Sen Samyabrata, Vairagare Indraneel, Gosai Jitendrapuri, Shrivastava Abhishek
Center for Fundamental and Applied Microbiomics, Biodesign Institute, Arizona State University, Tempe, AZ, 85287, USA.
School of Life Sciences, Arizona State University, Tempe, AZ, 85287, USA.
BMC Bioinformatics. 2025 May 18;26(1):127. doi: 10.1186/s12859-025-06145-w.
Bacterial tracking is crucial for understanding the mechanisms governing motility, chemotaxis, cell division, biofilm formation, and pathogenesis. Although modern microscopy and computing have enabled the collection of large datasets, many existing tools struggle with big data processing or with accurately detecting, segmenting, and tracking bacteria of various shapes. To address these issues, we developed RABiTPy, an open-source Python software pipeline that integrates traditional and artificial intelligence-based segmentation with tracking tools within a user-friendly framework. RABiTPy runs interactively in Jupyter notebooks and supports numerous image and video formats. Users can select from adaptive, automated thresholding, or AI-based segmentation methods, fine-tuning parameters to fit their needs. The software offers customizable parameters to enhance tracking efficiency, and its streamlined handling of large datasets offers an alternative to existing tracking software by emphasizing usability and modular integration. RABiTPy supports GPU and CPU processing as well as cloud computing. It offers comprehensive spatiotemporal analyses that includes trajectories, motile speeds, mean squared displacement, and turning angles-while providing a variety of visualization options. With its scalable and accessible platform, RABiTPy empowers researchers, even those with limited coding experience, to analyze bacterial physiology and behavior more effectively. By reducing technical barriers, this tool has the potential to accelerate discoveries in microbiology.
细菌追踪对于理解控制运动性、趋化性、细胞分裂、生物膜形成和发病机制的机制至关重要。尽管现代显微镜技术和计算技术能够收集大量数据集，但许多现有工具在大数据处理或准确检测、分割和追踪各种形状的细菌方面存在困难。为了解决这些问题，我们开发了RABiTPy，这是一个开源的Python软件管道，它在一个用户友好的框架内将传统的和基于人工智能的分割与追踪工具集成在一起。RABiTPy可在Jupyter笔记本中交互式运行，并支持多种图像和视频格式。用户可以从自适应、自动阈值处理或基于人工智能的分割方法中进行选择，并微调参数以满足他们的需求。该软件提供可定制的参数以提高追踪效率，其对大型数据集的简化处理通过强调可用性和模块化集成，为现有追踪软件提供了一种替代方案。RABiTPy支持GPU和CPU处理以及云计算。它提供全面的时空分析，包括轨迹、运动速度、均方位移和转向角度，同时提供各种可视化选项。凭借其可扩展且易于使用的平台，RABiTPy使研究人员，即使是那些编码经验有限的研究人员，也能够更有效地分析细菌生理学和行为。通过减少技术障碍，这个工具有可能加速微生物学领域的发现。
