Abstract
Genome-wide analyses of chromatin factor-binding sites or histone modification localization generate lists of up to several thousand potential target genes. For many model organisms, large annotation databases are available to help with the characterization and classification of genomic datasets. The term meta-analysis has been coined for this type of multi-database comparison. In this chapter, we describe a workflow to perform a transcriptional and functional analysis of genome-wide target genes. Sources of transcription data and clustering tools to subdivide genes according to their expression pattern are described. For a functional analysis, we focus on the Gene Ontology (GO) vocabulary and methods to uncover over- or underrepresented functions among target genes. Genomic targets of the histone modification H3K27me3 are presented as a case study to demonstrate that meta-analysis can uncover functions that were hidden in genome-wide datasets.
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