Interactive visualization of clusters in microarray data: an efficient tool for improved metabolic analysis of E. coli

Background Interpretation of comprehensive DNA microarray data sets is a challenging task for biologists and process engineers where scientific assistance of statistics and bioinformatics is essential. Interdisciplinary cooperation and concerted development of software-tools for simplified and accel...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Scharl, Theresa [verfasserIn] Striedner, Gerald Pötschacher, Florentina Leisch, Friedrich Bayer, Karl

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2009

Schlagwörter:	Microarray Data Cluster Solution Limited Induction Cluster Centroid Neighborhood Graph

Anmerkung:	© Scharl et al; licensee BioMed Central Ltd. 2009. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (

Übergeordnetes Werk:	Enthalten in: Microbial cell factories - London : Biomed Central, 2002, 8(2009), 1 vom: 15. Juli
Übergeordnetes Werk:	volume:8 ; year:2009 ; number:1 ; day:15 ; month:07

Links:	Volltext

DOI / URN:	10.1186/1475-2859-8-37

Katalog-ID:	SPR028557174

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520			\|a Background Interpretation of comprehensive DNA microarray data sets is a challenging task for biologists and process engineers where scientific assistance of statistics and bioinformatics is essential. Interdisciplinary cooperation and concerted development of software-tools for simplified and accelerated data analysis and interpretation is the key to overcome the bottleneck in data-analysis workflows. This approach is exemplified by gcExplorer an interactive visualization toolbox based on cluster analysis. Clustering is an important tool in gene expression data analysis to find groups of co-expressed genes which can finally suggest functional pathways and interactions between genes. The visualization of gene clusters gives practitioners an understanding of the cluster structure of their data and makes it easier to interpret the cluster results. Results In this study the interactive visualization toolbox gcExplorer is applied to the interpretation of E. coli microarray data. The data sets derive from two fedbatch experiments conducted in order to investigate the impact of different induction strategies on the host metabolism and product yield. The software enables direct graphical comparison of these two experiments. The identification of potentially interesting gene candidates or functional groups is substantially accelerated and eased. Conclusion It was shown that gcExplorer is a very helpful tool to gain a general overview of microarray experiments. Interesting gene expression patterns can easily be found, compared among different experiments and combined with information about gene function from publicly available databases.
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allfields	10.1186/1475-2859-8-37 doi (DE-627)SPR028557174 (SPR)1475-2859-8-37-e DE-627 ger DE-627 rakwb eng Scharl, Theresa verfasserin aut Interactive visualization of clusters in microarray data: an efficient tool for improved metabolic analysis of E. coli 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Scharl et al; licensee BioMed Central Ltd. 2009. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Interpretation of comprehensive DNA microarray data sets is a challenging task for biologists and process engineers where scientific assistance of statistics and bioinformatics is essential. Interdisciplinary cooperation and concerted development of software-tools for simplified and accelerated data analysis and interpretation is the key to overcome the bottleneck in data-analysis workflows. This approach is exemplified by gcExplorer an interactive visualization toolbox based on cluster analysis. Clustering is an important tool in gene expression data analysis to find groups of co-expressed genes which can finally suggest functional pathways and interactions between genes. The visualization of gene clusters gives practitioners an understanding of the cluster structure of their data and makes it easier to interpret the cluster results. Results In this study the interactive visualization toolbox gcExplorer is applied to the interpretation of E. coli microarray data. The data sets derive from two fedbatch experiments conducted in order to investigate the impact of different induction strategies on the host metabolism and product yield. The software enables direct graphical comparison of these two experiments. The identification of potentially interesting gene candidates or functional groups is substantially accelerated and eased. Conclusion It was shown that gcExplorer is a very helpful tool to gain a general overview of microarray experiments. Interesting gene expression patterns can easily be found, compared among different experiments and combined with information about gene function from publicly available databases. Microarray Data (dpeaa)DE-He213 Cluster Solution (dpeaa)DE-He213 Limited Induction (dpeaa)DE-He213 Cluster Centroid (dpeaa)DE-He213 Neighborhood Graph (dpeaa)DE-He213 Striedner, Gerald aut Pötschacher, Florentina aut Leisch, Friedrich aut Bayer, Karl aut Enthalten in Microbial cell factories London : Biomed Central, 2002 8(2009), 1 vom: 15. Juli (DE-627)355987651 (DE-600)2091377-1 1475-2859 nnns volume:8 year:2009 number:1 day:15 month:07 https://dx.doi.org/10.1186/1475-2859-8-37 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2009 1 15 07
