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sscMap:一个用于利用基因表达特征连接小分子药物的可扩展Java应用程序。

sscMap: an extensible Java application for connecting small-molecule drugs using gene-expression signatures.

作者信息

Zhang Shu-Dong, Gant Timothy W

机构信息

MRC Toxicology Unit, Hodgkin Building, Lancaster Road, University of Leicester, Leicester, UK.

出版信息

BMC Bioinformatics. 2009 Jul 31;10:236. doi: 10.1186/1471-2105-10-236.

Abstract

BACKGROUND

Connectivity mapping is a process to recognize novel pharmacological and toxicological properties in small molecules by comparing their gene expression signatures with others in a database. A simple and robust method for connectivity mapping with increased specificity and sensitivity was recently developed, and its utility demonstrated using experimentally derived gene signatures.

RESULTS

This paper introduces sscMap (statistically significant connections' map), a Java application designed to undertake connectivity mapping tasks using the recently published method. The software is bundled with a default collection of reference gene-expression profiles based on the publicly available dataset from the Broad Institute Connectivity Map 02, which includes data from over 7000 Affymetrix microarrays, for over 1000 small-molecule compounds, and 6100 treatment instances in 5 human cell lines. In addition, the application allows users to add their custom collections of reference profiles and is applicable to a wide range of other 'omics technologies.

CONCLUSION

The utility of sscMap is two fold. First, it serves to make statistically significant connections between a user-supplied gene signature and the 6100 core reference profiles based on the Broad Institute expanded dataset. Second, it allows users to apply the same improved method to custom-built reference profiles which can be added to the database for future referencing. The software can be freely downloaded from http://purl.oclc.org/NET/sscMap.

摘要

背景

连接性图谱绘制是一种通过将小分子的基因表达特征与数据库中的其他特征进行比较,来识别小分子中新的药理学和毒理学特性的过程。最近开发了一种简单且稳健的连接性图谱绘制方法,其特异性和灵敏度更高,并且利用实验得出的基因特征证明了其效用。

结果

本文介绍了sscMap(统计显著连接图谱),这是一个用Java编写的应用程序,旨在使用最近发表的方法执行连接性图谱绘制任务。该软件捆绑了基于布罗德研究所连接性图谱02公开数据集的默认参考基因表达谱集合,其中包括来自7000多个Affymetrix微阵列的数据,涉及1000多种小分子化合物以及5种人类细胞系中的6100个处理实例。此外,该应用程序允许用户添加自己的自定义参考谱集合,并且适用于广泛的其他“组学”技术。

结论

sscMap的效用体现在两个方面。首先,它用于在用户提供的基因特征与基于布罗德研究所扩展数据集的6100个核心参考谱之间建立统计显著的联系。其次,它允许用户将相同的改进方法应用于自定义参考谱,这些参考谱可以添加到数据库中以供将来参考。该软件可从http://purl.oclc.org/NET/sscMap免费下载。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9bf5/2732627/700dcb5a2ad9/1471-2105-10-236-1.jpg

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