STOP using just GO: a multi-ontology hypothesis generation tool for high throughput experimentation.

ABSTRACT:

Background

Gene Ontology (GO) enrichment analysis remains one of the most common methods for hypothesis generation from high throughput datasets. However, we believe that researchers strive to test other hypotheses that fall outside of GO. Here, we developed and evaluated a tool for hypothesis generation from gene or protein lists using ontological concepts present in manually curated text that describes those genes and proteins.

Results

As a consequence we have developed the method Statistical Tracking of Ontological Phrases (STOP) that expands the realm of testable hypotheses in gene set enrichment analyses by integrating automated annotations of genes to terms from over 200 biomedical ontologies. While not as precise as manually curated terms, we find that the additional

SUBMITTER: Wittkop T 

PROVIDER: S-EPMC3635999 | biostudies-literature | 2013 Feb

REPOSITORIES: biostudies-literature