geneXplain-platform

geneXplain platform

The comprehensive bioinformatics platform, also available as Java 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 start page

The start page provides an easy access to a number of application areas. Click image for full picture.

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.

Major features

New workflows

  • A new set of workflows is summarized under the start page button Metabolism
  • Flux balance analysis to identify enzymes, reactions and metabolites including clustering and mapping of flux data
  • Enriched upstream analysis (TRANSFAC® and TRANSPATH®)
  • Focused upstream analysis (TRANSFAC® and TRANSPATH®)
  • Upstream analysis with feedback loop (TRANSFAC® and TRANSPATH®) indicates master regulators with expression values (fold changes) from the input set
  • Identify enriched motifs in tissue specific tracks (TRANSFAC®)
  • Cross-species identification of enriched motifs in promoters
  • Cross-species mapping to ontologies

New and updated databases

  • Human Metabolic Reaction (HMR): HMR2 contains 3.765 genes associated with over 8.000 reactions and over 3.000 unique metabolites.
  • Human metabolism global reconstruction (Recon 2): The most comprehensive representation of human metabolism that is applicable to computational modeling
  • Integration of the latest TRANSFAC® and TRANSPATH® versions (releases 2019.3)
  • HOmo sapiens COmprehensive MOdel COllection (HOCOMOCO): This database provides transcription factor (TF) binding models for 601 human and 396 mouse TFs, partially based on TRANSFAC.
  • Reactome database has been updated to version 63.
  • Integration of new Ensembl versions (Release 96), Ensembl Homo sapiens (hg38), Ensembl Mus musculus (mm10), Ensembl Rattus norvegicus (rn6)

New methods

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Subread – aligns DNA- and RNA-seq reads

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Subjunc – exon–exon junctions

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featureCounts – counting of reads

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exactSNP – SNPs for individual samples

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Limma voom – Differential expression analysis

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Guided limma – Linear model analysis

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HISAT2 – Fast and sensitive alignment tool

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HTseq Count – reads mapping for each feature

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HTseq QA – Quality report for reads

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EdgeR – Empirical Analysis of Digital Gene Expression Data in R

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Plotting – Pie and bar charts

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Tree map – Reduce functional classification terms

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Get miRNA targets

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miRNA feed forward loops

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Analyze miRNA target enrichment

Current platform release

geneXplain® platform 6.3 release

Download full new features list

Database updates:

  • HumanPSD™ is updated to version 2021.1 (January 2021).
  • TRANSFAC® is updated to version 2021.1 (January 2021).
  • TRANSPATH® is updated to version 2021.1 (January 2021).

New workflow:

  • Identify enriched motifs in cell specific promoters (TRANSFAC(R))

This workflow searches for enriched transcription factor binding sites (TFBSs) in a set of gene promoters versus a random promoter set. The input gene set is used to extract promoter regions by mapping it against the TSS locations defined in the Fantom5 (Nature 507:462–470) database for one selected cell-type among 172 available cell-types. The over-represented sites identified with the MEALR method are converted into a profile, which is used for a second round of site search analysis and ends up with the identification of potential transcription factors.

New features:

  • MEALR classifier (tracks)

MEALR searches for a combination of transcription factor binding motifs that discriminate between a positive (Yes) and a negative (No) sequence set. This tool takes a sparse logistic regression model derived with MEALR and applies it to new sequences to predict whether they can be bound by TF complexes or contribute to gene regulation in the same way as the Yes sequences used to train the MEALR model.

  • Random forest prediction

Random forests are a combination of tree predictors and that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. This statistical method performs a classification or regression with a random forest model, based on Breiman (published in Machine Learning). Please refer to documentation of the R randomForest package for computational details. Random forests can be trained using the Train random forest tool (see below).

  • Train random forest

This method can be used to train a random forest model for classification, regression, or clustering. Please refer to documentation of the R randomForest package for computational details. Except for unsupervised models, the trained random forests can be used for further classification or regression analysis with the Random forest prediction tool.

  • t-SNE

The new tool contains a R wrapper around the fast T-distributed Stochastic Neighbor Embedding implementation by Van der Maaten (more information on the original implementation is here). The tool can be used for data visualization using the t-SNE algorithm.

Enhancements:

  • Fantom5 workflows now available for both, hg19 and hg38 genome versions.
  • The workflow for analyzing a SNP list with TRANSFAC database is now available for the hg38 genome version.
  • New example with Gene Expression Omnibus data (GSE156063): Upper airway gene expression differentiates COVID-19 from other acute respiratory illnesses and reveals suppression of innate immune responses by SARS-CoV-2, Expression profiling by high throughput sequencing, Illumina NovaSeq 6000 Homo sapiens.

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    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

    Meier, T., Timm, M., Montani, M., & Wilkens, L. (2021) Gene networks and transcriptional regulators associated with liver cancer development and progression. BMC medical genomics, 14(1). doi.org/10.1186/s12920-021-00883-5 Link

    Mekonnen, Y. A., Gültas, M., Effa, K., Hanotte, O. and Schmitt, A. O. (2019) Identification of Candidate Signature Genes and Key Regulators Associated With Trypanotolerance in the Sheko Breed. Front. Genet. 10:1095. doi: 10.3389/fgene.2019.01095. 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 UK., Pukrop T. (2018) PI3K: A master regulator of brain metastasis-promoting macrophages/microglia. Glia. 66(11):2438-2455. doi: 10.1002/glia.23485. Epub 2018 Oct 25. 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. and Bukrinsky, M. (2018) Modified Ldl Particles Activate Inflammatory Pathways In Monocyte-Derived Macrophages: Transcriptome Analysis. Curr. Pharm. Des., 11. doi: 10.2174/1381612824666180911120039. 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(8):e0201742. doi: 10.1371/journal.pone.0201742. 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(8):1103-1119. doi: 10.2217/epi-2018-0001. 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. doi: 10.1186/s12920-018-0330-5. Link
    Triska, M., Solovyev, V., Baranova, A., Kel, A., Tatarinova, T.V. (2017) Nucleotide patterns aiding in prediction of eukaryotic promoters. PLoS One. 12(11):e0187243. doi: 10.1371/journal.pone.0187243. 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. doi: 10.1186/s13104-017-2447-6. 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(5):1211-1223. doi: 10.1038/mi.2016.125. 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. doi: 10.1016/j.ejps.2016.08.033. 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. doi: 10.1186/s12864-016-3352-4. 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. doi: 10.1016/j.euprot.2016.09.002. 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. doi: 10.18632/oncotarget.11804. 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. doi: 10.3389/fgene.2016.00042. 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. doi:10.18632/oncotarget.7698. 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. doi:10.1186/s12864-015-1312-z. 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. doi:10.3390/microarrays4020270. 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. doi:10.1038/cdd.2013.186 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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