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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 exper...
Autores principales: | , , |
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2006
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1413562/ https://www.ncbi.nlm.nih.gov/pubmed/16519817 http://dx.doi.org/10.1186/1471-2105-7-109 |
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author | Junker, Björn H Klukas, Christian Schreiber, Falk |
author_facet | Junker, Björn H Klukas, Christian Schreiber, Falk |
author_sort | Junker, Björn H |
collection | PubMed |
description | 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 . |
format | Text |
id | pubmed-1413562 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2006 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-14135622006-03-25 VANTED: A system for advanced data analysis and visualization in the context of biological networks Junker, Björn H Klukas, Christian Schreiber, Falk BMC Bioinformatics Software 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 . BioMed Central 2006-03-06 /pmc/articles/PMC1413562/ /pubmed/16519817 http://dx.doi.org/10.1186/1471-2105-7-109 Text en Copyright © 2006 Junker et al; licensee BioMed Central Ltd. |
spellingShingle | Software Junker, Björn H Klukas, Christian Schreiber, Falk VANTED: A system for advanced data analysis and visualization in the context of biological networks |
title | VANTED: A system for advanced data analysis and visualization in the context of biological networks |
title_full | VANTED: A system for advanced data analysis and visualization in the context of biological networks |
title_fullStr | VANTED: A system for advanced data analysis and visualization in the context of biological networks |
title_full_unstemmed | VANTED: A system for advanced data analysis and visualization in the context of biological networks |
title_short | VANTED: A system for advanced data analysis and visualization in the context of biological networks |
title_sort | vanted: a system for advanced data analysis and visualization in the context of biological networks |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1413562/ https://www.ncbi.nlm.nih.gov/pubmed/16519817 http://dx.doi.org/10.1186/1471-2105-7-109 |
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