High dimensional nature of the chromosomal conformation contact map (‘Hi-C Map’), even for microscopically small bacterial cell, poses challenges for extracting meaningful information related to its complex organization. Here we first demonstrate that an artificial deep neural network-based machine-learnt (ML) low-dimensional representation of a recently reported Hi-C interaction map of archetypal bacteria Escherichia coli can decode crucial underlying structural pattern. The ML-derived representation of Hi-C map can automatically detect a set of spatially distinct domains across E. coli genome, sharing reminiscences of six putative macro-domains previously posited via recombination assay. Subsequently, a ML-generated model assimilates the intricate relationship between large array of Hi-C-derived chromosomal contact probabilities and respective diffusive dynamics of each individual chromosomal gene and identifies an optimal number of functionally important chromosomal contact-pairs that are majorly responsible for heterogenous, coordinate-dependent sub-diffusive motions of chromosomal loci. Finally, the ML models, trained on wild-type E. coli show-cased its predictive capabilities on mutant bacterial strains, shedding light on the structural and dynamic nuances of ΔMatP30MM and ΔMukBEF22MM chromosomes. Overall our results illuminate the power of ML techniques in unraveling the complex relationship between structure and dynamics of bacterial chromosomal loci, promising meaningful connections between ML-derived insights and biological phenomena.

中文翻译：

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机器学习揭示了大肠杆菌 Hi-C 基质中的固有结构模式并预测染色体动力学


染色体构象接触图（“Hi-C 图”）的高维性质，即使对于微观较小的细菌细胞，也对提取与其复杂组织相关的有意义信息构成挑战。在这里，我们首先证明了最近报道的原型细菌大肠杆菌的 Hi-C 相互作用图的基于人工深度神经网络的机器学习 （ML） 低维表示可以解码关键的潜在结构模式。Hi-C 图谱的 ML 衍生表示可以自动检测大肠杆菌基因组中的一组空间上不同的结构域，共享先前通过重组测定提出的六个假定宏结构域的回忆。随后，ML 生成的模型吸收了大量 Hi-C 衍生的染色体接触概率与每个单独染色体基因的相应扩散动力学之间的复杂关系，并确定了功能上重要的染色体接触对的最佳数量，这些接触对主要负责染色体基因座的异质性、坐标依赖性亚扩散运动。最后，在野生型大肠杆菌上训练的 ML 模型展示了其对突变细菌菌株的预测能力，阐明了 ΔMatP30MM 和 ΔMukBEF22MM 染色体的结构和动力学细微差别。总体而言，我们的结果阐明了 ML 技术在解开细菌染色体基因座结构和动力学之间复杂关系方面的力量，有望在 ML 衍生的见解和生物现象之间建立有意义的联系。