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New Trends in the Analysis of Functional Genomic Data

  • David Montaner
  • Fatima Al-Shahrour
  • Joaquin Dopazo
Part of the Mathematics in Industry book series (MATHINDUSTRY, volume 12)

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 [1], KEGG [2] or Interpro [3] 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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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • David Montaner
    • 1
    • 2
  • Fatima Al-Shahrour
    • 1
  • Joaquin Dopazo
    • 1
    • 2
  1. 1.Bioinformatics DepartmentCentro de Investigacin Prncipe Felipe, CIPFValenciaSpain
  2. 2.Integromics S.L.GranadaSpain

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