Buck Institute for Research on Aging, Novato, CA, USA.
BMC Bioinformatics. 2013 Feb 14;14:53. doi: 10.1186/1471-2105-14-53.
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.
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 enriched concepts have value when coupled with traditional enrichment analyses using curated terms.
Multiple ontologies have been developed for gene and protein annotation, by using a dataset of both manually curated GO terms and automatically recognized concepts from curated text we can expand the realm of hypotheses that can be discovered. The web application STOP is available at http://mooneygroup.org/stop/.
基因本体论（GO）富集分析仍然是从高通量数据集生成假设的最常用方法之一。然而，我们相信研究人员努力测试其他不属于 GO 的假设。在这里，我们开发并评估了一种使用描述这些基因和蛋白质的人工编辑文本中存在的本体论概念从基因或蛋白质列表生成假设的工具。
因此，我们开发了一种方法，即统计跟踪本体论短语（STOP），该方法通过将基因的自动注释与来自 200 多个生物医学本体的术语集成，扩展了基因集富集分析中可测试假设的范围。虽然不如人工编辑术语精确，但我们发现，当与使用编辑术语的传统富集分析结合使用时，这些额外的富集概念具有价值。
已经为基因和蛋白质注释开发了多个本体，通过使用手动编辑的 GO 术语数据集和从编辑文本中自动识别的概念，我们可以扩展可以发现的假设范围。Web 应用程序 STOP 可在 http://mooneygroup.org/stop/ 上获得。
