Visualizing post genomics data-sets on customized pathway maps by ProMeTra-aeration-dependent gene expression and metabolism of Corynebacterium glutamicum as an example.
ABSTRACT: BACKGROUND: The rapid progress of post-genomic analyses, such as transcriptomics, proteomics, and metabolomics has resulted in the generation of large amounts of quantitative data covering and connecting the complete cascade from genotype to phenotype for individual organisms. Various benefits can be achieved when these "Omics" data are integrated, such as the identification of unknown gene functions or the elucidation of regulatory networks of whole organisms. In order to be able to obtain deeper insights in the generated datasets, it is of utmost importance to present the data to the researcher in an intuitive, integrated, and knowledge-based environment. Therefore, various visualization paradigms have been established during the last years. The visualization of "Omics" data using metabolic pathway maps is intuitive and has been applied in various software tools. It has become obvious that the application of web-based and user driven software tools has great potential and benefits from the use of open and standardized formats for the description of pathways. RESULTS: In order to combine datasets from heterogeneous "Omics" sources, we present the web-based ProMeTra system that visualizes and combines datasets from transcriptomics, proteomics, and metabolomics on user defined metabolic pathway maps. Therefore, structured exchange of data with our "Omics" applications Emma 2, Qupe and MeltDB is employed. Enriched SVG images or animations are generated and can be obtained via the user friendly web interface. To demonstrate the functionality of ProMeTra, we use quantitative data obtained during a fermentation experiment of the L-lysine producing strain Corynebacterium glutamicum DM1730. During fermentation, oxygen supply was switched off in order to perturb the system and observe its reaction. At six different time points, transcript abundances, intracellular metabolite pools, as well as extracellular glucose, lactate, and L-lysine levels were determined. CONCLUSION: The interpretation and visualization of the results of this complex experiment was facilitated by the ProMeTra software. Both transcriptome and metabolome data were visualized on a metabolic pathway map. Visual inspection of the combined data confirmed existing knowledge but also delivered novel correlations that are of potential biotechnological importance.
Project description:BACKGROUND: Integrative and comparative analyses of multiple transcriptomics, proteomics and metabolomics datasets require an intensive knowledge of tools and background concepts. Thus, it is challenging for users to perform such analyses, highlighting the need for a single tool for such purposes. The 3Omics one-click web tool was developed to visualize and rapidly integrate multiple human inter- or intra-transcriptomic, proteomic, and metabolomic data by combining five commonly used analyses: correlation networking, coexpression, phenotyping, pathway enrichment, and GO (Gene Ontology) enrichment. RESULTS: 3Omics generates inter-omic correlation networks to visualize relationships in data with respect to time or experimental conditions for all transcripts, proteins and metabolites. If only two of three omics datasets are input, then 3Omics supplements the missing transcript, protein or metabolite information related to the input data by text-mining the PubMed database. 3Omics' coexpression analysis assists in revealing functions shared among different omics datasets. 3Omics' phenotype analysis integrates Online Mendelian Inheritance in Man with available transcript or protein data. Pathway enrichment analysis on metabolomics data by 3Omics reveals enriched pathways in the KEGG/HumanCyc database. 3Omics performs statistical Gene Ontology-based functional enrichment analyses to display significantly overrepresented GO terms in transcriptomic experiments. Although the principal application of 3Omics is the integration of multiple omics datasets, it is also capable of analyzing individual omics datasets. The information obtained from the analyses of 3Omics in Case Studies 1 and 2 are also in accordance with comprehensive findings in the literature. CONCLUSIONS: 3Omics incorporates the advantages and functionality of existing software into a single platform, thereby simplifying data analysis and enabling the user to perform a one-click integrated analysis. Visualization and analysis results are downloadable for further user customization and analysis. The 3Omics software can be freely accessed at http://3omics.cmdm.tw.
