BACKGROUND The spatial configuration of chromosomes is essential to various cellular processes, notably gene regulation, while architecture related alterations, such as translocations and gene fusions, are often cancer drivers. Thus, eliciting chromatin conformation is important, yet challenging due to compaction, dynamics and scale. However, a variety of recent assays, in particular Hi-C, have generated new details of chromatin structure, spawning a number of novel biological findings. Many findings have resulted from analyses on the level of native contact data as generated by the assays. Alternatively, reconstruction based approaches often proceed by first converting contact frequencies into distances, then generating a three dimensional (3D) chromatin configuration that best recapitulates these distances. Subsequent analyses can enrich contact level analyses via superposition of genomic attributes on the reconstruction. But, such advantages depend on the accuracy of the reconstruction which, absent gold standards, is inherently difficult to assess. Attempts at accuracy evaluation have relied on simulation and/or FISH imaging that typically features a handful of low resolution probes. While newly advanced multiplexed FISH imaging offers possibilities for refined 3D reconstruction accuracy evaluation, availability of such data is limited due to assay complexity and the resolution thereof is appreciably lower than the reconstructions being assessed. Accordingly, there is demand for new methods of reconstruction accuracy appraisal. RESULTS Here we explore the potential of recently proposed stationary distributions, hereafter StatDns, derived from Hi-C contact matrices, to serve as a basis for reconstruction accuracy assessment. Current usage of such StatDns has focussed on the identification of highly interactive regions (HIRs): computationally defined regions of the genome purportedly involved in numerous long-range intra-chromosomal contacts. Consistent identification of HIRs would be informative with respect to inferred 3D architecture since the corresponding regions of the reconstruction would have an elevated number of k nearest neighbors (kNNs). More generally, we anticipate a monotone decreasing relationship between StatDn values and kNN distances. After initially evaluating the reproducibility of StatDns across replicate Hi-C data sets, we use this implied StatDn - kNN relationship to gauge the utility of StatDns for reconstruction validation, making recourse to both real and simulated examples. CONCLUSIONS Our analyses demonstrate that, as constructed, StatDns do not provide a suitable measure for assessing the accuracy of 3D genome reconstructions. Whether this is attributable to specific choices surrounding normalization in defining StatDns or to the logic underlying their very formulation remains to be determined.

中文翻译：

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评估从染色质接触图得出的平稳分布。

背景技术染色体的空间构型对于各种细胞过程是至关重要的，特别是基因调控，而与体系结构相关的改变（例如易位和基因融合）通常是癌症的驱动因素。因此，引起染色质构象很重要，但由于紧密性，动力学和规模而具有挑战性。然而，各种最近的测定法，特别是Hi-C，已产生了染色质结构的新细节，产生了许多新的生物学发现。分析所产生的自然联系数据水平的分析得出了许多发现。替代地，基于重建的方法通常通过首先将接触频率转换为距离，然后生成最能概括这些距离的三维（3D）染色质配置来进行。随后的分析可以通过在重建过程中叠加基因组属性来丰富接触水平分析。但是，这种优势取决于重建的准确性，而缺乏金标准，重建的准确性本来就很难评估。准确性评估的尝试依赖于通常具有少量低分辨率探头的模拟和/或FISH成像。尽管新近先进的多路FISH成像为精细的3D重建精度评估提供了可能性，但由于测定的复杂性，此类数据的可用性受到限制，并且其分辨率明显低于所评估的重建。因此，需要新的重建精度评估方法。结果在这里，我们探索了最近提出的平稳分布（以下称StatDns）的潜力，源自Hi-C接触矩阵，可作为重建精度评估的基础。此类StatDns的当前用途集中在鉴定高度交互性区域（HIR）：据称在基因组上的计算上定义的区域涉嫌参与许多远程染色体内接触。相对于推断的3D架构，HIR的一致标识将提供有益的信息，因为重建的相应区域的k个最近邻居（kNN）数量将增加。更普遍地，我们期望StatDn值与kNN距离之间的单调递减关系。在初步评估了StatDns在复制的Hi-C数据集之间的可重复性之后，我们使用这种隐含的StatDn-kNN关系来评估StatDns用于重建验证的效用，求助于真实和模拟的例子。结论我们的分析表明，StatDns构建后不能为评估3D基因组重建的准确性提供合适的方法。这是否归因于定义StatDns时围绕归一化的特定选择，还是归因于其具体表达的逻辑尚待确定。