Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden.
Biophys J. 2018 Jul 17;115(2):276-282. doi: 10.1016/j.bpj.2018.05.027. Epub 2018 Jun 21.
Single-particle tracking offers a noninvasive high-resolution probe of biomolecular reactions inside living cells. However, efficient data analysis methods that correctly account for various noise sources are needed to realize the full quantitative potential of the method. We report algorithms for hidden Markov-based analysis of single-particle tracking data, which incorporate most sources of experimental noise, including heterogeneous localization errors and missing positions. Compared to previous implementations, the algorithms offer significant speedups, support for a wider range of inference methods, and a simple user interface. This will enable more advanced and exploratory quantitative analysis of single-particle tracking data.
单颗粒追踪为活细胞内生物分子反应提供了一种非侵入性的高分辨率探测手段。然而，需要有效的数据分析方法来正确考虑各种噪声源，以实现该方法的全部定量潜力。我们报告了基于隐马尔可夫模型的单颗粒追踪数据分析算法，该算法纳入了大多数实验噪声源，包括异质定位误差和缺失位置。与以前的实现相比，这些算法提供了显著的加速，支持更广泛的推断方法，以及简单的用户界面。这将使单颗粒追踪数据的更高级和探索性的定量分析成为可能。
