Name
Abstract | ||
|---|---|---|
High-throughput technologies like functional screens and gene expression analysis produce extended lists of candidate genes. Gene-Set Enrichment Analysis is a commonly used and well established technique to test for the statistically significant over-representation of particular pathways. A shortcoming of this method is however, that most genes that are investigated in the experiments have very sparse functional or pathway annotation and therefore cannot be the target of such an analysis. The approach presented here aims to assign lists of genes with limited annotation to previously described functional gene collections or pathways. This works by comparing InterPro domain signatures of the candidate gene lists with domain signatures of gene sets derived from known classifications, e.g. KEGG pathways.In order to validate our approach, we designed a simulation study. Based on all pathways available in the KEGG database, we create test gene lists by randomly selecting pathway genes, removing these genes from the known pathways and adding variable amounts of noise in the form of genes not annotated to the pathway. We show that we can recover pathway memberships based on the simulated gene lists with high accuracy. We further demonstrate the applicability of our approach on a biological example.Results based on simulation and data analysis show that domain based pathway enrichment analysis is a very sensitive method to test for enrichment of pathways in sparsely annotated lists of genes. An R based software package domainsignatures, to routinely perform this analysis on the results of high-throughput screening, is available via Bioconductor. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1186/1471-2105-9-3 | BMC Bioinformatics |
Keywords | Field | DocType |
describing function,algorithms,bioinformatics,high throughput screening,candidate gene,gene expression analysis,microarrays,data analysis,statistical significance,high throughput | Annotation,Gene,Candidate gene,Biology,Gene expression,KEGG,Bioinformatics,Genetics,DNA microarray,Gene expression profiling,InterPro | Journal |
Volume | Issue | ISSN |
9 | 1 | 1471-2105 |
Citations | PageRank | References |
34 | 0.60 | 10 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Florian Hahne | 1 | 120 | 8.60 |
| Alexander Mehrle | 2 | 69 | 3.21 |
| Dorit Arlt | 3 | 113 | 5.01 |
| Annemarie Poustka | 4 | 556 | 76.59 |
| Stefan Wiemann | 5 | 204 | 12.49 |
| Tim Beissbarth | 6 | 134 | 7.03 |