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Meta analysis of gene expression data within and across species

Ana Carolina Fierro, Filip Vandenbussche UGent, Kristof Engelen, Yves Van de Peer UGent and Kathleen Marchal UGent (2008) CURRENT GENOMICS. 9(8). p.525-534
abstract
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.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
EST ANALYSIS, METAANALYSIS, HUMAN GENOME, PROSTATE-CANCER, CROSS-PLATFORM, SCALE STATISTICAL-ANALYSES, OMICS DATA, SEQUENCE TAGS, MICROARRAY DATA, PROFILES
journal title
CURRENT GENOMICS
Curr. Genomics
volume
9
issue
8
pages
525 - 534
Web of Science type
Article
Web of Science id
000261501000003
JCR category
GENETICS & HEREDITY
JCR impact factor
0.613 (2008)
JCR rank
125/138 (2008)
JCR quartile
4 (2008)
ISSN
1389-2029
DOI
10.2174/138920208786847935
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
593198
handle
http://hdl.handle.net/1854/LU-593198
date created
2009-04-10 11:56:21
date last changed
2013-09-16 15:39:08
@article{593198,
  abstract     = {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.},
  author       = {Fierro, Ana Carolina and Vandenbussche, Filip and Engelen, Kristof and Van de Peer, Yves and Marchal, Kathleen},
  issn         = {1389-2029},
  journal      = {CURRENT GENOMICS},
  keyword      = {EST ANALYSIS,METAANALYSIS,HUMAN GENOME,PROSTATE-CANCER,CROSS-PLATFORM,SCALE STATISTICAL-ANALYSES,OMICS DATA,SEQUENCE TAGS,MICROARRAY DATA,PROFILES},
  language     = {eng},
  number       = {8},
  pages        = {525--534},
  title        = {Meta analysis of gene expression data within and across species},
  url          = {http://dx.doi.org/10.2174/138920208786847935},
  volume       = {9},
  year         = {2008},
}

Chicago
Fierro, Ana Carolina, Filip Vandenbussche, Kristof Engelen, Yves Van de Peer, and Kathleen Marchal. 2008. “Meta Analysis of Gene Expression Data Within and Across Species.” Current Genomics 9 (8): 525–534.
APA
Fierro, A. C., Vandenbussche, F., Engelen, K., Van de Peer, Y., & Marchal, K. (2008). Meta analysis of gene expression data within and across species. CURRENT GENOMICS, 9(8), 525–534.
Vancouver
1.
Fierro AC, Vandenbussche F, Engelen K, Van de Peer Y, Marchal K. Meta analysis of gene expression data within and across species. CURRENT GENOMICS. 2008;9(8):525–34.
MLA
Fierro, Ana Carolina, Filip Vandenbussche, Kristof Engelen, et al. “Meta Analysis of Gene Expression Data Within and Across Species.” CURRENT GENOMICS 9.8 (2008): 525–534. Print.