TRamWAy：在大规模单粒子跟踪实验中绘制单个生物分子随机运动的物理性质图谱。
TRamWAy: mapping physical properties of individual biomolecule random motion in large-scale single-particle tracking experiments.
机构信息
Decision and Bayesian Computation, Computational Biology Department, Neuroscience Department, CNRS USR 3756, CNRS UMR 3571, Institut Pasteur, Paris 75015, France.
Institut Pasteur, Université Paris Cité, Bioinformatics and Biostatistics Hub, Paris F-75015, France.
出版信息
Bioinformatics. 2022 May 26;38(11):3149-3150. doi: 10.1093/bioinformatics/btac291.
MOTIVATION
Single-molecule localization microscopy allows studying the dynamics of biomolecules in cells and resolving the biophysical properties of the molecules and their environment underlying cellular function. With the continuously growing amount of data produced by individual experiments, the computational cost of quantifying these properties is increasingly becoming the bottleneck of single-molecule analysis. Mining these data requires an integrated and efficient analysis toolbox.
RESULTS
We introduce TRamWAy, a modular Python library that features: (i) a conservative tracking procedure for localization data, (ii) a range of sampling techniques for meshing the spatio-temporal support of the data, (iii) computationally efficient solvers for inverse models, with the option of plugging in user-defined functions and (iv) a collection of analysis tools and a simple web-based interface.
AVAILABILITY AND IMPLEMENTATION
TRamWAy is a Python library and can be installed with pip and conda. The source code is available at https://github.com/DecBayComp/TRamWAy.
动机
单分子定位显微镜允许研究细胞中生物分子的动力学，并解析分子及其环境的生物物理特性，从而了解细胞功能。随着单个实验产生的数据量不断增加，量化这些特性的计算成本越来越成为单分子分析的瓶颈。挖掘这些数据需要一个集成和高效的分析工具包。
结果
我们引入了 TRamWAy，这是一个模块化的 Python 库，具有以下特点：（i）用于定位数据的保守跟踪过程，（ii）用于网格化数据时空支持的一系列采样技术，（iii）用于逆模型的计算高效求解器，可选择插入用户定义的函数，（iv）一系列分析工具和一个简单的基于网络的界面。
可用性和实现
TRamWAy 是一个 Python 库，可以使用 pip 和 conda 进行安装。源代码可在 https://github.com/DecBayComp/TRamWAy 获得。
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