Visualizing post genomics data-sets on customized pathway maps by ProMeTra – aeration-dependent gene expression and metabolism of Corynebacterium glutamicum as an example

Neuweger H, Persicke M, Albaum S, et al. Visualizing post genomics data-sets on customized pathway maps by ProMeTra – aeration-dependent gene expression and metabolism of Corynebacterium glutamicum as an example. BMC Systems Biology. 2009;3(1): 82.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

Paintomics: a web based tool for the joint visualization of transcriptomics and metabolomics data

Motivation: The development of the omics technologies such as transcriptomics, proteomics and metabolomics has made possible the realization of systems biology studies where biological systems are interrogated at different levels of biochemical activity (gene expression, protein activity and/or metabolite concentration). An effective approach to the analysis of these complex datasets is the joined visualization of the disparate biomolecular data on the framework of known biological pathways

VANTED: A system for advanced data analysis and visualization in the context of biological networks

BACKGROUND: Recent advances with high-throughput methods in life-science research have increased the need for automatized data analysis and visual exploration techniques. Sophisticated bioinformatics tools are essential to deduct biologically meaningful interpretations from the large amount of experimental data, and help to understand biological processes. RESULTS: We present VANTED, a tool for the visualization and analysis of networks with related experimental data. Data from large-scale biochemical experiments is uploaded into the software via a Microsoft Excel-based form. Then it can be mapped on a network that is either drawn with the tool itself, downloaded from the KEGG Pathway database, or imported using standard network exchange formats. Transcript, enzyme, and metabolite data can be presented in the context of their underlying networks, e. g. metabolic pathways or classification hierarchies. Visualization and navigation methods support the visual exploration of the data-enriched networks. Statistical methods allow analysis and comparison of multiple data sets such as different developmental stages or genetically different lines. Correlation networks can be automatically generated from the data and substances can be clustered according to similar behavior over time. As examples, metabolite profiling and enzyme activity data sets have been visualized in different metabolic maps, correlation networks have been generated and similar time patterns detected. Some relationships between different metabolites were discovered which are in close accordance with the literature. CONCLUSION: VANTED greatly helps researchers in the analysis and interpretation of biochemical data, and thus is a useful tool for modern biological research. VANTED as a Java Web Start Application including a user guide and example data sets is available free of charge at

KaPPA-View4: a metabolic pathway database for representation and analysis of correlation networks of gene co-expression and metabolite co-accumulation and omics data

Correlations of gene-to-gene co-expression and metabolite-to-metabolite co-accumulation calculated from large amounts of transcriptome and metabolome data are useful for uncovering unknown functions of genes, functional diversities of gene family members and regulatory mechanisms of metabolic pathway flows. Many databases and tools are available to interpret quantitative transcriptome and metabolome data, but there are only limited ones that connect correlation data to biological knowledge and can be utilized to find biological significance of it. We report here a new metabolic pathway database, KaPPA-View4 (http://kpv.kazusa.or.jp/kpv4/), which is able to overlay gene-to-gene and/or metabolite-to-metabolite relationships as curves on a metabolic pathway map, or on a combination of up to four maps. This representation would help to discover, for example, novel functions of a transcription factor that regulates genes on a metabolic pathway. Pathway maps of the Kyoto Encyclopedia of Genes and Genomes (KEGG) and maps generated from their gene classifications are available at KaPPA-View4 KEGG version (http://kpv.kazusa.or.jp/kpv4-kegg/). At present, gene co-expression data from the databases ATTED-II, COXPRESdb, CoP and MiBASE for human, mouse, rat, Arabidopsis, rice, tomato and other plants are available

Consolidating metabolite identifiers to enable contextual and multi-platform metabolomics data analysis

<p>Abstract</p> <p>Background</p> <p>Analysis of data from high-throughput experiments depends on the availability of well-structured data that describe the assayed biomolecules. Procedures for obtaining and organizing such meta-data on genes, transcripts and proteins have been streamlined in many data analysis packages, but are still lacking for metabolites. Chemical identifiers are notoriously incoherent, encompassing a wide range of different referencing schemes with varying scope and coverage. Online chemical databases use multiple types of identifiers in parallel but lack a common primary key for reliable database consolidation. Connecting identifiers of analytes found in experimental data with the identifiers of their parent metabolites in public databases can therefore be very laborious.</p> <p>Results</p> <p>Here we present a strategy and a software tool for integrating metabolite identifiers from local reference libraries and public databases that do not depend on a single common primary identifier. The program constructs groups of interconnected identifiers of analytes and metabolites to obtain a local metabolite-centric SQLite database. The created database can be used to map in-house identifiers and synonyms to external resources such as the KEGG database. New identifiers can be imported and directly integrated with existing data. Queries can be performed in a flexible way, both from the command line and from the statistical programming environment R, to obtain data set tailored identifier mappings.</p> <p>Conclusions</p> <p>Efficient cross-referencing of metabolite identifiers is a key technology for metabolomics data analysis. We provide a practical and flexible solution to this task and an open-source program, the metabolite masking tool (MetMask), available at <url>http://metmask.sourceforge.net</url>, that implements our ideas.</p

