ePlant:一款可视化的分析工具

作者:谷丰光电 来源: 时间:2017-08-16 11:41:17 次数:

目前系统生物学研究面临一个重大挑战:不同类型的数据必须从不同来源获取,且需要使用单独的工具进行可视化。

ePlant:一款可视化的分析工具

目前系统生物学研究面临一个重大挑战:不同类型的数据必须从不同来源获取,且需要使用单独的工具进行可视化。完成这样的工作流程所需的高认知负荷,对假设生成是十分不利的。因此,科学家需要一个能够结合所有数据的强大的研究平台,并通过单个门户实现集成搜索、分析和可视化功能。

ePlant,一款可视化的分析工具,通过可缩放的用户界面探索拟南芥的多层面数据。ePlant通过链接到几个公共的数据库,下载单个或多个感兴趣的基因或基因产物的基因组、蛋白质组、转录组和三维分子结构数据。这些数据通过可视化工具使用概念层次结构从大到小呈现,不通的工具组合代表着不同数据类型的信息。本文描述了ePlant的发展,并举例说明其在“假设生成”上的综合特征,同时描述了ePlant在Araport上作为一个应用程序的运行过程。基于现成的Web服务,ePlant的代码可免费提供给任何生物物种研究人员。


ePlant:一款可视化的分析工具

ePlant用户欢迎界面

Abstract

A big challenge in current systems biology research arises when different types of data must be accessed from separate sources and visualized using separate tools. The high cognitive load required to navigate such a workflow is detrimental to hypothesis generation. Accordingly, there is a need for a robust research platform that incorporates all data, and provides integrated search, analysis, and visualization features through a single portal. Here, we present ePlant (http://bar.utoronto.ca/eplant), a visual analytic tool for exploring multiple levels of Arabidopsis data through a zoomable user interface. ePlant connects to several publicly available web services to download genome, proteome, interactome, transcriptome, and 3D molecular structure data for one or more genes or gene products of interest. Data are displayed with a set of visualization tools that are presented using a conceptual hierarchy from big to small, and many of the tools combine information from more than one data type. We describe the development of ePlant in this paper and present several examples illustrating its integrative features for hypothesis generation. We also describe the process of deploying ePlant as an "app" on Araport. Building on readily available web services, the code for ePlant is freely available for any other biological species research.


来源:

植物表型资讯

Plant Cell.  

ePlant: Visualizing and Exploring Multiple Levels of Data for Hypothesis Generation in Plant Biology

Jamie Waese, Jim Fan, Asher Pasha, Hans Yu, Geoffrey Fucile, Ruian Shi, Matthew Cumming, Lawrence Kelley, Michael Sternberg, Vivek Krishnakumar, Erik Ferlanti, Jason Miller, Chris Town, Wolfgang Stuerzlinger, Nicholas J. Provart