spelling	10.1186/1475-2859-8-37 doi (DE-627)SPR028557174 (SPR)1475-2859-8-37-e DE-627 ger DE-627 rakwb eng Scharl, Theresa verfasserin aut Interactive visualization of clusters in microarray data: an efficient tool for improved metabolic analysis of E. coli 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Scharl et al; licensee BioMed Central Ltd. 2009. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Interpretation of comprehensive DNA microarray data sets is a challenging task for biologists and process engineers where scientific assistance of statistics and bioinformatics is essential. Interdisciplinary cooperation and concerted development of software-tools for simplified and accelerated data analysis and interpretation is the key to overcome the bottleneck in data-analysis workflows. This approach is exemplified by gcExplorer an interactive visualization toolbox based on cluster analysis. Clustering is an important tool in gene expression data analysis to find groups of co-expressed genes which can finally suggest functional pathways and interactions between genes. The visualization of gene clusters gives practitioners an understanding of the cluster structure of their data and makes it easier to interpret the cluster results. Results In this study the interactive visualization toolbox gcExplorer is applied to the interpretation of E. coli microarray data. The data sets derive from two fedbatch experiments conducted in order to investigate the impact of different induction strategies on the host metabolism and product yield. The software enables direct graphical comparison of these two experiments. The identification of potentially interesting gene candidates or functional groups is substantially accelerated and eased. Conclusion It was shown that gcExplorer is a very helpful tool to gain a general overview of microarray experiments. Interesting gene expression patterns can easily be found, compared among different experiments and combined with information about gene function from publicly available databases. Microarray Data (dpeaa)DE-He213 Cluster Solution (dpeaa)DE-He213 Limited Induction (dpeaa)DE-He213 Cluster Centroid (dpeaa)DE-He213 Neighborhood Graph (dpeaa)DE-He213 Striedner, Gerald aut Pötschacher, Florentina aut Leisch, Friedrich aut Bayer, Karl aut Enthalten in Microbial cell factories London : Biomed Central, 2002 8(2009), 1 vom: 15. Juli (DE-627)355987651 (DE-600)2091377-1 1475-2859 nnns volume:8 year:2009 number:1 day:15 month:07 https://dx.doi.org/10.1186/1475-2859-8-37 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2009 1 15 07
allfields_unstemmed	10.1186/1475-2859-8-37 doi (DE-627)SPR028557174 (SPR)1475-2859-8-37-e DE-627 ger DE-627 rakwb eng Scharl, Theresa verfasserin aut Interactive visualization of clusters in microarray data: an efficient tool for improved metabolic analysis of E. coli 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Scharl et al; licensee BioMed Central Ltd. 2009. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Interpretation of comprehensive DNA microarray data sets is a challenging task for biologists and process engineers where scientific assistance of statistics and bioinformatics is essential. Interdisciplinary cooperation and concerted development of software-tools for simplified and accelerated data analysis and interpretation is the key to overcome the bottleneck in data-analysis workflows. This approach is exemplified by gcExplorer an interactive visualization toolbox based on cluster analysis. Clustering is an important tool in gene expression data analysis to find groups of co-expressed genes which can finally suggest functional pathways and interactions between genes. The visualization of gene clusters gives practitioners an understanding of the cluster structure of their data and makes it easier to interpret the cluster results. Results In this study the interactive visualization toolbox gcExplorer is applied to the interpretation of E. coli microarray data. The data sets derive from two fedbatch experiments conducted in order to investigate the impact of different induction strategies on the host