediated recognition and clearance of Borrelia burgdorferi elicits MyD88-dependent and -independent phagosomal signals that contribute to phagocytosis and inflammation. BMC Immunol. 22, 32. Link
  • Menck K., Heinrichs S., Wlochowitz D., Sitte M., Noeding H., Janshoff A., Treiber H., Ruhwedel T., Schatlo B., von der Brelie C., Wiemann S., Pukrop T., Beißbarth T., Binder C., Bleckmann A. (2021) WNT11/ROR2 signaling is associated with tumor invasion and poor survival in breast cancer. J Exp Clin Cancer Res. 40, 395. Link
  • Meier, T., Timm, M., Montani, M., Wilkens, L. (2021) Gene networks and transcriptional regulators associated with liver cancer development and progression. BMC Med. Genomics 14, 41. Link
  • Chereda H., Bleckmann A., Menck K., Perera-Bel J., Stegmaier P., Auer F., Kramer F., Leha A., Beißbarth T. (2021) Explaining decisions of graph convolutional neural networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer. Genome Med. 13, 42. Link
  • Heinrich F., Ramzan F., Rajavel A., Schmitt A.O., Gültas M. (2021) MIDESP: Mutual Information-Based Detection of Epistatic SNP Pairs for Qualitative and Quantitative Phenotypes. Biology (Basel). 10(9), 921. Link
  • Tenesaca S., Vasquez M., Alvarez M., Otano I., Fernandez-Sendin M., Di Trani C.A., Ardaiz N., Gomar C., Bella A., Aranda F., Medina-Echeverz J., Melero I., Berraondo P. (2021) Statins act as transient type I interferon inhibitors to enable the antitumor activity of modified vaccinia Ankara viral vectors. J Immunother Cancer. 9(7), e001587. Link
  • Vanvanhossou S.F.U., Giambra I.J., Yin T., Brügemann K., Dossa L.H., König S. (2021) First DNA Sequencing in Beninese Indigenous Cattle Breeds Captures New Milk Protein Variants. Genes (Basel). 12(11), 1702. Link
  • Lloyd K., Papoutsopoulou S., Smith E., Stegmaier P., Bergey F., Morris L., Kittner M., England H., Spiller D., White M.H.R., Duckworth C.A., Campbell B.J., Poroikov V., Martins Dos Santos V.A.P., Kel A., Muller W., Pritchard D.M., Probert C., Burkitt M.D.; SysmedIBD Consortium. Using systems medicine to identify a therapeutic agent with potential for repurposing in inflammatory bowel disease. Dis Model Mech. 13(11), dmm044040. Link
  • Odagiu L., Boulet S., Maurice De Sousa D., Daudelin J.F., Nicolas S., Labrecque N. (2020) Early programming of CD8+ T cell response by the orphan nuclear receptor NR4A3. Proc Natl Acad Sci U S A. 117(39), 24392–24402. Link
  • Ayyildiz D., Antoniali G., D’Ambrosio C., Mangiapane G., Dalla E., Scaloni A., Tell G., Piazza S. (2020) Architecture of The Human Ape1 Interactome Defines Novel Cancers Signatures. Sci Rep. 10, 28. Link
  • Ural, B.B., Yeung, S.T., Damani-Yokota, P., Devlin, J.C., de Vries, M., Vera-Licona, P., Samji, T., Sawai, C.M., Jang, G., Perez, O.A., Pham, Q., Maher, L., Loke, P., Dittmann, M., Reizis, B., Khanna, K.M. (2020) Identification of a nerve-associated, lung-resident interstitial macrophage subset with distinct localization and immunoregulatory properties. Sci. Immunol. 5, eaax8756. Link
  • Leiherer A., Muendlein A., Saely C.H., Fraunberger P., Drexel H. (2019) Serotonin is elevated in risk-genotype carriers of TCF7L2 – rs7903146. Sci Rep. 9, 12863. Link
  • Wang B., Ran Z., Liu M., Ou Y. (2019) Prognostic Significance of Potential Immune Checkpoint Member HHLA2 in Human Tumors: A Comprehensive Analysis. Front Immunol. 10, 1573. Link
  • Mekonnen, Y.A., Gültas, M., Effa, K., Hanotte, O., Schmitt, A.O. (2019) Identification of Candidate Signature Genes and Key Regulators Associated With Trypanotolerance in the Sheko Breed. Front. Genet. 10, 1095. Link
  • Blazquez, R., Wlochowitz, D., Wolff, A., Seitz, S., Wachter, A., Perera-Bel, J., Bleckmann, A., Beißbarth, T., Salinas, G., Riemenschneider, M.J., Proescholdt, M., Evert, M., Utpatel, K., Siam, L., Schatlo, B., Balkenhol, M., Stadelmann, C., Schildhaus, H.U., Korf, U., Reinz, E., Wiemann, S., Vollmer, E., Schulz, M., Ritter, U., Hanisch, U.K., Pukrop, T. (2018) PI3K: A master regulator of brain metastasis-promoting macrophages/microglia. Glia 66, 2438-2455. Link
