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Gene set analysis methods applied to chicken microarray expression data

BACKGROUND: Gene set analysis is considered to be a way of improving our biological interpretation of the observed expression patterns. This paper describes different methods applied to analyse expression data from a chicken DNA microarray dataset. RESULTS: Applying different gene set analyses to th...

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Detalles Bibliográficos
Autores principales: Skarman, Axel, Jiang, Li, Henrik, Hornshøj, Buitenhuis, Bart, Hedegaard, Jakob, Conley, Lene N, Sørensen, Peter
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2712751/
https://www.ncbi.nlm.nih.gov/pubmed/19615121
http://dx.doi.org/10.1186/1753-6561-3-S4-S8
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author Skarman, Axel
Jiang, Li
Henrik, Hornshøj
Buitenhuis, Bart
Hedegaard, Jakob
Conley, Lene N
Sørensen, Peter
author_facet Skarman, Axel
Jiang, Li
Henrik, Hornshøj
Buitenhuis, Bart
Hedegaard, Jakob
Conley, Lene N
Sørensen, Peter
author_sort Skarman, Axel
collection PubMed
description BACKGROUND: Gene set analysis is considered to be a way of improving our biological interpretation of the observed expression patterns. This paper describes different methods applied to analyse expression data from a chicken DNA microarray dataset. RESULTS: Applying different gene set analyses to the chicken expression data led to different ranking of the Gene Ontology terms tested. A method for prediction of possible annotations was applied. CONCLUSION: Biological interpretation based on gene set analyses dependent on the statistical method used. Methods for predicting the possible annotations for genes with unknown function from the expression data at hand could be useful, but our results indicate that careful validation of the predictions is needed.
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spelling pubmed-27127512009-07-20 Gene set analysis methods applied to chicken microarray expression data Skarman, Axel Jiang, Li Henrik, Hornshøj Buitenhuis, Bart Hedegaard, Jakob Conley, Lene N Sørensen, Peter BMC Proc Research BACKGROUND: Gene set analysis is considered to be a way of improving our biological interpretation of the observed expression patterns. This paper describes different methods applied to analyse expression data from a chicken DNA microarray dataset. RESULTS: Applying different gene set analyses to the chicken expression data led to different ranking of the Gene Ontology terms tested. A method for prediction of possible annotations was applied. CONCLUSION: Biological interpretation based on gene set analyses dependent on the statistical method used. Methods for predicting the possible annotations for genes with unknown function from the expression data at hand could be useful, but our results indicate that careful validation of the predictions is needed. BioMed Central 2009-07-16 /pmc/articles/PMC2712751/ /pubmed/19615121 http://dx.doi.org/10.1186/1753-6561-3-S4-S8 Text en Copyright © 2009 Skarman et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Skarman, Axel
Jiang, Li
Henrik, Hornshøj
Buitenhuis, Bart
Hedegaard, Jakob
Conley, Lene N
Sørensen, Peter
Gene set analysis methods applied to chicken microarray expression data
title Gene set analysis methods applied to chicken microarray expression data
title_full Gene set analysis methods applied to chicken microarray expression data
title_fullStr Gene set analysis methods applied to chicken microarray expression data
title_full_unstemmed Gene set analysis methods applied to chicken microarray expression data
title_short Gene set analysis methods applied to chicken microarray expression data
title_sort gene set analysis methods applied to chicken microarray expression data
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2712751/
https://www.ncbi.nlm.nih.gov/pubmed/19615121
http://dx.doi.org/10.1186/1753-6561-3-S4-S8
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