Project description:Multi-omics analyses, combining transcriptomics, genomics, proteomics, and so on, have led to important insights in many areas of biology and medicine. To support these analyses, software that can handle the difficulties associated with multi-omics datasets is crucial. Here, we describe Panomicon, a web-based, interactive analysis environment for multi-omics data. Building on Toxygates, a tool previously created to study single-omics data that features interactive clustering, heatmaps, and user data uploads, Panomicon introduces improvements for the storage and handling of additional omics types, as well as tools for the generation and visualization of interaction networks between different types of omics data. Panomicon is a new type of environment for the collaborative study of multi-omics data, both for users uploading data to our server and for groups wishing to host their own deployment of Panomicon. We demonstrate Panomicon's capabilities by revisiting a microRNA-mRNA interaction networks study in a non-small cell lung cancer dataset.
Project description:Pathway analysis is widely used in omics studies. Pathway-based data integration and visualization is a critical component of the analysis. To address this need, we recently developed a novel R package called Pathview. Pathview maps, integrates and renders a large variety of biological data onto molecular pathway graphs. Here we developed the Pathview Web server, as to make pathway visualization and data integration accessible to all scientists, including those without the special computing skills or resources. Pathview Web features an intuitive graphical web interface and a user centered design. The server not only expands the core functions of Pathview, but also provides many useful features not available in the offline R package. Importantly, the server presents a comprehensive workflow for both regular and integrated pathway analysis of multiple omics data. In addition, the server also provides a RESTful API for programmatic access and conveniently integration in third-party software or workflows. Pathview Web is openly and freely accessible at https://pathview.uncc.edu/.
Project description:Personalized medicine is promising a revolution for medicine and human biology in the 21st century. The scientific foundation for this revolution is accomplished by analyzing biological high-throughput data sets from genomics, transcriptomics, proteomics, and metabolomics. Currently, access to these data has been limited to either rather simple Web-based tools, which do not grant much insight or analysis by trained specialists, without firsthand involvement of the physician. Here, we present the novel Web-based tool "BioMiner," which was developed within the scope of an international and interdisciplinary project (SYSTHER) and gives access to a variety of high-throughput data sets. It provides the user with convenient tools to analyze complex cross-omics data sets and grants enhanced visualization abilities. BioMiner incorporates transcriptomic and cross-omics high-throughput data sets, with a focus on cancer. A public instance of BioMiner along with the database is available at http://systherDB.microdiscovery.de/, login and password: "systher"; a tutorial detailing the usage of BioMiner can be found in the Supplementary File.
Project description:The amount of biological data, generated with (single cell) omics technologies, is rapidly increasing, thereby exacerbating bottlenecks in the data analysis and interpretation of omics experiments. Data mining platforms that facilitate non-bioinformatician experimental scientists to analyze a wide range of experimental designs and data types can alleviate such bottlenecks, aiding in the exploration of (newly generated or publicly available) omics datasets. Here, we present BIOMEX, a browser-based software, designed to facilitate the Biological Interpretation Of Multi-omics EXperiments by bench scientists. BIOMEX integrates state-of-the-art statistical tools and field-tested algorithms into a flexible but well-defined workflow that accommodates metabolomics, transcriptomics, proteomics, mass cytometry and single cell data from different platforms and organisms. The BIOMEX workflow is accompanied by a manual and video tutorials that provide the necessary background to navigate the interface and get acquainted with the employed methods. BIOMEX guides the user through omics-tailored analyses, such as data pretreatment and normalization, dimensionality reduction, differential and enrichment analysis, pathway mapping, clustering, marker analysis, trajectory inference, meta-analysis and others. BIOMEX is fully interactive, allowing users to easily change parameters and generate customized plots exportable as high-quality publication-ready figures. BIOMEX is open source and freely available at https://www.vibcancer.be/software-tools/biomex.
Project description:The increasing availability of multi-omic platforms poses new challenges to data analysis. Joint visualization of multi-omics data is instrumental in better understanding interconnections across molecular layers and in fully utilizing the multi-omic resources available to make biological discoveries. We present here PaintOmics 3, a web-based resource for the integrated visualization of multiple omic data types onto KEGG pathway diagrams. PaintOmics 3 combines server-end capabilities for data analysis with the potential of modern web resources for data visualization, providing researchers with a powerful framework for interactive exploration of their multi-omics information. Unlike other visualization tools, PaintOmics 3 covers a comprehensive pathway analysis workflow, including automatic feature name/identifier conversion, multi-layered feature matching, pathway enrichment, network analysis, interactive heatmaps, trend charts, and more. It accepts a wide variety of omic types, including transcriptomics, proteomics and metabolomics, as well as region-based approaches such as ATAC-seq or ChIP-seq data. The tool is freely available at www.paintomics.org.