  • Orekhov, A.N., Oishi, Y., Nikiforov, N.G., Zhelankin, A.V., Dubrovsky, L., Sobenin, I.A., Kel, A., Stelmashenko, D., Makeev, V.J., Foxx, K., Jin, X., Kruth, H.S. Bukrinsky, M. (2018) Modified LDL Particles Activate Inflammatory Pathways in Monocyte-derived Macrophages: Transcriptome Analysis. Curr. Pharm. Des. 24, 3143-3151. Link
  • Smetanina, M.A., Kel, A.E., Sevost’ianova, K.S., Maiborodin, I.V., Shevela, A.I., Zolotukhin, I.A., Stegmaier, P., Filipenko, M.L. (2018) DNA methylation and gene expression profiling reveal MFAP5 as a regulatory driver of extracellular matrix remodeling in varicose vein disease. Epigenomics 10, 1103-1119. Link
  • Kalozoumi, G., Kel-Margoulis, O., Vafiadaki, E., Greenberg, D., Bernard, H., Soreq, H., Depaulis, A., Sanoudou, D. (2018) Glial responses during epileptogenesis in Mus musculus point to potential therapeutic targets. PLoS One 13, e0201742. Link
  • Mandić, A.D., Bennek, E., Verdier, J., Zhang, K., Roubrocks, S., Davis, R.J., Denecke, B., Gassler, N., Streetz, K., Kel, A., Hornef, M., Cubero, F. J., Trautwein, C. and Sellge, G. (2017) c-Jun N-terminal kinase 2 promotes enterocyte survival and goblet cell differentiation in the inflamed intestine. Mucosal Immunol. 10, 1211-1223. Link
  • Niehof, M., Hildebrandt, T., Danov, O., Arndt, K., Koschmann, J., Dahlmann, F., Hansen, T. and Sewald, K. (2017) RNA isolation from precision-cut lung slices (PCLS) from different species. BMC Res. Notes 10, 121. Link
  • Triska, M., Solovyev, V., Baranova, A., Kel, A., Tatarinova, T.V. (2017) Nucleotide patterns aiding in prediction of eukaryotic promoters. PLoS One 12, e0187243. Link
  • Pietrzyńska, M., Zembrzuska, J., Tomczak, R., Mikołajczyk, J., Rusińska-Roszak, D., Voelkel, A., Buchwald, T., Jampílek, J., Lukáč, M., Devínsky, F. (2016) Experimental and in silico investigations of organic phosphates and phosphonates sorption on polymer-ceramic monolithic materials and hydroxyapatite. Eur. J. Pharm. Sci. 93, 295-303. Link
  • Ciribilli, Y., Singh, P., Inga, A., Borlak, J. (2016) c-Myc targeted regulators of cell metabolism in a transgenic mouse model of papillary lung adenocarcinoma. Oncotarget 7, 65514-65539. Link
  • Wlochowitz, D., Haubrock, M., Arackal, J., Bleckmann, A., Wolff, A., Beißbarth, T., Wingender, E., Gültas, M. (2016) Computational Identification of Key Regulators in Two Different Colorectal Cancer Cell Lines. Front. Genet. 7, 42. Link
  • Lee, E.H., Oh, J.H., Selvaraj, S., Park, S.M., Choi, M.S., Spanel, R., Yoon, S. and Borlak, J. (2016) Immunogenomics reveal molecular circuits of diclofenac induced liver injury in mice. Oncotarget 7, 14983-15017. Link
  • Kural, K.C., Tandon, N., Skoblov, M., Kel-Margoulis, O.V. and Baranova, A.V. (2016) Pathways of aging: comparative analysis of gene signatures in replicative senescence and stress induced premature senescence. BMC Genomics 17(Suppl 14), 1030. Link
  • Borlak, J., Singh, P. and Gazzana, G. (2015) Proteome mapping of epidermal growth factor induced hepatocellular carcinomas identifies novel cell metabolism targets and mitogen activated protein kinase signalling events. BMC Genomics 16, 124. Link
  • Shi, Y., Nikulenkov, F., Zawacka-Pankau, J., Li, H., Gabdoulline, R., Xu, J., Eriksson, S., Hedström, E., Issaeva, N., Kel, A., Arnér, E.S., Selivanova, G. (2014) ROS-dependent activation of JNK converts p53 into an efficient inhibitor of oncogenes leading to robust apoptosis. Cell Death Differ. 21, 612-623. Link
  • Schlereth, K., Heyl, C., Krampitz, A.M., Mernberger, M., Finkernagel, F., Scharfe, M., Jarek, M., Leich, E., Rosenwald, A., Stiewe, T. (2013) Characterization of the p53 Cistrome – DNA Binding Cooperativity Dissects p53’s Tumor Suppressor Functions. PLoS Genet. 9, e1003726. Link
  • Nikulenkov, F., Spinnler, C., Li, H., Tonelli, C., Shi, Y., Turunen, M., Kivioja, T., Ignatiev, I., Kel, A., Taipale, J., Selivanova, G. (2012) Insights into p53 transcriptional function via genome-wide chromatin occupancy and gene expression analysis. Cell Death Differ. 19, 1992-2002. Link
  • Zawacka-Pankau, J., Grinkevich, V.V., Hunten, S., Nikulenkov, F., Gluch, A., Li, H., Enge, M., Kel, A., Selivanova, G. (2011) Inhibition of glycolytic enzymes mediated by pharmacologically activated p53: targeting Warburg effect to fight cancer. J. Biol. Chem. 286, 41600-41615. Link

