用高斯过程分析蛋白熔解曲线，突破传统方法的5%检测上限。
Thermal Tracks: A Gaussian process-based framework for universal melting curve analysis enabling unconstrained hit identification in thermal proteome profiling experiments
- 基于高斯过程建模任意熔解曲线形状，无需假设为S型。
- 通过核先验生成无偏零分布，显著提升异常信号检出率。
- 适合研究复杂蛋白如相分离蛋白、膜蛋白的热稳定性变化。
Thermal Tracks 是一个基于 Python 的统计分析框架，用于解析蛋白质热稳定性数据，克服了现有热蛋白组学（TPP）工作流程的关键局限。与传统方法假设熔解曲线为S型且受经验零分布限制（导致显著差异仅占约5%数据）不同，Thermal Tracks 采用带平方指数核的高斯过程模型，可灵活拟合任意熔解曲线形态，并通过核先验生成无偏零分布。该框架特别适用于分析显著改变蛋白热稳定性的全局扰动，如通路抑制、基因修饰或环境应激；在这些情况下，传统方法可能因统计约束而遗漏生物相关变化。此外，Thermal Tracks 在分析非典型熔解行为的蛋白（如相分离蛋白和膜蛋白）方面表现优异。该工具已开源，可通过 GitHub 免费获取，为大规模热蛋白组分析提供可访问、灵活的解决方案。
原文摘要 · Abstract (English)
Thermal Tracks is a Python-based statistical framework for analyzing protein thermal stability data that overcomes key limitations of existing thermal proteome profiling (TPP) work-flows. Unlike standard approaches that assume sigmoidal melting curves and are constrained by empirical null distributions (limiting significant hits to approximately 5 % of data), Thermal Tracks uses Gaussian Process (GP) models with squared-exponential kernels to flexibly model any melting curve shape while generating unbiased null distributions through kernel priors. This framework is particularly valuable for analyzing proteome-wide perturbations that significantly alter protein thermal stability, such as pathway inhibitions, genetic modifications, or environmental stresses, where conventional TPP methods may miss biologically relevant changes due to their statistical constraints. Furthermore, Thermal Tracks excels at analyzing proteins with un-conventional melting profiles, including phase-separating proteins and membrane proteins, which often exhibit complex, non-sigmoidal thermal stability behaviors. Thermal Tracks is freely available from GitHub and is implemented in Python, providing an accessible and flexible tool for proteome-wide thermal profiling studies.
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