ErmineJ: Tool for functional analysis of gene expression data sets

Persistent Link:
http://hdl.handle.net/10150/610121
Title:
ErmineJ: Tool for functional analysis of gene expression data sets
Author:
Lee, Homin; Braynen, William; Keshav, Kiran; Pavlidis, Paul
Affiliation:
Columbia Genome Center, Columbia University, New York NY 10032, USA; Department of Computer Science, Columbia University, New York NY 10025, USA; Department of Philosophy, University of Arizona, Tucson, AZ 85721, USA
Issue Date:
2005
Publisher:
BioMed Central
Citation:
BMC Bioinformatics 2005, 6:269 doi:10.1186/1471-2105-6-269
Journal:
BMC Bioinformatics
Rights:
© 2005 Lee et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0)
Collection Information:
This item is part of the UA Faculty Publications collection. For more information this item or other items in the UA Campus Repository, contact the University of Arizona Libraries at repository@u.library.arizona.edu.
Abstract:
BACKGROUND:It is common for the results of a microarray study to be analyzed in the context of biologically-motivated groups of genes such as pathways or Gene Ontology categories. The most common method for such analysis uses the hypergeometric distribution (or a related technique) to look for "over-representation" of groups among genes selected as being differentially expressed or otherwise of interest based on a gene-by-gene analysis. However, this method suffers from some limitations, and biologist-friendly tools that implement alternatives have not been reported.RESULTS:We introduce ErmineJ, a multiplatform user-friendly stand-alone software tool for the analysis of functionally-relevant sets of genes in the context of microarray gene expression data. ErmineJ implements multiple algorithms for gene set analysis, including over-representation and resampling-based methods that focus on gene scores or correlation of gene expression profiles. In addition to a graphical user interface, ErmineJ has a command line interface and an application programming interface that can be used to automate analyses. The graphical user interface includes tools for creating and modifying gene sets, visualizing the Gene Ontology as a table or tree, and visualizing gene expression data. ErmineJ comes with a complete user manual, and is open-source software licensed under the Gnu Public License.CONCLUSION:The availability of multiple analysis algorithms, together with a rich feature set and simple graphical interface, should make ErmineJ a useful addition to the biologist's informatics toolbox. ErmineJ is available from http://microarray.cu.genome.org webcite.
EISSN:
1471-2105
DOI:
10.1186/1471-2105-6-269
Version:
Final published version
Additional Links:
http://www.biomedcentral.com/1471-2105/6/269

Full metadata record

DC FieldValue Language
dc.contributor.authorLee, Hominen
dc.contributor.authorBraynen, Williamen
dc.contributor.authorKeshav, Kiranen
dc.contributor.authorPavlidis, Paulen
dc.date.accessioned2016-05-20T08:59:04Z-
dc.date.available2016-05-20T08:59:04Z-
dc.date.issued2005en
dc.identifier.citationBMC Bioinformatics 2005, 6:269 doi:10.1186/1471-2105-6-269en
dc.identifier.doi10.1186/1471-2105-6-269en
dc.identifier.urihttp://hdl.handle.net/10150/610121-
dc.description.abstractBACKGROUND:It is common for the results of a microarray study to be analyzed in the context of biologically-motivated groups of genes such as pathways or Gene Ontology categories. The most common method for such analysis uses the hypergeometric distribution (or a related technique) to look for "over-representation" of groups among genes selected as being differentially expressed or otherwise of interest based on a gene-by-gene analysis. However, this method suffers from some limitations, and biologist-friendly tools that implement alternatives have not been reported.RESULTS:We introduce ErmineJ, a multiplatform user-friendly stand-alone software tool for the analysis of functionally-relevant sets of genes in the context of microarray gene expression data. ErmineJ implements multiple algorithms for gene set analysis, including over-representation and resampling-based methods that focus on gene scores or correlation of gene expression profiles. In addition to a graphical user interface, ErmineJ has a command line interface and an application programming interface that can be used to automate analyses. The graphical user interface includes tools for creating and modifying gene sets, visualizing the Gene Ontology as a table or tree, and visualizing gene expression data. ErmineJ comes with a complete user manual, and is open-source software licensed under the Gnu Public License.CONCLUSION:The availability of multiple analysis algorithms, together with a rich feature set and simple graphical interface, should make ErmineJ a useful addition to the biologist's informatics toolbox. ErmineJ is available from http://microarray.cu.genome.org webcite.en
dc.language.isoenen
dc.publisherBioMed Centralen
dc.relation.urlhttp://www.biomedcentral.com/1471-2105/6/269en
dc.rights© 2005 Lee et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0)en
dc.titleErmineJ: Tool for functional analysis of gene expression data setsen
dc.typeArticleen
dc.identifier.eissn1471-2105en
dc.contributor.departmentColumbia Genome Center, Columbia University, New York NY 10032, USAen
dc.contributor.departmentDepartment of Computer Science, Columbia University, New York NY 10025, USAen
dc.contributor.departmentDepartment of Philosophy, University of Arizona, Tucson, AZ 85721, USAen
dc.identifier.journalBMC Bioinformaticsen
dc.description.collectioninformationThis item is part of the UA Faculty Publications collection. For more information this item or other items in the UA Campus Repository, contact the University of Arizona Libraries at repository@u.library.arizona.edu.en
dc.eprint.versionFinal published versionen
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