Publications

Selection of publications authored by the geneXplain team:
  • Kalya M., Kel A., Wlochowitz D., Wingender E., Beißbarth T. (2021) IGFBP2 Is a Potential Master Regulator Driving the Dysregulated Gene Network Responsible for Short Survival in Glioblastoma Multiforme. Front Genet. 12, 670240. Link
  • Alachram H., Chereda H., Beißbarth T., Wingender E., Stegmaier P. (2021) Text mining-based word representations for biomedical data analysis and protein-protein interaction networks in machine learning tasks. PLoS One., 16(10), e0258623. Link
  • Kel A., Boyarskikh U., Stegmaier P., Leskov L.S., Sokolov A.V., Yevshin I., Mandrik N., Stelmashenko D., Koschmann J., Kel-Margoulis O., Krull M., Martínez-Cardús A., Moran S., Esteller M., Kolpakov F., Filipenko M., Wingender E. (2019) Walking pathways with positive feedback loops reveal DNA methylation biomarkers of colorectal cancer. BMC Bioinformatics. 20(Suppl 4),119. Link
  • Boyarskikh, U., Pintus, S., Mandrik, N., Stelmashenko, D., Kiselev, I., Evshin, I., Sharipov, R., Stegmaier, P., Kolpakov, F., Filipenko, M., Kel, A. (2018) Computational master-regulator search reveals mTOR and PI3K pathways responsible for low sensitivity of NCI-H292 and A427 lung cancer cell lines to cytotoxic action of p53 activator Nutlin-3. BMC Med. Genomics 11(Suppl 1), 12. Link
  • Kel, A.E., Stegmaier, P., Valeev, T., Koschmann, J., Poroikov, V., Kel-Margoulis, O.V. and Wingender, E. (2016) Multi-omics “upstream analysis” of regulatory genomic regions helps identifying targets against methotrexate resistance of colon cancer. EuPA Open Proteomics 13, 1-13. Link
  • Koschmann, J., Bhar, A., Stegmaier,P., Kel, A. E. and Wingender, E. (2015) “Upstream Analysis”: An integrated promoter-pathway analysis approach to causal interpretation of microarray data. Microarrays 4, 270-286. Link
  • Kel, A., Kolpakov, F., Poroikov, V., Selivanova, G. (2011) GeneXplain — Identification of Causal Biomarkers and Drug Targets in Personalized Cancer Pathways. J. Biomol. Tech. 22(Suppl), S16. Link

