VolcaNoseR is a web app for creating, exploring, labeling and sharing volcano plots.
VolcaNoseR is a web app for creating, exploring, labeling and sharing volcano plots.
复制标题
DOI：
10.1038/s41598-020-76603-3
复制
发表时间：
2020-11-25
影响因子：
4.6
通讯作者：
Luijsterburg MS
中科院分区：
文献类型：
--
作者：
Goedhart J;Luijsterburg MS
比较基因组和蛋白质组的筛选产生了大量的数据。为了有效地呈现这种数据集并简化命中的识别，结果通常以一种称为火山图的散点图的形式呈现，该散点图显示效果大小相对于重要性的度量。影响大小最大且统计显著性超过用户定义的阈值的数据点被视为命中。这类命中作品通常在剧情中用带有他们名字的标签进行注释。火山图可以代表数以万计的数据点，其中通常只有一小部分被注释。未加注释的数据信息很难或无法访问。为了简化对数据的访问并使其能够重复使用，我们开发了一个开源的在线网络工具R/SHINY。该Web应用程序名为VolcaNoseR，可用于创建、探索、标注和共享火山地块(https://huygens.science.uva.nl/VolcaNoseR).当数据存储在在线数据存储库中时，Web应用程序可以检索该数据以及用户定义的设置，以生成定制的交互式火山图表。用户可以与数据交互，调整曲线图，并将修改后的曲线图与底层数据一起共享。因此，VolcaNoseR提高了大型比较基因组和蛋白质组范围的数据集的透明度和重用性。
Comparative genome- and proteome-wide screens yield large amounts of data. To efficiently present such datasets and to simplify the identification of hits, the results are often presented in a type of scatterplot known as a volcano plot, which shows a measure of effect size versus a measure of significance. The data points with the largest effect size and a statistical significance beyond a user-defined threshold are considered as hits. Such hits are usually annotated in the plot by a label with their name. Volcano plots can represent ten thousands of data points, of which typically only a handful is annotated. The information of data that is not annotated is hardly or not accessible. To simplify access to the data and enable its re-use, we have developed an open source and online web tool with R/Shiny. The web app is named VolcaNoseR and it can be used to create, explore, label and share volcano plots (https://huygens.science.uva.nl/VolcaNoseR). When the data is stored in an online data repository, the web app can retrieve that data together with user-defined settings to generate a customized, interactive volcano plot. Users can interact with the data, adjust the plot and share their modified plot together with the underlying data. Therefore, VolcaNoseR increases the transparency and re-use of large comparative genome- and proteome-wide datasets.
登录
查看更多内容
影响因子：
7.7
作者：
Gillingham，Alison K.;Bertram，Jessie;Munro，Sean
通讯作者：
Munro，Sean
影响因子：
8.8
作者：
Becares, Natalia;Gage, Matthew C.;Pineda-Torra, Ines
通讯作者：
Pineda-Torra, Ines
影响因子：
11.8
作者：
Pusapati GV;Kong JH;Patel BB;Krishnan A;Sagner A;Kinnebrew M;Briscoe J;Aravind L;Rohatgi R
通讯作者：
Rohatgi R
影响因子：
12.3
作者：
Cui X;Churchill GA
通讯作者：
Churchill GA
影响因子：
9.8
作者：
Goedhart, Joachim
通讯作者：
Goedhart, Joachim