metabolism and product yield. The software enables direct graphical comparison of these two experiments. The identification of potentially interesting gene candidates or functional groups is substantially accelerated and eased. Conclusion It was shown that gcExplorer is a very helpful tool to gain a general overview of microarray experiments. Interesting gene expression patterns can easily be found, compared among different experiments and combined with information about gene function from publicly available databases. Microarray Data (dpeaa)DE-He213 Cluster Solution (dpeaa)DE-He213 Limited Induction (dpeaa)DE-He213 Cluster Centroid (dpeaa)DE-He213 Neighborhood Graph (dpeaa)DE-He213 Striedner, Gerald aut Pötschacher, Florentina aut Leisch, Friedrich aut Bayer, Karl aut Enthalten in Microbial cell factories London : Biomed Central, 2002 8(2009), 1 vom: 15. Juli (DE-627)355987651 (DE-600)2091377-1 1475-2859 nnns volume:8 year:2009 number:1 day:15 month:07 https://dx.doi.org/10.1186/1475-2859-8-37 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2009 1 15 07
allfieldsGer	10.1186/1475-2859-8-37 doi (DE-627)SPR028557174 (SPR)1475-2859-8-37-e DE-627 ger DE-627 rakwb eng Scharl, Theresa verfasserin aut Interactive visualization of clusters in microarray data: an efficient tool for improved metabolic analysis of E. coli 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Scharl et al; licensee BioMed Central Ltd. 2009. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Interpretation of comprehensive DNA microarray data sets is a challenging task for biologists and process engineers where scientific assistance of statistics and bioinformatics is essential. Interdisciplinary cooperation and concerted development of software-tools for simplified and accelerated data analysis and interpretation is the key to overcome the bottleneck in data-analysis workflows. This approach is exemplified by gcExplorer an interactive visualization toolbox based on cluster analysis. Clustering is an important tool in gene expression data analysis to find groups of co-expressed genes which can finally suggest functional pathways and interactions between genes. The visualization of gene clusters gives practitioners an understanding of the cluster structure of their data and makes it easier to interpret the cluster results. Results In this study the interactive visualization toolbox gcExplorer is applied to the interpretation of E. coli microarray data. The data sets derive from two fedbatch experiments conducted in order to investigate the impact of different induction strategies on the host metabolism and product yield. The software enables direct graphical comparison of these two experiments. The identification of potentially interesting gene candidates or functional groups is substantially accelerated and eased. Conclusion It was shown that gcExplorer is a very helpful tool to gain a general overview of microarray experiments. Interesting gene expression patterns can easily be found, compared among different experiments and combined with information about gene function from publicly available databases. Microarray Data (dpeaa)DE-He213 Cluster Solution (dpeaa)DE-He213 Limited Induction (dpeaa)DE-He213 Cluster Centroid (dpeaa)DE-He213 Neighborhood Graph (dpeaa)DE-He213 Striedner, Gerald aut Pötschacher, Florentina aut Leisch, Friedrich aut Bayer, Karl aut Enthalten in Microbial cell factories London : Biomed Central, 2002 8(2009), 1 vom: 15. Juli (DE-627)355987651 (DE-600)2091377-1 1475-2859 nnns volume:8 year:2009 number:1 day:15 month:07 https://dx.doi.org/10.1186/1475-2859-8-37 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2009 1 15 07
allfieldsSound	10.1186/1475-2859-8-37 doi (DE-627)SPR028557174 (SPR)1475-2859-8-37-e DE-627 ger DE-627 rakwb eng Scharl, Theresa verfasserin aut Interactive visualization of clusters in microarray data: an efficient tool for improved metabolic analysis of E. coli 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Scharl et al; licensee BioMed Central Ltd. 2009. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background Interpretation of comprehensive DNA microarray data sets is a challenging task for biologists and process engineers where scientific assistance of statistics and bioinformatics is essential. Interdisciplinary cooperation and concerted development of software-tools for simplified and accelerated data analysis and interpretation is the key to overcome the bottleneck in data-analysis workflows. This approach is exemplified by gcExplorer an interactive visualization toolbox based on cluster analysis. Clustering is an important tool in gene expression data analysis to find groups of co-expressed genes which can finally suggest functional pathways and interactions between genes. The visualization of gene clusters