Get platform now

Make your registration and get your basic platform account for free now!

 

Register via this form to immediately get your free geneXplain platform account.

Select the “Registration for a free platform account” option at the top of the form to proceed.

 

Register your free account

 

Please note that the free account provides 15 MB disk space for performing the analysis. In case you will exceed this limit, an email will be sent to the address you have provided upon registration and your account will be transferred to a read/delete access mode within 3 days. Starting from that point, you will have 30 calendar days to either delete your data to meet the free account limit of 15 MB, or purchase disk space from us. In case no further action will be taken by you within this period, all your data will be permanently deleted, after what your account will be reactivated.

If you will have any questions towards the geneXplain platform disk space purchasing procedure, please contact us via this form or by email inquiries@genexplain.com.

Current platform release

geneXplain® platform release 7.2

Download full new features list

Updated databases:

  • HumanPSD™ is updated to version 2023.1
  • TRANSFAC® is updated to version 2023.1
  • TRANSPATH® is updated to version 2023.1

New workflows and methods:

  • The platform now provides a new section called “Combinatorial regulation”, which provides tools for analysis of combinations of TF binding sites in promoters, enhancers and silencers, and other regulatory genomic regions. In this section, in addition to our traditional Composite Module Analyst, we introduce a completely new branch of site search combinatorial analysis methods based on the sparse logistic regression (MEALR).
  • The new workflow Combinatorial regulation analysis of genomic or custom sequences scans input sequences for tissue and cell type- specific transcription factor binding regions using the newly created library of MEALR models delivered with the TRANSFAC® database.
  • The new method MEALR combinatorial regulation analysis applies combinatorial regulatory models (CRMs) based on the MEALR affinity score to classify or scan sequences for occurrences of combinations of transcription factor binding sites represented by TRANSFAC® PWMs.
  • The new method Extract TRANSFAC(R) PWMs from combinatorial regulation analysis extracts TRANSFAC® PWMs from a result table generated by the MEALR combinatorial regulation analysis. The PWMs represent transcription factor binding motifs for the TF in focus and its co-factors that constitute the combinatorial module predicted by the MEALR model.

New import possibility:

    Price request platform

    Free account

    Register  your free account account today!

    Registered users may go straight to the login.

    Demo Workflows

    Here, we list workflows that were used to prove the capabilities of the geneXplain platform, for instance in the cited publications. The links given direct you straight into the platform without requiring any registration.
    Using these demo workflows, you will be able to reproduce the published results and to learn more about the platform’s look and feel. To work with your own data, however, registration is required. Additional licensing is necessary for certain third-party products such as TRANSFAC® or TRANSPATH®.
    Workflows 1-3 are from our recent publication Koschmann et al., Microarrays 4, 270-286.
    1. Workflow “Identify enriched motifs in promoters“,
    applied to datasets of naphthalene-treated mouse liver and lung tissue.
    Find further instructions and explanations here.
    Origin of datasets:
    GEO GSE18858 and GSE17933
    Thomas, R.S., et al. (2011) Application of transcriptional benchmark dose values in quantitative cancer and noncancer risk assessment. Toxicol. Sci. 120, 194-205. PMID: 21097997
    Thomas, R.S., et al. (2009) Use of short-term transcriptional profiles to assess the long-term cancer-related safety of environmental and industrial chemicals.Toxicol. Sci. 112, 311-321. PMID: 19776212
    2. Workflow “Find master regulators in the TRANSPATH® network“,
    applied to datasets of naphthalene-treated mouse liver and lung tissue.
    Find further instructions and explanations here.
    The data sources are as for Workflow 1.
    3. Workflow “Enriched upstream analysis with TRANSFAC® and TRANSPATH® network“,
    applied to datasets of naphthalene-treated mouse liver and lung tissue.
    Find further instructions and explanations here.
    The data sources are as for Workflow 1.

