Deep probabilistic models for analysing complex DNA structures in high-resolution atomic force microscopy images.

罗莎琳德·富兰克林建立DNA原子结构的开创性工作，在很大程度上巩固了我们对“生命分子”的理解。基因组DNA紧凑到细胞核中会导致显著的拓扑应力，并形成卷曲、扭曲和打结的DNA结构，影响细胞的活性，从DNA复制到癌症和感染中的治疗剂活性。了解这些复杂的DNA结构如何影响DNA处理的挑战从根本上受到可用的工具的限制。高分辨率原子力显微镜(AFM)的独特之处在于，它能够以纳米级的分辨率在液体中提供关于DNA结构、功能和动力学的定量信息，而无需标记或平均[1]，然而，到目前为止，这些数据集的分析一直依赖经验丰富的显微镜专家的眼睛[2]。尽管原子力显微镜产生的数据集的大小不断增加，但自动分析和/或机器学习技术并没有被常规应用。机器学习推动了我们对生物现象的理解的一步改变(例如AlphaFold)。使用人工神经网络的深度学习已被应用于用相邻显微镜(特别是低温EM在其分辨率革命中)产生的数据集，以解决以前无法访问的生物学问题。高斯过程(GP)是另一种重要的机器学习技术，在数据不那么丰富且更多地了解被建模系统的行为(例如DNA力学)的情况下很有用。我们建议使用这些和类似技术的组合，在分析复杂的生物AFM数据集时对它们进行调整和改进。

Rosalind Franklin's pioneering work to establish the atomic structure of DNA has underpinned much of our understanding of the 'molecule of life'. The compaction of genomic DNA into the nucleus results in significant topological stress and the formation of coiled, twisted and knotted DNA structures which impact cell viability, with ramifications from DNA replication to the activity of therapeutic agents in cancer and infection. The challenge of understanding how these complex DNA structures influence DNA processing has been fundamentally limited by the tools available.High-resolution atomic force microscopy (AFM) is unique in its ability to provide quantitative information on DNA structure, function and kinetics in liquid with nanometre resolution without labelling or averaging [1], however the analysis of these datasets has until now relied on the eye of an experienced microscopist [2]. Despite the increasing size of datasets generated by AFM, automated analysis and/or machine learning techniques are not routinely applied. Machine learning has driven step changes in our understanding of biological phenomena (e.g. AlphaFold). Deep learning using artificial neural networks has been applied to datasets produced with adjacent microscopies (notably cryo-EM in its resolution revolution), to solve previously inaccessible biological problems. Gaussian processes (GPs) are another important machine learning technique, useful in situations where data is less abundant and more is known about the behaviour of the system being modelled (e.g. DNA mechanics). We propose to use a combination of these and similar techniques, adapting and improving them in analysis of complex bio-AFM datasets.