Yu Guangchuang, Wang Li-Gen, Yan Guang-Rong, He Qing-Yu
State Key Laboratory of Emerging Infectious Diseases, School of Public Health, The University of Hong Kong, Hong Kong SAR, Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, College of Life Science and Technology, Jinan University, Guangzhou 510632 and Guangdong Information Center, Guangzhou 510031, China State Key Laboratory of Emerging Infectious Diseases, School of Public Health, The University of Hong Kong, Hong Kong SAR, Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, College of Life Science and Technology, Jinan University, Guangzhou 510632 and Guangdong Information Center, Guangzhou 510031, China.
State Key Laboratory of Emerging Infectious Diseases, School of Public Health, The University of Hong Kong, Hong Kong SAR, Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, College of Life Science and Technology, Jinan University, Guangzhou 510632 and Guangdong Information Center, Guangzhou 510031, China.
Bioinformatics. 2015 Feb 15;31(4):608-9. doi: 10.1093/bioinformatics/btu684. Epub 2014 Oct 17.
SUMMARY: Disease ontology (DO) annotates human genes in the context of disease. DO is important annotation in translating molecular findings from high-throughput data to clinical relevance. DOSE is an R package providing semantic similarity computations among DO terms and genes which allows biologists to explore the similarities of diseases and of gene functions in disease perspective. Enrichment analyses including hypergeometric model and gene set enrichment analysis are also implemented to support discovering disease associations of high-throughput biological data. This allows biologists to verify disease relevance in a biological experiment and identify unexpected disease associations. Comparison among gene clusters is also supported. AVAILABILITY AND IMPLEMENTATION: DOSE is released under Artistic-2.0 License. The source code and documents are freely available through Bioconductor (http://www.bioconductor.org/packages/release/bioc/html/DOSE.html). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. CONTACT: gcyu@connect.hku.hk or tqyhe@jnu.edu.cn.
摘要：疾病本体论（DO）在疾病背景下注释人类基因。DO是将高通量数据中的分子发现转化为临床相关性的重要注释。DOSE是一个R包，可提供DO术语和基因之间的语义相似性计算，使生物学家能够从疾病角度探索疾病和基因功能的相似性。还实施了包括超几何模型和基因集富集分析在内的富集分析，以支持发现高通量生物学数据的疾病关联。这使生物学家能够在生物学实验中验证疾病相关性，并识别意外的疾病关联。还支持基因簇之间的比较。 可用性和实现方式：DOSE根据Artistic-2.0许可发布。源代码和文档可通过Bioconductor（http://www.bioconductor.org/packages/release/bioc/html/DOSE.html）免费获取。 补充信息：补充数据可在《生物信息学》在线获取。 联系方式：gcyu@connect.hku.hk或tqyhe@jnu.edu.cn。
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