    Workflow management

    Sequential launching of particular analysis modules can be saved as a graphically represented workflow. Modules are shown as purple rectangles, and outputs of each step serve as inputs into the next analysis step. A workflow that is specific for a given data set can be easily constructed by drag and drop of the required analysis modules. In addition, Java scripts and R scripts can be added directly within the platform, for more specific requirements of the analysis.
    The picture shows a small workflow for the gene set enrichment analysis (GSEA) of using four different ontologies: Gene Ontology (GO) Biological Process, GO Cellular Compartment, GO Molecular Function, and Reactome's functional assignments (see on the right side). The larger light blue boxes are analysis functions (program modules, "Bricks"). Green boxes stand for input files, especially user-defined inputs. Yellow boxes represent automatic delivery or output files. A workflow can be intuitively designs by simple drag-and-drop of the constituents and graphically connecting them.

    Workflow management in the geneXplain platform. (Click image to see the complete picture).

    See demo workflows for a collection of executable workflows: no registration required!

    Videos

    Intro video of the geneXplain platform.

    Information downloads

    Publications

    Drake, C., Zobl W., Wehr M., Koschmann J., De Luca D., Kühne B. A., Vrieling H. , Boei J. , Hansen T. , Escher S. E. (2023) Substantiate a read-across hypothesis by using transcriptome data—A case study on volatile diketones. Front. Toxicol. 5. Link

    Rajavel A., Klees S., Hui Y., Schmitt A.O., Gültas M. (2022) Deciphering the Molecular Mechanism Underlying African Animal Trypanosomiasis by Means of the 1000 Bull Genomes Project Genomic Dataset. Biology (Basel). 11(5), 742. Link

    Menck K., Wlochowitz D., Wachter A., Conradi L.C., Wolff A., Scheel A.H., Korf U., Wiemann S., Schildhaus H.U., Bohnenberger H., Wingender E., Pukrop T., Homayounfar K., Beißbarth T., Bleckmann A. (2022) High-Throughput Profiling of Colorectal Cancer Liver Metastases Reveals Intra- and Inter-Patient Heterogeneity in the EGFR and WNT Pathways Associated with Clinical Outcome. Cancers 14(9), 2084. Link

    Myer P.A., Kim H., Blümel A.M., Finnegan E., Kel A., Thompson T.V., Greally J.M., Prehn J.H., O’Connor D.P., Friedman R.A., Floratos A., Das S. (2022) Master Transcription Regulators and Transcription Factors Regulate Immune-Associated Differences Between Patients of African and European Ancestry With Colorectal Cancer. Gastro Hep Adv. 1(3), 328–341. Link

    Kawashima Y., Nagai H., Konno R., Ishikawa M., Nakajima D., Sato H., Nakamura R., Furuyashiki T., Ohara O. (2022) Single-Shot 10K Proteome Approach: Over 10,000 Protein Identifications by Data-Independent Acquisition-Based Single-Shot Proteomics with Ion Mobility Spectrometry. J Proteome Res. 21(6), 1418–1427. Link

    Klees S., Schlüter J.S., Schellhorn J., Bertram H., Kurzweg A.C., Ramzan F., Schmitt A.O., Gültas M. (2022) Comparative Investigation of Gene Regulatory Processes Underlying Avian Influenza Viruses in Chicken and Duck. Biology (Basel). 11(2), 219. Link

    Benjamin, S.J., Hawley, K.L., Vera-Licona, P., La Vake, C.J., Cervantes, J.L., Ruan, Y., Radolf, J.D., Salazar, J.C. (2021) Macrophage mediated recognition and clearance of Borrelia burgdorferi elicits MyD88-dependent and -independent phagosomal signals that contribute to phagocytosis and inflammation. BMC Immunol. 22, 32 Link

    Meier, T., Timm, M., Montani, M., Wilkens, L. (2021) Gene networks and transcriptional regulators associated with liver cancer development and progression. BMC Med. Genomics 14, 41. Link

