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腾讯登录Cancer Res：研究者开发出促进与基因匹配的高效癌症药物的工具
| 导读 | 近日，国外研究人员开发出了一款网络工具，这种工具可以通过比较药物和不同的遗传靶点，帮助研究者轻松识别那些针对不同类型癌症的有效药物。这种刚刚上线的软件名为CellMiner，用于检测并且识别潜在的抗癌药物，可以提供22379个基因编目的快速入口。研究者的相关研究成果刊登在了16日的国际杂志<em>Cancer Research</em>上。
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近日，国外研究人员开发出了一款网络工具，这种工具可以通过比较药物和不同的遗传靶点，帮助研究者轻松识别那些针对不同类型癌症的有效药物。这种刚刚上线的软件名为CellMiner，用于检测并且识别潜在的抗癌药物，可以提供22379个基因编目的快速入口。研究者的相关研究成果刊登在了16日的国际杂志<em>Cancer Research</em>上。
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研究者Yves教授表示，以前我们不得不邀请生物信息学研究小组来对数据进行分类检索，现在开发出的这种新型工具允许研究者分析药物的反应以及方便对比药物与药物、基因与基因之间的差别。
全基因组测序和分析对于生物医药越来越重要，但是与此同时便会产生很多的数据，使得研究者很费力去处理分析这些数据，如今的这款软件CellMiner可以允许大量基因组和药物数据输入，并且计算基因和药物活性之间的关联度，以及在统计学上进行分析比较。研究者表示某种特殊药物可以用这种软件进行数据存取，并且分析这种药物和其它药物、基因之间是否存在某种关系。
最后研究者希望看到更多的人们都会使用这款软件，并且清楚地看到基因之间的共调节作用以及基因的表达作用等等。相关研究由国家癌症中心提供支持。
编译自：<a title="" href="http://www.sciencedaily.com/releases/2012/07/120716090426.htm" target="_blank">New Tools Facilitate Matching Cancer Drugs With Gene Targets</a>
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<a title="" href="http://dx.doi.org/doi:10.1158/0008-5472.CAN-12-1370" target="_blank">doi:10.1158/0008-5472.CAN-12-1370</a>
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<br/><strong>CellMiner: A Web-Based Suite of Genomic and Pharmacologic Tools to Explore Transcript and Drug Patterns in the NCI-60 Cell Line Set</strong><br/>
William C. Reinhold1, Margot Sunshine1,3, Hongfang Liu1,4, Sudhir Varma1,5, Kurt W. Kohn1, Joel Morris2, James Doroshow1,2, and Yves Pommier1
High-throughput and high-content databases are increasingly important resources in molecular medicine, systems biology, and pharmacology. However, the information usually resides in unwieldy databases, limiting ready data analysis and integration. One resource that offers substantial potential for improvement in this regard is the NCI-60 cell line database compiled by the U.S. National Cancer Institute, which has been extensively characterized across numerous genomic and pharmacologic response platforms. In this report, we introduce a CellMiner (http://discover.nci.nih.gov/cellminer/) web application designed to improve the use of this extensive database. CellMiner tools allowed rapid data retrieval of transcripts for 22,379 genes and 360 microRNAs along with activity reports for 20,503 chemical compounds including 102 drugs approved by the U.S. Food and Drug Administration. Converting these differential levels into quantitative patterns across the NCI-60 clarified data organization and cross-comparisons using a novel pattern match tool. Data queries for potential relationships among parameters can be conducted in an iterative manner specific to user interests and expertise. Examples of the in silico discovery process afforded by CellMiner were provided for multidrug resistance analyses and doxorubicin activity; identification of colon-specific genes, microRNAs, and drugs; microRNAs related to the miR-17-92 cluster; and drug identification patterns matched to erlotinib, gefitinib, afatinib, and lapatinib. CellMiner greatly broadens applications of the extensive NCI-60 database for discovery by creating web-based processes that are rapid, flexible, and readily applied by users without bioinformatics expertise. Cancer Res; 72(14); 3499–511. ©2012 AACR.
<br/>来源：生物谷
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研究者Yves教授表示，以前我们不得不邀请生物信息学研究小组来对数据进行分类检索，现在开发出的这种新型工具允许研究者分析药物的反应以及方便对比药物与药物、基因与基因之间的差别。
全基因组测序和分析对于生物医药越来越重要，但是与此同时便会产生很多的数据，使得研究者很费力去处理分析这些数据，如今的这款软件CellMiner可以允许大量基因组和药物数据输入，并且计算基因和药物活性之间的关联度，以及在统计学上进行分析比较。研究者表示某种特殊药物可以用这种软件进行数据存取，并且分析这种药物和其它药物、基因之间是否存在某种关系。
最后研究者希望看到更多的人们都会使用这款软件，并且清楚地看到基因之间的共调节作用以及基因的表达作用等等。相关研究由国家癌症中心提供支持。
编译自：<a title="" href="http://www.sciencedaily.com/releases/2012/07/120716090426.htm" target="_blank">New Tools Facilitate Matching Cancer Drugs With Gene Targets</a>
<div id="ztload">
<div> </div>
<div>
<div>
<img src="http://www.bioon.com/biology/UploadFiles/201207/2012071722564110.jpg" alt="" width="113" height="149" border="0" />
<a title="" href="http://dx.doi.org/doi:10.1158/0008-5472.CAN-12-1370" target="_blank">doi:10.1158/0008-5472.CAN-12-1370</a>
PMC:
PMID:
</div>
<div>
<br/><strong>CellMiner: A Web-Based Suite of Genomic and Pharmacologic Tools to Explore Transcript and Drug Patterns in the NCI-60 Cell Line Set</strong><br/>
William C. Reinhold1, Margot Sunshine1,3, Hongfang Liu1,4, Sudhir Varma1,5, Kurt W. Kohn1, Joel Morris2, James Doroshow1,2, and Yves Pommier1
High-throughput and high-content databases are increasingly important resources in molecular medicine, systems biology, and pharmacology. However, the information usually resides in unwieldy databases, limiting ready data analysis and integration. One resource that offers substantial potential for improvement in this regard is the NCI-60 cell line database compiled by the U.S. National Cancer Institute, which has been extensively characterized across numerous genomic and pharmacologic response platforms. In this report, we introduce a CellMiner (http://discover.nci.nih.gov/cellminer/) web application designed to improve the use of this extensive database. CellMiner tools allowed rapid data retrieval of transcripts for 22,379 genes and 360 microRNAs along with activity reports for 20,503 chemical compounds including 102 drugs approved by the U.S. Food and Drug Administration. Converting these differential levels into quantitative patterns across the NCI-60 clarified data organization and cross-comparisons using a novel pattern match tool. Data queries for potential relationships among parameters can be conducted in an iterative manner specific to user interests and expertise. Examples of the in silico discovery process afforded by CellMiner were provided for multidrug resistance analyses and doxorubicin activity; identification of colon-specific genes, microRNAs, and drugs; microRNAs related to the miR-17-92 cluster; and drug identification patterns matched to erlotinib, gefitinib, afatinib, and lapatinib. CellMiner greatly broadens applications of the extensive NCI-60 database for discovery by creating web-based processes that are rapid, flexible, and readily applied by users without bioinformatics expertise. Cancer Res; 72(14); 3499–511. ©2012 AACR.
<br/>来源：生物谷
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