近日,，國外研究人員開發(fā)出了一款網(wǎng)絡(luò)工具,，這種工具可以通過比較藥物和不同的遺傳靶點，幫助研究者輕松識別那些針對不同類型癌癥的有效藥物,。這種剛剛上線的軟件名為CellMiner,，用于檢測并且識別潛在的抗癌藥物，可以提供22379個基因編目的快速入口,。研究者的相關(guān)研究成果刊登在了16日的國際雜志Cancer Research上,。
研究者Yves教授表示，以前我們不得不邀請生物信息學(xué)研究小組來對數(shù)據(jù)進行分類檢索,，現(xiàn)在開發(fā)出的這種新型工具允許研究者分析藥物的反應(yīng)以及方便對比藥物與藥物,、基因與基因之間的差別。
全基因組測序和分析對于生物醫(yī)藥越來越重要,，但是與此同時便會產(chǎn)生很多的數(shù)據(jù),，使得研究者很費力去處理分析這些數(shù)據(jù)，如今的這款軟件CellMiner可以允許大量基因組和藥物數(shù)據(jù)輸入,，并且計算基因和藥物活性之間的關(guān)聯(lián)度,，以及在統(tǒng)計學(xué)上進行分析比較。研究者表示某種特殊藥物可以用這種軟件進行數(shù)據(jù)存取,，并且分析這種藥物和其它藥物,、基因之間是否存在某種關(guān)系。
最后研究者希望看到更多的人們都會使用這款軟件,，并且清楚地看到基因之間的共調(diào)節(jié)作用以及基因的表達作用等等,。相關(guān)研究由國家癌癥中心提供支持。（生物谷Bioon.com）
編譯自：New Tools Facilitate Matching Cancer Drugs With Gene Targets
doi:10.1158/0008-5472.CAN-12-1370
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CellMiner: A Web-Based Suite of Genomic and Pharmacologic Tools to Explore Transcript and Drug Patterns in the NCI-60 Cell Line Set
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.
