目前系統生物學研究面臨一個重大挑戰：不同類型的數據必須從不同來源獲取，且需要使用單獨的工具進行可視化。完成這樣的工作流程所需的高認知負荷，對假設生成是十分不利的。因此，科學家需要一個能夠結合所有數據的強大的研究平臺，并通過單個門戶實現集成搜索、分析和可視化功能。
ePlant，一款可視化的分析工具，通過可縮放的用戶界面探索擬南芥的多層面數據。ePlant通過鏈接到幾個公共的數據庫，下載單個或多個感興趣的基因或基因產物的基因組、蛋白質組、轉錄組和三維分子結構數據。這些數據通過可視化工具使用概念層次結構從大到小呈現，不通的工具組合代表著不同數據類型的信息。本文描述了ePlant的發展，并舉例說明其在“假設生成”上的綜合特征，同時描述了ePlant在Araport上作為一個應用程序的運行過程?；诂F成的Web服務，ePlant的代碼可免費提供給任何生物物種研究人員。
ePlant用戶歡迎界面
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.
來源：
植物表型資訊
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