    Ural, B.B., Yeung, S.T., Damani-Yokota, P., Devlin, J.C., de Vries, M., Vera-Licona, P., Samji, T., Sawai, C.M., Jang, G., Perez, O.A., Pham, Q., Maher, L., Loke, P., Dittmann, M., Reizis, B., Khanna, K.M. (2020) Identification of a nerve-associated, lung-resident interstitial macrophage subset with distinct localization and immunoregulatory properties. Sci. Immunol. 5, eaax8756. Link

    Mekonnen, Y.A., Gültas, M., Effa, K., Hanotte, O., Schmitt, A.O. (2019) Identification of Candidate Signature Genes and Key Regulators Associated With Trypanotolerance in the Sheko Breed. Front. Genet. 10, 1095. Link

    Blazquez, R., Wlochowitz, D., Wolff, A., Seitz, S., Wachter, A., Perera-Bel, J., Bleckmann, A., Beißbarth, T., Salinas, G., Riemenschneider, M.J., Proescholdt, M., Evert, M., Utpatel, K., Siam, L., Schatlo, B., Balkenhol, M., Stadelmann, C., Schildhaus, H.U., Korf, U., Reinz, E., Wiemann, S., Vollmer, E., Schulz, M., Ritter, U., Hanisch, U.K., Pukrop, T. (2018) PI3K: A master regulator of brain metastasis-promoting macrophages/microglia. Glia 66, 2438-2455. Link

    Orekhov, A.N., Oishi, Y., Nikiforov, N.G., Zhelankin, A.V., Dubrovsky, L., Sobenin, I.A., Kel, A., Stelmashenko, D., Makeev, V.J., Foxx, K., Jin, X., Kruth, H.S. Bukrinsky, M. (2018) Modified LDL Particles Activate Inflammatory Pathways in Monocyte-derived Macrophages: Transcriptome Analysis. Curr. Pharm. Des. 24, 3143-3151. Link

    Kalozoumi, G., Kel-Margoulis, O., Vafiadaki, E., Greenberg, D., Bernard, H., Soreq, H., Depaulis, A., Sanoudou, D. (2018) Glial responses during epileptogenesis in Mus musculus point to potential therapeutic targets. PLoS One 13, e0201742. Link

    Smetanina, M.A., Kel, A.E., Sevost’ianova, K.S., Maiborodin, I.V., Shevela, A.I., Zolotukhin, I.A., Stegmaier, P., Filipenko, M.L. (2018) DNA methylation and gene expression profiling reveal MFAP5 as a regulatory driver of extracellular matrix remodeling in varicose vein disease. Epigenomics 10, 1103-1119. Link

    Boyarskikh, U., Pintus, S., Mandrik, N., Stelmashenko, D., Kiselev, I., Evshin, I., Sharipov, R., Stegmaier, P., Kolpakov, F., Filipenko, M., Kel, A. (2018) Computational master-regulator search reveals mTOR and PI3K pathways responsible for low sensitivity of NCI-H292 and A427 lung cancer cell lines to cytotoxic action of p53 activator Nutlin-3. BMC Med. Genomics 11(Suppl 1), 12. Link

    Triska, M., Solovyev, V., Baranova, A., Kel, A., Tatarinova, T.V. (2017) Nucleotide patterns aiding in prediction of eukaryotic promoters. PLoS One 12, e0187243. Link

    Niehof, M., Hildebrandt, T., Danov, O., Arndt, K., Koschmann, J., Dahlmann, F., Hansen, T. and Sewald, K. (2017) RNA isolation from precision-cut lung slices (PCLS) from different species. BMC Res. Notes 10, 121. Link

    Mandić, A.D., Bennek, E., Verdier, J., Zhang, K., Roubrocks, S., Davis, R.J., Denecke, B., Gassler, N., Streetz, K., Kel, A., Hornef, M., Cubero, F. J., Trautwein, C. and Sellge, G. (2017) c-Jun N-terminal kinase 2 promotes enterocyte survival and goblet cell differentiation in the inflamed intestine. Mucosal Immunol. 10, 1211-1223. Link

