New Trends in the Analysis of Functional Genomic Data
Most analyses carried out using high throughput data consist of the repetition of the same statistical test for all genes in the dataset. As a result of such replicated analysis we get, for each gene, several estimates of statistical parameters: statistics, p-values or confidence intervals. Being aware that most statistical methods were developed to test for a single hypothesis, researchers will usually correct p-values for multiple testing before choosing a cut-off that will indicate the rejection of the null hypotheses, whichever it is. Once chosen the genes with alternative pattern (meaning different form the one stated in the null hypothesis) the next step is to biologically interpret such departure from hypothesis. Different repositories of functionally relevant biological information such as Gene Ontology , KEGG  or Interpro  are available and can be used for the functional annotation of genome-scale experiments. Thus the functional properties of the selected genes can be analysed.
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- Mootha, V.K., Lindgren, C.M., Eriksson, K.F., Subramanian, A., Sihag, S., Lehar, J., Puigserver, P., Carlsson, E., Ridderstrale, M., Laurila, E., et al; PGC- 1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nature Genet., 34, 267-273 2003CrossRefGoogle Scholar
- Subramanian, A., Tamayo, P., Mootha, V.K., Mukherjee, S., Ebert, B.L., Gillette, M.A., Paulovich, A., Pomeroy, S.L., Golub, T.R., Lander, E.S., et al; Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA, 102, 15545-15550 2005CrossRefGoogle Scholar
- Al-Shahrour, F., Diaz-Uriarte, R., Dopazo, J.; Discovering molecular functions significantly related to phenotypes by combining gene expression data and bi- ological information Bioinformatics, 21, 2988-2993 2005Google Scholar