gives practitioners an understanding of the cluster structure of their data and makes it easier to interpret the cluster results. Results In this study the interactive visualization toolbox gcExplorer is applied to the interpretation of E. coli microarray data. The data sets derive from two fedbatch experiments conducted in order to investigate the impact of different induction strategies on the host metabolism and product yield. The software enables direct graphical comparison of these two experiments. The identification of potentially interesting gene candidates or functional groups is substantially accelerated and eased. Conclusion It was shown that gcExplorer is a very helpful tool to gain a general overview of microarray experiments. Interesting gene expression patterns can easily be found, compared among different experiments and combined with information about gene function from publicly available databases. Microarray Data (dpeaa)DE-He213 Cluster Solution (dpeaa)DE-He213 Limited Induction (dpeaa)DE-He213 Cluster Centroid (dpeaa)DE-He213 Neighborhood Graph (dpeaa)DE-He213 Striedner, Gerald aut Pötschacher, Florentina aut Leisch, Friedrich aut Bayer, Karl aut Enthalten in Microbial cell factories London : Biomed Central, 2002 8(2009), 1 vom: 15. Juli (DE-627)355987651 (DE-600)2091377-1 1475-2859 nnns volume:8 year:2009 number:1 day:15 month:07 https://dx.doi.org/10.1186/1475-2859-8-37 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2009 1 15 07
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author	Scharl, Theresa
spellingShingle	Scharl, Theresa misc Microarray Data misc Cluster Solution misc Limited Induction misc Cluster Centroid misc Neighborhood Graph Interactive visualization of clusters in microarray data: an efficient tool for improved metabolic analysis of E. coli
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topic_title	Interactive visualization of clusters in microarray data: an efficient tool for improved metabolic analysis of E. coli Microarray Data (dpeaa)DE-He213 Cluster Solution (dpeaa)DE-He213 Limited Induction (dpeaa)DE-He213 Cluster Centroid (dpeaa)DE-He213 Neighborhood Graph (dpeaa)DE-He213
topic	misc Microarray Data misc Cluster Solution misc Limited Induction misc Cluster Centroid misc Neighborhood Graph
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title	Interactive visualization of clusters in microarray data: an efficient tool for improved metabolic analysis of E. coli
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title_full	Interactive visualization of clusters in microarray data: an efficient tool for improved metabolic analysis of E. coli
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title_sort	interactive visualization of clusters in microarray data: an efficient tool for improved metabolic analysis of e. coli
title_auth	Interactive visualization of clusters in microarray data: an efficient tool for improved metabolic analysis of E. coli
abstract	Background Interpretation of comprehensive DNA microarray data sets is a challenging task for biologists and process engineers where scientific assistance of statistics and bioinformatics is essential. Interdisciplinary cooperation and concerted development of software-tools for simplified and accelerated data analysis and interpretation is the key to overcome the bottleneck in data-analysis workflows. This approach is exemplified by gcExplorer an interactive visualization toolbox based on cluster analysis. Clustering is an important tool in gene expression data analysis to find groups of co-expressed genes which can finally suggest functional pathways and interactions between genes. The visualization of gene clusters gives practitioners an understanding of the cluster structure of their data and makes it easier to interpret the cluster results. Results In this study the interactive visualization toolbox gcExplorer is applied to the interpretation of E. coli microarray data. The data sets derive from two fedbatch experiments conducted in order to investigate the impact of different induction strategies on the host metabolism and product yield. The software enables direct graphical comparison of these two experiments. The identification of potentially interesting gene candidates or functional groups is substantially accelerated and eased. Conclusion It was shown that gcExplorer is a very helpful tool to gain a general overview of microarray experiments. Interesting gene expression patterns can easily be found, compared among different experiments and combined with information about gene function from publicly available databases. © Scharl et al; licensee BioMed Central Ltd. 2009. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
