TRamWAy: mapping physical properties of individual biomolecule random motion in large-scale single-particle tracking experiments
TRamWAy: mapping physical properties of individual biomolecule random motion in large-scale single-particle tracking experiments
复制标题
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特性
DOI：
10.1093/bioinformatics/btac291
复制
发表时间：
2022
期刊：
影响因子：
5.8
通讯作者：
J. Masson
中科院分区：
文献类型：
--
作者：
François Laurent;Hippolyte Verdier;M. Duval;A. Serov;Christian L. Vestergaard;J. Masson
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，并解析分子的生物物理特性及其细胞功能的环境。随着单个实验产生的数据量的不断增长，量化这些性质的计算成本日益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箱。
结果
我们介绍TRamWAy，一个模块化的Python库，它具有以下特点：1.用于定位数据的保守跟踪过程，2.一系列采样技术，用于网格化数据的时空支持，3.逆模型的计算高效求解器，可选择插入用户定义的函数，4.一系列分析工具和一个简单的基于Web的界面。
可用性
TRamWAy是一个Python库，可以通过pip和conda安装。源代码可在https://github.com/DecBayComp/TRamWAy上获得。
教学和辅导
可在https://tramway-tour.readthedocs.io上在线获得。
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 require an integrated and efficient analysis toolbox.
RESULTS
We introduce TRamWAy, a modular Python library that features: 1. a conservative tracking procedure for localization data, 2. a range of sampling techniques for meshing the spatio-temporal support of the data, 3. computationally efficient solvers for inverse models, with the option of plugging in user-defined functions, 4. a collection of analysis tools and a simple web-based interface.
AVAILABILITY
TRamWAy is a Python library and can be installed with pip & conda. The source code is available at https://github.com/DecBayComp/TRamWAy.
MANUAL AND TUTORIALS
available online at https://tramway-tour.readthedocs.io.
影响因子：
48
作者：
Jaqaman, Khuloud;Loerke, Dinah;Mettlen, Marcel;Kuwata, Hirotaka;Grinstein, Sergio;Schmid, Sandra L.;Danuser, Gaudenz
通讯作者：
Danuser, Gaudenz
影响因子：
4.6
作者：
Floderer C;Masson JB;Boilley E;Georgeault S;Merida P;El Beheiry M;Dahan M;Roingeard P;Sibarita JB;Favard C;Muriaux D
通讯作者：
Muriaux D