PYMEVisualize: an open-source tool for exploring 3D super-resolution data
PYMEVisualize: an open-source tool for exploring 3D super-resolution data
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PYMEVisualize：用于探索 3D 超分辨率数据的开源工具
DOI：
10.1101/2020.09.29.315671
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Marin Z
中科院分区：
文献类型：
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作者：
Marin Z
单分子定位显微镜技术，如PALM，STORM和PAINT是生物发现越来越重要的工具。这些方法生成单个荧光团位置的列表，这些位置捕获亚细胞组织的纳米级结构细节，但是为了开发生物学洞察力，我们必须以有意义的方式对这些数据进行后处理和可视化。已经开发了许多算法用于定位后处理1，2，将点数据转换为近似传统显微镜图像的表示2 -4，并直接对点1，2，5-7进行特定的定量分析。然而，可用的软件包（补充说明3）通常实现这些算法的一小部分，需要涉及多个不同软件包的复杂工作流程。在这里，我们提出PYMEVisualize，一个开源工具，用于交互式探索和分析三维（3D），可编程，单分子定位数据。PYMEVisualize在一个易于使用和可扩展的软件包中汇集了广泛的最常用的后处理，密度映射和直接定量工具（图1）。该软件是Python显微镜环境（http：//python-microscopy. org），一个集成的应用程序套件，用于光学显微镜采集，数据存储，可视化和分析，建立在科学Python环境之上7。
To the Editor—Single-molecule localization microscopy techniques such as PALM, STORM, and PAINT are increasingly critical tools for biological discovery. These methods generate lists of single fluorophore positions that capture nanoscale structural details of subcellular organization, but to develop biological insight, we must postprocess and visualize these data in a meaningful way. Many algorithms have been developed for localization postprocessing1, 2, transforming point data into representations that approximate traditional microscopy images2–4, and performing specific quantitative analysis directly on points1, 2, 5–7. Available packages (Supplementary Note 3), however, typically implement a small subset of these algorithms, necessitating complex workflows involving multiple different software packages. Here we present PYMEVisualize, an open-source tool for the interactive exploration and analysis of three-dimensional (3D), multicolor, single-molecule localization data. PYMEVisualize brings together a broad range of the most commonly used postprocessing, density mapping and direct quantification tools in an easy-to-use and extensible package (Fig. 1). This software is one component of the Python Microscopy Environment (http://python-microscopy. org), an integrated application suite for light microscopy acquisition, data storage, visualization and analysis built on top of the scientific Python environment7.
影响因子：
6.8
作者：
Coltharp, Carla;Yang, Xinxing;Xiao, Jie
通讯作者：
Xiao, Jie