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 studygene expression at the transcript level. To this end, two major strategies have been adopted, a first one relying on hybridizationtechniques such as microarrays, and a second one based on sequencing techniques such as serial analysis of geneexpression (SAGE), cDNA-AFLP, and analysis based on expressed sequence tags (ESTs). Despite both types of profilingexperiments becoming routine techniques in many research groups, their application remains costly and laborious. As aresult, the number of conditions profiled in individual studies is still relatively small and usually varies from only two tofew hundreds of samples for the largest experiments. More and more, scientific journals require the deposit of these highthroughput experiments in public databases upon publication. Mining the information present in these databases offersmolecular 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 associationbetween ESTs and microarray probes with the corresponding gene transcript, the incompleteness and inconsistencyin the annotation of experimental conditions, and the lack of standardized experimental protocols to generate geneexpression data, all impede the successful mining of these data. Here, we review the potential and difficulties of combiningpublicly available expression data from respectively EST analyses and microarray experiments. With examples fromliterature, we show how meta-analysis of expression profiling experiments can be used to study expression behavior in asingle organism or between organisms, across a wide range of experimental conditions. We also provide an overview ofthe methods and tools that can aid molecular biologists in exploiting these public data.

Fierro, A.C., Vandenbussche, M., Engelen, K., Van de Peer, Y., Marchal, K. (2008) Meta Analysis of Gene Expression Data within and Across Species. Curr. Genomics 9(8):525-34.

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