Wang Yaoyu E, Kutnetsov Lev, Partensky Antony, Farid Jalil, Quackenbush John
Center for Cancer Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts.
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
Cancer Res. 2017 Nov 1;77(21):e11-e14. doi: 10.1158/0008-5472.CAN-17-0802.
Although large, complex genomic datasets are increasingly easy to generate, and the number of publicly available datasets in cancer and other diseases is rapidly growing, the lack of intuitive, easy-to-use analysis tools has remained a barrier to the effective use of such data. WebMeV (http://mev.tm4.org) is an open-source, web-based tool that gives users access to sophisticated tools for analysis of RNA-Seq and other data in an interface designed to democratize data access. WebMeV combines cloud-based technologies with a simple user interface to allow users to access large public datasets, such as that from The Cancer Genome Atlas or to upload their own. The interface allows users to visualize data and to apply advanced data mining analysis methods to explore the data and draw biologically meaningful conclusions. We provide an overview of WebMeV and demonstrate two simple use cases that illustrate the value of putting data analysis in the hands of those looking to explore the underlying biology of the systems being studied. .
尽管大型、复杂的基因组数据集越来越容易生成,并且癌症及其他疾病的公开可用数据集数量也在迅速增长,但缺乏直观、易用的分析工具仍然是有效利用此类数据的障碍。WebMeV(http://mev.tm4.org)是一款基于网络的开源工具,它在一个旨在使数据访问民主化的界面中,为用户提供了用于分析RNA测序及其他数据的复杂工具。WebMeV将基于云的技术与简单的用户界面相结合,允许用户访问大型公共数据集,如来自癌症基因组图谱的数据集,或上传他们自己的数据。该界面允许用户可视化数据,并应用先进的数据挖掘分析方法来探索数据并得出具有生物学意义的结论。我们对WebMeV进行了概述,并展示了两个简单的用例,这些用例说明了将数据分析交给那些希望探索所研究系统潜在生物学特性的人手中所具有的价值。