Pathway and ontology analysis: emerging approaches connecting transcriptome data and clinical endpoints.
The increasing use of gene expression profiling offers great promise in clinical research into disease biology and its treatment. Along with the ability to measure changing expression levels in thousands of genes at once, comes the challenge of analyzing and interpreting the vast sets of data generated. Analysis tools are evolving rapidly to meet such challenges. The next step is to interpret observed changes in terms of the biological properties or relationships underlying them. One powerful approach is to make associations between the genes that are under investigation and well-known biochemical or signaling pathways, and further to assess the significance of such associations. Similarly, genes can be mapped to standardized biological categories via an ontology resource. We discuss these approaches and several web-based resources and tools designed to facilitate such analyses. This information can be used to facilitate understanding and to help design more focused experiments for validating the relevance and importance of these biological pathways and processes in human disease and therapeutics.
Yue
L
L
Bioinformatics, GlaxoSmithKline, Collegeville, PA 19426, USA. Lin.Yue-1@gsk.com
Reisdorf
W C
WC
D000818:Animals; D003628:Database Management Systems; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D006801:Humans; D016247:Information Storage and Retrieval; D020411:Oligonucleotide Array Sequence Analysis; D015203:Reproducibility of Results; D015398:Signal Transduction / Q000235:genetics