Project description:MOTIVATION:Systems biology faces two key challenges when dealing with large amounts of disparate data produced by different experiments: the integration of results across different experiments, and the extraction of meaningful information from the data produced by these experiments. An ongoing challenge is to provide better tools that can mine data patterns that could not have been discovered through simple visualization. Such mining capabilities also need to be coupled with intuitive visualization to portray those findings. We introduce a software toolbox entitled BioNetApp to mine these patterns and visualize them across all experiments. RESULTS:BioNetApp is an interactive visual data mining software for analyzing high-volume molecular expression data obtained from multiple 'omics experiments. By integrating visualization, statistical methods, and data mining techniques, BioNetApp can perform interactive correlative and comparative analysis along time-course studies of molecular expression data. Correlation analysis provides several visualization features such as Kamada-Kawai, Fruchterman-Reingold Spring embedding network layouts, in addition to single circle, multiple circle and heatmap layouts, whereas comparative analysis presents expression-data distributions across samples, groups, and time points with boxplot display, outlier detection, and data curve fitting. BioNetApp also provides data clustering based on molecular concentrations using Self Organizing Maps (SOM), K-Means, K-Medoids, and Farthest First algorithms. CONCLUSION:BioNetApp has been utilized in a metabolomics study to investigate the metabolite abundance changes in alcohol induced fatty liver, where pair-wise analyses of metabolome concentration revealed correlation networks and interesting patterns in the metabolomics dataset. This study case demonstrates the effectiveness of the BioNetApp software as an interactive visual analysis tool for molecular expression data in systems biology. The BioNetApp software is freely available under GNU GPL license and can be downloaded (including the case-study data and user-manual) at: https://doi.org/10.5281/zenodo.2563129.
Project description:The Malaria Genome Exploration Tool (MaGnET) is a software tool enabling intuitive 'exploration-style' visualization of functional genomics data relating to the malaria parasite, Plasmodium falciparum. MaGnET provides innovative integrated graphic displays for different datasets, including genomic location of genes, mRNA expression data, protein-protein interactions and more. Any selection of genes to explore made by the user is easily carried over between the different viewers for different datasets, and can be changed interactively at any point (without returning to a search).Free online use (Java Web Start) or download (Java application archive and MySQL database; requires local MySQL installation) at http://email@example.com or firstname.lastname@example.orgSupplementary data are available at Bioinformatics online.
Project description:The value of metabolomics in translational research is undeniable, and metabolomics data are increasingly generated in large cohorts. The functional interpretation of disease-associated metabolites though is difficult, and the biological mechanisms that underlie cell type or disease-specific metabolomics profiles are oftentimes unknown. To help fully exploit metabolomics data and to aid in its interpretation, analysis of metabolomics data with other complementary omics data, including transcriptomics, is helpful. To facilitate such analyses at a pathway level, we have developed RaMP (Relational database of Metabolomics Pathways), which combines biological pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, WikiPathways, and the Human Metabolome DataBase (HMDB). To the best of our knowledge, an off-the-shelf, public database that maps genes and metabolites to biochemical/disease pathways and can readily be integrated into other existing software is currently lacking. For consistent and comprehensive analysis, RaMP enables batch and complex queries (e.g., list all metabolites involved in glycolysis and lung cancer), can readily be integrated into pathway analysis tools, and supports pathway overrepresentation analysis given a list of genes and/or metabolites of interest. For usability, we have developed a RaMP R package (https://github.com/Mathelab/RaMP-DB), including a user-friendly RShiny web application, that supports basic simple and batch queries, pathway overrepresentation analysis given a list of genes or metabolites of interest, and network visualization of gene-metabolite relationships. The package also includes the raw database file (mysql dump), thereby providing a stand-alone downloadable framework for public use and integration with other tools. In addition, the Python code needed to recreate the database on another system is also publicly available (https://github.com/Mathelab/RaMP-BackEnd). Updates for databases in RaMP will be checked multiple times a year and RaMP will be updated accordingly.