迈向可扩展蛋白质组学：HTCondor上的机会性SMC采样器
Towards Scalable Proteomics: Opportunistic SMC Samplers on HTCondor
AI总结：
针对蛋白质组学大规模贝叶斯推理的计算瓶颈，提出基于HTCondor空闲资源的机会性SMC框架，采用协调者-管理者-跟随者架构，实现弱扩展的准确推理，并开源CondorSMC。
摘要：
定量蛋白质组学在揭示调控机制、识别疾病生物标志物以及指导精准疗法开发中发挥着核心作用。这些见解通常通过复杂的贝叶斯模型获得，其推理过程计算密集，尤其是在大规模应用于生物数据集时。这限制了充分利用蛋白质组学数据所需的高级建模技术的可及性。尽管序贯蒙特卡洛（SMC）方法为传统马尔可夫链蒙特卡洛提供了一种可并行化的替代方案，但其高性能实现往往依赖专用硬件，增加了财务和能源成本。我们通过引入一种针对大规模蛋白质组学推理需求定制的机会性计算框架来解决这些挑战。我们的方法利用利物浦大学通过HTCondor提供的空闲计算资源，无需专用高性能计算基础设施即可实现可扩展的贝叶斯推理。该框架的核心是一种新颖的协调者-管理者-跟随者架构，该架构减少了同步开销，并支持在异构、不可靠环境中的稳健运行。我们在一个真实的蛋白质组学模型上评估了该框架，结果表明机会性SMC能够提供准确的推理，并具有弱扩展性，随着更多资源加入，在固定时间预算内生成的样本数量增加。为支持采用，我们发布了CondorSMC，这是一个用于在机会性计算环境中部署SMC采样器的开源软件包。
英文摘要：
Quantitative proteomics plays a central role in uncovering regulatory mechanisms, identifying disease biomarkers, and guiding the development of precision therapies. These insights are often obtained through complex Bayesian models, whose inference procedures are computationally intensive, especially when applied at scale to biological datasets. This limits the accessibility of advanced modelling techniques needed to fully exploit proteomics data. Although Sequential Monte Carlo (SMC) methods offer a parallelisable alternative to traditional Markov Chain Monte Carlo, their high-performance implementations often rely on specialised hardware, increasing both financial and energy costs. We address these challenges by introducing an opportunistic computing framework for SMC samplers, tailored to the demands of large-scale proteomics inference. Our approach leverages idle compute resources at the University of Liverpool via HTCondor, enabling scalable Bayesian inference without dedicated high-performance computing infrastructure. Central to this framework is a novel Coordinator-Manager-Follower architecture that reduces synchronisation overhead and supports robust operation in heterogeneous, unreliable environments. We evaluate the framework on a realistic proteomics model and show that opportunistic SMC delivers accurate inference with weak scaling, increasing samples generated under a fixed time budget as more resources join. To support adoption, we release CondorSMC, an open-source package for deploying SMC samplers in opportunistic computing environments.
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