    Pietrzyńska, M., Zembrzuska, J., Tomczak, R., Mikołajczyk, J., Rusińska-Roszak, D., Voelkel, A., Buchwald, T., Jampílek, J., Lukáč, M., Devínsky, F. (2016) Experimental and in silico investigations of organic phosphates and phosphonates sorption on polymer-ceramic monolithic materials and hydroxyapatite. Eur. J. Pharm. Sci. 93, 295-303. Link

    Kural, K.C., Tandon, N., Skoblov, M., Kel-Margoulis, O.V. and Baranova, A.V. (2016) Pathways of aging: comparative analysis of gene signatures in replicative senescence and stress induced premature senescence. BMC Genomics 17(Suppl 14), 1030. Link

    Kel, A.E., Stegmaier, P., Valeev, T., Koschmann, J., Poroikov, V., Kel-Margoulis, O.V. and Wingender, E. (2016) Multi-omics “upstream analysis” of regulatory genomic regions helps identifying targets against methotrexate resistance of colon cancer. EuPA Open Proteomics 13, 1-13. Link

    Ciribilli, Y., Singh, P., Inga, A., Borlak, J. (2016) c-Myc targeted regulators of cell metabolism in a transgenic mouse model of papillary lung adenocarcinoma. Oncotarget 7, 65514-65539. Link

    Wlochowitz, D., Haubrock, M., Arackal, J., Bleckmann, A., Wolff, A., Beißbarth, T., Wingender, E., Gültas, M. (2016) Computational Identification of Key Regulators in Two Different Colorectal Cancer Cell Lines. Front. Genet. 7, 42. Link

    Lee, E.H., Oh, J.H., Selvaraj, S., Park, S.M., Choi, M.S., Spanel, R., Yoon, S. and Borlak, J. (2016) Immunogenomics reveal molecular circuits of diclofenac induced liver injury in mice. Oncotarget 7, 14983-15017. Link

    Borlak, J., Singh, P. and Gazzana, G. (2015) Proteome mapping of epidermal growth factor induced hepatocellular carcinomas identifies novel cell metabolism targets and mitogen activated protein kinase signalling events. BMC Genomics 16, 124. Link

    Koschmann, J., Bhar, A., Stegmaier,P., Kel, A. E. and Wingender, E. (2015) “Upstream Analysis”: An integrated promoter-pathway analysis approach to causal interpretation of microarray data. Microarrays 4, 270-286. Link

    Shi, Y., Nikulenkov, F., Zawacka-Pankau, J., Li, H., Gabdoulline, R., Xu, J., Eriksson, S., Hedström, E., Issaeva, N., Kel, A., Arnér, E.S., Selivanova, G. (2014) ROS-dependent activation of JNK converts p53 into an efficient inhibitor of oncogenes leading to robust apoptosis. Cell Death Differ. 21, 612-623. Link

    Schlereth, K., Heyl, C., Krampitz, A.M., Mernberger, M., Finkernagel, F., Scharfe, M., Jarek, M., Leich, E., Rosenwald, A., Stiewe, T. (2013) Characterization of the p53 Cistrome – DNA Binding Cooperativity Dissects p53’s Tumor Suppressor Functions. PLoS Genet. 9, e1003726. PubMed

    Nikulenkov, F., Spinnler, C., Li, H., Tonelli, C., Shi, Y., Turunen, M., Kivioja, T., Ignatiev, I., Kel, A., Taipale, J., Selivanova, G. (2012) Insights into p53 transcriptional function via genome-wide chromatin occupancy and gene expression analysis. Cell Death Differ. 19, 1992-2002. PubMed

    Zawacka-Pankau, J., Grinkevich, V.V., Hunten, S., Nikulenkov, F., Gluch, A., Li, H., Enge, M., Kel, A., Selivanova, G. (2011) Inhibition of glycolytic enzymes mediated by pharmacologically activated p53: targeting Warburg effect to fight cancer. J. Biol. Chem. 286, 41600-41615. PubMed

    Kel, A., Kolpakov, F., Poroikov, V., Selivanova, G. (2011) GeneXplain — Identification of Causal Biomarkers and Drug Targets in Personalized Cancer Pathways. J. Biomol. Tech. 22(Suppl), S16. PubMed
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