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生物测定本体注释有助于对各种高通量筛选数据集进行交叉分析。

BioAssay ontology annotations facilitate cross-analysis of diverse high-throughput screening data sets.

作者信息

Schürer Stephan C, Vempati Uma, Smith Robin, Southern Mark, Lemmon Vance

机构信息

Center for Computational Science, University of Miami, Miami, Florida 33136, USA.

出版信息

J Biomol Screen. 2011 Apr;16(4):415-26. doi: 10.1177/1087057111400191.

Abstract

High-throughput screening data repositories, such as PubChem, represent valuable resources for the development of small-molecule chemical probes and can serve as entry points for drug discovery programs. Although the loose data format offered by PubChem allows for great flexibility, important annotations, such as the assay format and technologies employed, are not explicitly indexed. The authors have previously developed a BioAssay Ontology (BAO) and curated more than 350 assays with standardized BAO terms. Here they describe the use of BAO annotations to analyze a large set of assays that employ luciferase- and β-lactamase-based technologies. They identified promiscuous chemotypes pertaining to different subcategories of assays and specific mechanisms by which these chemotypes interfere in reporter gene assays. Results show that the data in PubChem can be used to identify promiscuous compounds that interfere nonspecifically with particular technologies. Furthermore, they show that BAO is a valuable toolset for the identification of related assays and for the systematic generation of insights that are beyond the scope of individual assays or screening campaigns.

摘要

高通量筛选数据存储库,如PubChem,是开发小分子化学探针的宝贵资源,可作为药物发现计划的切入点。尽管PubChem提供的松散数据格式具有很大的灵活性,但重要的注释,如所采用的测定形式和技术,并未明确索引。作者此前开发了生物测定本体(BAO),并用标准化的BAO术语策划了350多种测定。在此,他们描述了使用BAO注释来分析大量采用基于荧光素酶和β-内酰胺酶技术的测定。他们确定了与不同测定子类别相关的混杂化学型,以及这些化学型干扰报告基因测定的具体机制。结果表明,PubChem中的数据可用于识别非特异性干扰特定技术的混杂化合物。此外,他们还表明,BAO是识别相关测定以及系统生成超出单个测定或筛选活动范围的见解的宝贵工具集。

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本文引用的文献

1
A Java API for working with PubChem datasets.
Bioinformatics. 2011 Mar 1;27(5):741-2. doi: 10.1093/bioinformatics/btq715. Epub 2011 Jan 6.
2
Extended-connectivity fingerprints.
J Chem Inf Model. 2010 May 24;50(5):742-54. doi: 10.1021/ci100050t.
3
An overview of the PubChem BioAssay resource.
Nucleic Acids Res. 2010 Jan;38(Database issue):D255-66. doi: 10.1093/nar/gkp965. Epub 2009 Nov 19.
4
Novel trends in high-throughput screening.
Curr Opin Pharmacol. 2009 Oct;9(5):580-8. doi: 10.1016/j.coph.2009.08.004. Epub 2009 Sep 21.
5
Novel web-based tools combining chemistry informatics, biology and social networks for drug discovery.
Drug Discov Today. 2009 Mar;14(5-6):261-70. doi: 10.1016/j.drudis.2008.11.015. Epub 2009 Feb 11.
6
Chemical biology of natural indolocarbazole products: 30 years since the discovery of staurosporine.
J Antibiot (Tokyo). 2009 Jan;62(1):17-26. doi: 10.1038/ja.2008.4. Epub 2009 Jan 9.
7
Massively parallel screening of the receptorome.
Comb Chem High Throughput Screen. 2008 Jul;11(6):420-6. doi: 10.2174/138620708784911483.
8
Characterization of chemical libraries for luciferase inhibitory activity.
J Med Chem. 2008 Apr 24;51(8):2372-86. doi: 10.1021/jm701302v. Epub 2008 Mar 26.
9
High throughput screening informatics.
Comb Chem High Throughput Screen. 2008 Mar;11(3):249-57. doi: 10.2174/138620708783877726.
10
Compound cytotoxicity profiling using quantitative high-throughput screening.
Environ Health Perspect. 2008 Mar;116(3):284-91. doi: 10.1289/ehp.10727.

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