PaintOmics 3: a web resource for the pathway analysis and visualization of multi-omics data

[EN] 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.orgEuropean Union Seventh Framework Programme [FP7/2007-2013] under the grant agreement [306000-STATegra]; Marie Curie International Research Staff Exchange Scheme under grant agreement [612583-DEANN]; Spanish MINECO [BIO2012-40244]. INB Grant [PT17/0009/0015 - ISCIII-SGEFI / ERDF]. Funding for open access charge: MINECO [BIO2012-40244].Hernandez-De Diego, R.; Tarazona Campos, S.; Martínez-Mira, C.; Balzano-Nogueira, L.; Furió-Tarí, P.; Conesa, A.; Pappas, G. (2018). PaintOmics 3: a web resource for the pathway analysis and visualization of multi-omics data. Nucleic Acids Research. 46(W1):503-509. https://doi.org/10.1093/nar/gky466S50350946W

Recent advances of metabolomics in plant biotechnology

Biotechnology, including genetic modification, is a very important approach to regulate the production of particular metabolites in plants to improve their adaptation to environmental stress, to improve food quality, and to increase crop yield. Unfortunately, these approaches do not necessarily lead to the expected results due to the highly complex mechanisms underlying metabolic regulation in plants. In this context, metabolomics plays a key role in plant molecular biotechnology, where plant cells are modified by the expression of engineered genes, because we can obtain information on the metabolic status of cells via a snapshot of their metabolome. Although metabolome analysis could be used to evaluate the effect of foreign genes and understand the metabolic state of cells, there is no single analytical method for metabolomics because of the wide range of chemicals synthesized in plants. Here, we describe the basic analytical advancements in plant metabolomics and bioinformatics and the application of metabolomics to the biological study of plants

Analysis of transcriptomic differences between NK603 maize and near-isogenic varieties using RNA sequencing and RT-qPCR

Background The insertion of a transgene into a plant organism can, in addition to the intended effects, lead to unintended effects in the plants. To uncover such effects, we compared maize grains of two genetically modified varieties containing NK603 (AG8025RR2, AG9045RR2) to their non-transgenic counterparts (AG8025conv, AG9045conv) using high-throughput RNA sequencing. Moreover, in-depth analysis of these data was performed to reveal the biological meaning of detected differences. Results Uniquely mapped reads corresponded to 29,146 and 33,420 counts in the AG8025 and AG9045 varieties, respectively. An analysis using the R-Bioconductor package EdgeR revealed 3534 and 694 DEGs (significant differentially expressed genes) between the varieties AG8025RR2 and AG9045RR2, respectively, and their non-transgenic counterparts. Furthermore, a Deseq2 package revealed 2477 and 440 DEGs between AG8025RR2 and AG9045RR2, respectively, and their counterparts. We were able to confirm the RNA-seq results by the analysis of two randomly selected genes using RT-qPCR (reverse transcription quantitative PCR). PCA and heatmap analysis confirmed a robust data set that differentiates the genotypes even by transgenic event. A detailed analysis of the DEGs was performed by the functional annotation of GO (Gene Ontology), annotation/enrichment analysis of KEGG (Kyoto Encyclopedia of Genes and Genomes) ontologies and functional classification of resulting key genes using the DAVID Bioinformatics Package. Several biological processes and metabolic pathways were found to be significantly different in both variety pairs. Conclusion Overall, our data clearly demonstrate substantial differences between the analyzed transgenic varieties and their non-transgenic counterparts. These differences indicate that several unintended effects have occurred as a result of NK603 integration. Heatmap data imply that most of the transgenic insert effects are variety-dependent. However, identified key genes involved in affected pathways of both variety pairs show that transgenic independent effects cannot be excluded. Further research of different NK603 varieties is necessary to clarify the role of internal and external influences on gene expression. Nevertheless, our study suggests that RNA-seq analysis can be utilized as a tool to characterize unintended genetic effects in transgenic plants and may also be useful in the safety assessment and authorization of genetically modified (GM) plants.publishedVersio