Cargando…

Meta Analysis of Gene Expression Data within and Across Species

Since the second half of the 1990s, a large number of genome-wide analyses have been described that study gene expression at the transcript level. To this end, two major strategies have been adopted, a first one relying on hybridization techniques such as microarrays, and a second one based on seque...

Descripción completa

Detalles Bibliográficos
Autores principales: Fierro, Ana C, Vandenbussche, Filip, Engelen, Kristof, Van de Peer, Yves, Marchal, Kathleen
Formato: Texto
Lenguaje:English
Publicado: Bentham Science Publishers Ltd. 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2694560/
https://www.ncbi.nlm.nih.gov/pubmed/19516959
http://dx.doi.org/10.2174/138920208786847935
_version_ 1782168100029857792
author Fierro, Ana C
Vandenbussche, Filip
Engelen, Kristof
Van de Peer, Yves
Marchal, Kathleen
author_facet Fierro, Ana C
Vandenbussche, Filip
Engelen, Kristof
Van de Peer, Yves
Marchal, Kathleen
author_sort Fierro, Ana C
collection PubMed
description Since the second half of the 1990s, a large number of genome-wide analyses have been described that study gene expression at the transcript level. To this end, two major strategies have been adopted, a first one relying on hybridization techniques such as microarrays, and a second one based on sequencing techniques such as serial analysis of gene expression (SAGE), cDNA-AFLP, and analysis based on expressed sequence tags (ESTs). Despite both types of profiling experiments becoming routine techniques in many research groups, their application remains costly and laborious. As a result, the number of conditions profiled in individual studies is still relatively small and usually varies from only two to few hundreds of samples for the largest experiments. More and more, scientific journals require the deposit of these high throughput experiments in public databases upon publication. Mining the information present in these databases offers molecular biologists the possibility to view their own small-scale analysis in the light of what is already available. However, so far, the richness of the public information remains largely unexploited. Several obstacles such as the correct association between ESTs and microarray probes with the corresponding gene transcript, the incompleteness and inconsistency in the annotation of experimental conditions, and the lack of standardized experimental protocols to generate gene expression data, all impede the successful mining of these data. Here, we review the potential and difficulties of combining publicly available expression data from respectively EST analyses and microarray experiments. With examples from literature, we show how meta-analysis of expression profiling experiments can be used to study expression behavior in a single organism or between organisms, across a wide range of experimental conditions. We also provide an overview of the methods and tools that can aid molecular biologists in exploiting these public data.
format Text
id pubmed-2694560
institution National Center for Biotechnology Information
language English
publishDate 2008
publisher Bentham Science Publishers Ltd.
record_format MEDLINE/PubMed
spelling pubmed-26945602009-06-09 Meta Analysis of Gene Expression Data within and Across Species Fierro, Ana C Vandenbussche, Filip Engelen, Kristof Van de Peer, Yves Marchal, Kathleen Curr Genomics Article Since the second half of the 1990s, a large number of genome-wide analyses have been described that study gene expression at the transcript level. To this end, two major strategies have been adopted, a first one relying on hybridization techniques such as microarrays, and a second one based on sequencing techniques such as serial analysis of gene expression (SAGE), cDNA-AFLP, and analysis based on expressed sequence tags (ESTs). Despite both types of profiling experiments becoming routine techniques in many research groups, their application remains costly and laborious. As a result, the number of conditions profiled in individual studies is still relatively small and usually varies from only two to few hundreds of samples for the largest experiments. More and more, scientific journals require the deposit of these high throughput experiments in public databases upon publication. Mining the information present in these databases offers molecular biologists the possibility to view their own small-scale analysis in the light of what is already available. However, so far, the richness of the public information remains largely unexploited. Several obstacles such as the correct association between ESTs and microarray probes with the corresponding gene transcript, the incompleteness and inconsistency in the annotation of experimental conditions, and the lack of standardized experimental protocols to generate gene expression data, all impede the successful mining of these data. Here, we review the potential and difficulties of combining publicly available expression data from respectively EST analyses and microarray experiments. With examples from literature, we show how meta-analysis of expression profiling experiments can be used to study expression behavior in a single organism or between organisms, across a wide range of experimental conditions. We also provide an overview of the methods and tools that can aid molecular biologists in exploiting these public data. Bentham Science Publishers Ltd. 2008-12 /pmc/articles/PMC2694560/ /pubmed/19516959 http://dx.doi.org/10.2174/138920208786847935 Text en ©2008 Bentham Science Publishers Ltd. http://creativecommons.org/licenses/by/2.5/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.5/) which permits unrestrictive use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Article
Fierro, Ana C
Vandenbussche, Filip
Engelen, Kristof
Van de Peer, Yves
Marchal, Kathleen
Meta Analysis of Gene Expression Data within and Across Species
title Meta Analysis of Gene Expression Data within and Across Species
title_full Meta Analysis of Gene Expression Data within and Across Species
title_fullStr Meta Analysis of Gene Expression Data within and Across Species
title_full_unstemmed Meta Analysis of Gene Expression Data within and Across Species
title_short Meta Analysis of Gene Expression Data within and Across Species
title_sort meta analysis of gene expression data within and across species
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2694560/
https://www.ncbi.nlm.nih.gov/pubmed/19516959
http://dx.doi.org/10.2174/138920208786847935
work_keys_str_mv AT fierroanac metaanalysisofgeneexpressiondatawithinandacrossspecies
AT vandenbusschefilip metaanalysisofgeneexpressiondatawithinandacrossspecies
AT engelenkristof metaanalysisofgeneexpressiondatawithinandacrossspecies
AT vandepeeryves metaanalysisofgeneexpressiondatawithinandacrossspecies
AT marchalkathleen metaanalysisofgeneexpressiondatawithinandacrossspecies