Cryo-RALib——一个用于加速冷冻电镜（cryo-EM）中比对的模块化库
Cryo-RALib -- a modular library for accelerating alignment in cryo-EM
AI总结：
本文提出模块化 GPU 加速库 Cryo-RALib，通过扩展 CUDA 并集成 Python 数据科学栈来加速冷冻电镜多参考比对分类，TWCC 测试显示计算速度提升一个数量级。
摘要：
得益于自动化冷冻电镜（cryo-EM）和 GPU 加速处理，单颗粒冷冻电镜已成为一种快速的结构测定方法，能够捕捉溶液中分子的动态结构；近期在2020年1月底 COVID-19 暴发后不久，便于3月测定了 COVID-19 刺突蛋白结构，证明了这一点。这种快速性对于应对新出现大流行病的疫苗开发至关重要。这也解释了为什么基于多参考比对（MRA）的二维分类方法不如基于贝叶斯的方法流行，尽管前者在低信噪比下区分结构变化方面具有优势。这或许是因为 MRA 是一个耗时的过程，并且缺乏用于 MRA 的模块化 GPU 加速库。在此，我们介绍一个名为 Cryo-RALib 的库，它扩展了 GPU ISAC 所使用的 CUDA 库的功能。该库包含一个 GPU 加速的 MRA 例程，用于加速基于 MRA 的分类算法。此外，我们将冷冻电镜图像分析与 Python 数据科学技术栈相连接，以便用户更轻松地进行数据分析和可视化。在台湾计算云（TWCC）容器上进行的基准测试表明，我们的实现可将计算加速一个数量级。该库可在 https://github.com/phonchi/Cryo-RAlib 获取。
英文摘要：
Thanks to automated cryo-EM and GPU-accelerated processing, single-particle cryo-EM has become a rapid structure determination method that permits capture of dynamical structures of molecules in solution, which has been recently demonstrated by the determination of COVID-19 spike protein in March, shortly after its breakout in late January 2020. This rapidity is critical for vaccine development in response to emerging pandemic. This explains why a 2D classification approach based on multi-reference alignment (MRA) is not as popular as the Bayesian-based approach despite that the former has advantage in differentiating structural variations under low signal-to-noise ratio. This is perhaps because that MRA is a time-consuming process and a modular GPU-acceleration library for MRA is lacking. Here, we introduce a library called Cryo-RALib that expands the functionality of CUDA library used by GPU ISAC. It contains a GPU-accelerated MRA routine for accelerating MRA-based classification algorithms. In addition, we connect the cryo-EM image analysis with the python data science stack so as to make it easier for users to perform data analysis and visualization. Benchmarking on the TaiWan Computing Cloud (TWCC) container shows that our implementation can accelerate the computation by one order of magnitude. The library is available at https://github.com/phonchi/Cryo-RAlib.
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