Leaf-GP: an open and automated software application for measuring growth phenotypes for arabidopsis and wheat.
Leaf-GP: an open and automated software application for measuring growth phenotypes for arabidopsis and wheat.
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DOI：
10.1186/s13007-017-0266-3
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发表时间：
2017
期刊：
影响因子：
5.1
通讯作者：
Pullen N
中科院分区：
文献类型：
--
作者：
Zhou J;Applegate C;Alonso AD;Reynolds D;Orford S;Mackiewicz M;Griffiths S;Penfield S;Pullen N
植物表现出由遗传和环境因素决定的动态生长表型。随着时间的推移，生长特征的表型分析是了解植物如何与环境变化相互作用以及对不同处理的反应的关键方法。尽管测量动态生长性状的重要性已得到广泛认可，但可用的开放软件工具在批量图像处理、多性状分析、软件可用性和实验之间的交叉参考结果方面受到限制，使得自动化表型分析存在问题。在这里，我们提出了叶GP（生长表型），一个易于使用和开放的软件应用程序，可以在不同的计算平台上执行。为了促进不同的科学社区，我们提供了三个软件版本，包括个人计算机（PC）用户的图形用户界面（GUI），高性能计算机（HPC）用户的命令行界面，以及为计算生物学家和计算机科学家提供的评论良好的交互式Python笔记本（也称为iPython笔记本）。该软件能够从大型图像数据集中自动提取多种生长性状。我们已经在诺维奇研究园（NRP，英国）的拟南芥和小麦（Triticum aestivum）生长研究中使用了它。通过定量的生长表型随着时间的推移，我们已经确定了不同的植物生长模式在几个实验条件下不同的基因型。由于Leaf-GP已通过不同成像设备（例如智能手机和数码相机）获取的噪声图像系列进行评估，并且仍然产生可靠的生物输出，因此我们相信我们的自动化分析工作流程和基于计算机视觉的定制特征提取软件实施可以促进更广泛的植物研究社区的生长和发育研究。此外，由于我们基于开放的基于Python的计算机视觉，图像分析和机器学习库实现了Leaf-GP，我们相信我们的软件不仅可以为生物研究做出贡献，而且还演示了如何利用现有的开放数字和科学库（例如Scikit-image，OpenCV，SciPy和Scikit-learn）以高效和有效的方式构建合理的植物表型组学分析解决方案。Leaf-GP是一个复杂的软件应用程序，它提供了三种方法来量化来自大型图像系列的生长表型。我们证明了它的有用性和高精度的基础上两个生物应用：（1）在两种温度条件下的拟南芥基因型的生长性状的量化;（2）测量小麦生长在温室中随着时间的推移。该软件易于使用和跨平台，可以在Mac OS，Windows和HPC上执行，并预装了基于Python的开放式科学库。我们的工作展示了如何将计算机视觉，图像分析，机器学习和软件工程集成到植物表型组学软件实现中的进展。为了服务于植物研究社区，我们的调制源代码，详细的注释，可执行文件（Windows的.exe; Mac的.app）和实验结果可在https://github.com/Crop-Phenomics-Group/Leaf-GP/releases上免费获得。本文的在线版本（10.1186/s13007-017-0266-3）包含补充材料，可供授权用户使用。
Plants demonstrate dynamic growth phenotypes that are determined by genetic and environmental factors. Phenotypic analysis of growth features over time is a key approach to understand how plants interact with environmental change as well as respond to different treatments. Although the importance of measuring dynamic growth traits is widely recognised, available open software tools are limited in terms of batch image processing, multiple traits analyses, software usability and cross-referencing results between experiments, making automated phenotypic analysis problematic. Here, we present Leaf-GP (Growth Phenotypes), an easy-to-use and open software application that can be executed on different computing platforms. To facilitate diverse scientific communities, we provide three software versions, including a graphic user interface (GUI) for personal computer (PC) users, a command-line interface for high-performance computer (HPC) users, and a well-commented interactive Jupyter Notebook (also known as the iPython Notebook) for computational biologists and computer scientists. The software is capable of extracting multiple growth traits automatically from large image datasets. We have utilised it in Arabidopsis thaliana and wheat (Triticum aestivum) growth studies at the Norwich Research Park (NRP, UK). By quantifying a number of growth phenotypes over time, we have identified diverse plant growth patterns between different genotypes under several experimental conditions. As Leaf-GP has been evaluated with noisy image series acquired by different imaging devices (e.g. smartphones and digital cameras) and still produced reliable biological outputs, we therefore believe that our automated analysis workflow and customised computer vision based feature extraction software implementation can facilitate a broader plant research community for their growth and development studies. Furthermore, because we implemented Leaf-GP based on open Python-based computer vision, image analysis and machine learning libraries, we believe that our software not only can contribute to biological research, but also demonstrates how to utilise existing open numeric and scientific libraries (e.g. Scikit-image, OpenCV, SciPy and Scikit-learn) to build sound plant phenomics analytic solutions, in a efficient and effective way. Leaf-GP is a sophisticated software application that provides three approaches to quantify growth phenotypes from large image series. We demonstrate its usefulness and high accuracy based on two biological applications: (1) the quantification of growth traits for Arabidopsis genotypes under two temperature conditions; and (2) measuring wheat growth in the glasshouse over time. The software is easy-to-use and cross-platform, which can be executed on Mac OS, Windows and HPC, with open Python-based scientific libraries preinstalled. Our work presents the advancement of how to integrate computer vision, image analysis, machine learning and software engineering in plant phenomics software implementation. To serve the plant research community, our modulated source code, detailed comments, executables (.exe for Windows; .app for Mac), and experimental results are freely available at https://github.com/Crop-Phenomics-Group/Leaf-GP/releases. The online version of this article (10.1186/s13007-017-0266-3) contains supplementary material, which is available to authorized users.
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影响因子：
3.6
作者：
Easlon HM;Bloom AJ
通讯作者：
Bloom AJ
影响因子：
8.3
作者：
Chene, Yann;Rousseau, David;Chapeau-Blondeau, Francois
通讯作者：
Chapeau-Blondeau, Francois
影响因子：
10.6
作者：
Flusser, Jan;Suk, Tomas
通讯作者：
Suk, Tomas
影响因子：
48
作者：
Eliceiri, Kevin W.;Berthold, Michael R.;Goldberg, Ilya G.;Ibanez, Luis;Manjunath, B. S.;Martone, Maryann E.;Murphy, Robert F.;Peng, Hanchuan;Plant, Anne L.;Roysam, Badrinath;Stuurmann, Nico;Swedlow, Jason R.;Tomancak, Pavel;Carpenter, Anne E.
通讯作者：
Carpenter, Anne E.
影响因子：
7.2
作者：
Dhondt, Stijn;Van Haerenborgh, Dirk;Inze, Dirk
通讯作者：
Inze, Dirk