A comparison of step-detection methods: How well can you do?
A comparison of step-detection methods: How well can you do?
复制标题
DOI：
10.1529/biophysj.107.110601
复制
发表时间：
2008-01-01
影响因子：
3.4
通讯作者：
Gross, Steven P.
中科院分区：
文献类型：
--
作者：
Carter, Brian C.;Vershinin, Michael;Gross, Steven P.
许多生物机器以离散的步骤运行，并且检测这些步骤可以提供对机器动态的洞察。因此，开发一种自动化方法来检测步骤并确定其成功如何受到通常存在的显著噪声的影响至关重要。在以前的研究中已经使用了许多步骤检测方法，但尚未评估其鲁棒性和相对成功率。在这里，我们比较了四种步进检测方法在人工基准数据上的性能（模拟不同的数据采集和步进速率，以及不同数量的高斯噪声）。对于每一种方法，我们研究如何通过参数选择和通过预过滤的数据来优化性能。虽然我们的分析表明，许多测试方法在优化时具有相似的性能，但我们发现基于卡方优化过程的方法最容易优化，并且具有出色的时间分辨率。最后，我们将这些步骤检测方法的问题，观察到的货物移动的多个驱动蛋白电机在体外的步长。我们的结论是，在我们的多个电机记录中，有强有力的证据表明货物质心的步长为8纳米以下。
Many biological machines function in discrete steps, and detection of such steps can provide insight into the machines' dynamics. It is therefore crucial to develop an automated method to detect steps, and determine how its success is impaired by the significant noise usually present. A number of step detection methods have been used in previous studies, but their robustness and relative success rate have not been evaluated. Here, we compare the performance of four step detection methods on artificial benchmark data (simulating different data acquisition and stepping rates, as well as varying amounts of Gaussian noise). For each of the methods we investigate how to optimize performance both via parameter selection and via prefiltering of the data. While our analysis reveals that many of the tested methods have similar performance when optimized, we find that the method based on a chi-squared optimization procedure is simplest to optimize, and has excellent temporal resolution. Finally, we apply these step detection methods to the question of observed step sizes for cargoes moved by multiple kinesin motors in vitro. We conclude there is strong evidence for sub-8-nm steps of the cargo's center of mass in our multiple motor records.