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MarVis-Pathway: integrative and exploratory pathway analysis of non-targeted metabolomics data.

Kaever A, Landesfeind M, Feussner K, Mosblech A, Heilmann I, Morgenstern B, Feussner I, Meinicke P - Metabolomics (2014)

Bottom Line: In many applications, this identification needs to be guided by expert knowledge and interactive tools for exploratory data analysis can significantly support this process.The analysis is supported by an extensive framework for pathway enrichment and meta-analysis.This application confirms jasmonate biosynthesis as important metabolic pathway that is upregulated during the wound response of Arabidopsis plants.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioinformatics, Institute of Microbiology and Genetics, Georg-August-University Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany.

ABSTRACT

A central aim in the evaluation of non-targeted metabolomics data is the detection of intensity patterns that differ between experimental conditions as well as the identification of the underlying metabolites and their association with metabolic pathways. In this context, the identification of metabolites based on non-targeted mass spectrometry data is a major bottleneck. In many applications, this identification needs to be guided by expert knowledge and interactive tools for exploratory data analysis can significantly support this process. Additionally, the integration of data from other omics platforms, such as DNA microarray-based transcriptomics, can provide valuable hints and thereby facilitate the identification of metabolites via the reconstruction of related metabolic pathways. We here introduce the MarVis-Pathway tool, which allows the user to identify metabolites by annotation of pathways from cross-omics data. The analysis is supported by an extensive framework for pathway enrichment and meta-analysis. The tool allows the mapping of data set features by ID, name, and accurate mass, and can incorporate information from adduct and isotope correction of mass spectrometry data. MarVis-Pathway was integrated in the MarVis-Suite (http://marvis.gobics.de), which features the seamless highly interactive filtering, combination, clustering, and visualization of omics data sets. The functionality of the new software tool is illustrated using combined mass spectrometry and DNA microarray data. This application confirms jasmonate biosynthesis as important metabolic pathway that is upregulated during the wound response of Arabidopsis plants.

No MeSH data available.


Related in: MedlinePlus

Interactive workflow of data analysis within the MarVis-Suite
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Related In: Results  -  Collection


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Fig1: Interactive workflow of data analysis within the MarVis-Suite

Mentions: In order to identify data set features in a functional context, we introduce the MarVis-Pathway tool, which allows the annotation and analysis of organism-specific pathways from the KEGG and BioCyc database collections in combination with an SEA meta-analysis framework for multi-omics data sets (Kaever et al. 2014). The mapping of features to database entries is based on the matching of IDs, names, or accurate masses. MarVis-Pathway thereby completes the MarVis-Suite pipeline by providing a knowledge-based interpretation of results from explorative data analysis (see Fig. 1 for an overview on the interactive workflow). In addition, we introduce a signal-to-noise ratio-based ranking and filtering method for the MarVis-Filter tool, which features the statistical analysis of heterogeneous omics data based on minimal assumptions and which can be easily used for exploratory data analysis by modifying the signal definition. The proposed methods and tools are applied to data sets combining LC/MS with DNA microarray data in the context of a cross-omics study on the wound response of Arabidopsis plants, which represents a well-established model system. We show that the strength of MarVis-Pathway lies in the enhancement of analysis and interpretation of non-targeted LC/MS data sets in combination with transcriptomics data.


MarVis-Pathway: integrative and exploratory pathway analysis of non-targeted metabolomics data.

Kaever A, Landesfeind M, Feussner K, Mosblech A, Heilmann I, Morgenstern B, Feussner I, Meinicke P - Metabolomics (2014)

Interactive workflow of data analysis within the MarVis-Suite
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4419191&req=5

Fig1: Interactive workflow of data analysis within the MarVis-Suite
Mentions: In order to identify data set features in a functional context, we introduce the MarVis-Pathway tool, which allows the annotation and analysis of organism-specific pathways from the KEGG and BioCyc database collections in combination with an SEA meta-analysis framework for multi-omics data sets (Kaever et al. 2014). The mapping of features to database entries is based on the matching of IDs, names, or accurate masses. MarVis-Pathway thereby completes the MarVis-Suite pipeline by providing a knowledge-based interpretation of results from explorative data analysis (see Fig. 1 for an overview on the interactive workflow). In addition, we introduce a signal-to-noise ratio-based ranking and filtering method for the MarVis-Filter tool, which features the statistical analysis of heterogeneous omics data based on minimal assumptions and which can be easily used for exploratory data analysis by modifying the signal definition. The proposed methods and tools are applied to data sets combining LC/MS with DNA microarray data in the context of a cross-omics study on the wound response of Arabidopsis plants, which represents a well-established model system. We show that the strength of MarVis-Pathway lies in the enhancement of analysis and interpretation of non-targeted LC/MS data sets in combination with transcriptomics data.

Bottom Line: In many applications, this identification needs to be guided by expert knowledge and interactive tools for exploratory data analysis can significantly support this process.The analysis is supported by an extensive framework for pathway enrichment and meta-analysis.This application confirms jasmonate biosynthesis as important metabolic pathway that is upregulated during the wound response of Arabidopsis plants.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioinformatics, Institute of Microbiology and Genetics, Georg-August-University Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany.

ABSTRACT

A central aim in the evaluation of non-targeted metabolomics data is the detection of intensity patterns that differ between experimental conditions as well as the identification of the underlying metabolites and their association with metabolic pathways. In this context, the identification of metabolites based on non-targeted mass spectrometry data is a major bottleneck. In many applications, this identification needs to be guided by expert knowledge and interactive tools for exploratory data analysis can significantly support this process. Additionally, the integration of data from other omics platforms, such as DNA microarray-based transcriptomics, can provide valuable hints and thereby facilitate the identification of metabolites via the reconstruction of related metabolic pathways. We here introduce the MarVis-Pathway tool, which allows the user to identify metabolites by annotation of pathways from cross-omics data. The analysis is supported by an extensive framework for pathway enrichment and meta-analysis. The tool allows the mapping of data set features by ID, name, and accurate mass, and can incorporate information from adduct and isotope correction of mass spectrometry data. MarVis-Pathway was integrated in the MarVis-Suite (http://marvis.gobics.de), which features the seamless highly interactive filtering, combination, clustering, and visualization of omics data sets. The functionality of the new software tool is illustrated using combined mass spectrometry and DNA microarray data. This application confirms jasmonate biosynthesis as important metabolic pathway that is upregulated during the wound response of Arabidopsis plants.

No MeSH data available.


Related in: MedlinePlus