abstractGer	Background Interpretation of comprehensive DNA microarray data sets is a challenging task for biologists and process engineers where scientific assistance of statistics and bioinformatics is essential. Interdisciplinary cooperation and concerted development of software-tools for simplified and accelerated data analysis and interpretation is the key to overcome the bottleneck in data-analysis workflows. This approach is exemplified by gcExplorer an interactive visualization toolbox based on cluster analysis. Clustering is an important tool in gene expression data analysis to find groups of co-expressed genes which can finally suggest functional pathways and interactions between genes. The visualization of gene clusters gives practitioners an understanding of the cluster structure of their data and makes it easier to interpret the cluster results. Results In this study the interactive visualization toolbox gcExplorer is applied to the interpretation of E. coli microarray data. The data sets derive from two fedbatch experiments conducted in order to investigate the impact of different induction strategies on the host metabolism and product yield. The software enables direct graphical comparison of these two experiments. The identification of potentially interesting gene candidates or functional groups is substantially accelerated and eased. Conclusion It was shown that gcExplorer is a very helpful tool to gain a general overview of microarray experiments. Interesting gene expression patterns can easily be found, compared among different experiments and combined with information about gene function from publicly available databases. © Scharl et al; licensee BioMed Central Ltd. 2009. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
abstract_unstemmed	Background Interpretation of comprehensive DNA microarray data sets is a challenging task for biologists and process engineers where scientific assistance of statistics and bioinformatics is essential. Interdisciplinary cooperation and concerted development of software-tools for simplified and accelerated data analysis and interpretation is the key to overcome the bottleneck in data-analysis workflows. This approach is exemplified by gcExplorer an interactive visualization toolbox based on cluster analysis. Clustering is an important tool in gene expression data analysis to find groups of co-expressed genes which can finally suggest functional pathways and interactions between genes. The visualization of gene clusters gives practitioners an understanding of the cluster structure of their data and makes it easier to interpret the cluster results. Results In this study the interactive visualization toolbox gcExplorer is applied to the interpretation of E. coli microarray data. The data sets derive from two fedbatch experiments conducted in order to investigate the impact of different induction strategies on the host metabolism and product yield. The software enables direct graphical comparison of these two experiments. The identification of potentially interesting gene candidates or functional groups is substantially accelerated and eased. Conclusion It was shown that gcExplorer is a very helpful tool to gain a general overview of microarray experiments. Interesting gene expression patterns can easily be found, compared among different experiments and combined with information about gene function from publicly available databases. © Scharl et al; licensee BioMed Central Ltd. 2009. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
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title_short	Interactive visualization of clusters in microarray data: an efficient tool for improved metabolic analysis of E. coli
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author2	Striedner, Gerald Pötschacher, Florentina Leisch, Friedrich Bayer, Karl
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The visualization of gene clusters gives practitioners an understanding of the cluster structure of their data and makes it easier to interpret the cluster results. Results In this study the interactive visualization toolbox gcExplorer is applied to the interpretation of E. coli microarray data. The data sets derive from two fedbatch experiments conducted in order to investigate the impact of different induction strategies on the host metabolism and product yield. The software enables direct graphical comparison of these two experiments. The identification of potentially interesting gene candidates or functional groups is substantially accelerated and eased. Conclusion It was shown that gcExplorer is a very helpful tool to gain a general overview of microarray experiments. 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Interactive visualization of clusters in microarray data: an efficient tool for improved metabolic analysis of E. coli

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