This application is no longer being maintained. You are advised to use the upgraded version instead, StRAnGER 2
StRAnGER is a web-based application, which performs functional analysis of high-throughput genomic datasets, starting from a list of significant genes derived from statistical and empirical thresholds, by utilizing the GO database and the KEGG pathway database as well as established statistical methods in order to relate the identified significant genes with important nodes in the GO tree structure or map those genes to over-represented metabolic pathways. In this way, cellular actions are seen as conceptual entities that are mapped as nodes to a hierarchical organizational schemas such as as the GO tree structure, where all functional annotations stem from the root nodes of molecular function, cellular component or biological process. Aim of the application is to suggest whole molecular pathways or parts of them, incorporating a number of significantly differentially expressed genes of the list, rather than isolated genes whose measurements are more susceptible to systematic or random errors, as interesting targets for further biological research. Regarding GO analysis, the rationale supporting StRAnGER is the exploitation of an essential property of the GO terms tree structure and subsequently of the population of GO terms (GOTs) derived by each significant gene list; many genes which are hierarchically lower (descendants) in the context of several biological functions are represented as ‘leaves’ in the GO tree structure but are connected to hierarchically higher biological entities through the same tree structure, and as a result inherit these GOTs too. The main goal of StRAnGER is to sort out among all the GOTs associated with the significant gene list, those associated with nodes higher in the GO hierarchy, which consequently encompass a number of genes that act on a specific biochemical pathway, and rank them according to their statistical significance, following their p-value score as derived from a suitable over-representation test. In this sense the result of the inference of noise on high-throughput genomic experiments is significantly mitigated, thus enabling the targeting of specific biological objects for further investigation.
Chatziioannou, A. and Moulos, P.
Exploiting statistical methodologies and controlled vocabularies for prioritized functional analysis of genomic experiments: the StRAnGER web application
Front. Neurosci. 5:8. doi: 10.3389/fnins.2011.00008 (PubMed)