Life continues to shock and amaze us, reminding us that truth is far stranger than fiction. http://Euplotid.io is a quantized, geometric model of the eukaryotic cell, an attempt at quantifying the incredible complexity that gives rise to a living cell by beginning from the smallest unit, a quanta. Starting from the very bottom we are able to build the pieces which when hierarchically and combinatorially combined produce the emergent complex behavior that even a single celled organism can show. Euplotid is composed of a set of quantized geometric 3D building blocks and constantly evolving dockerized bioinformatic pipelines enabling a user to build and interact with the local regulatory architecture of every gene starting from DNA-interactions, chromatin accessibility, and RNA-sequencing. Reads are quantified using the latest computational tools and the results are normalized, quality-checked, and stored. The local regulatory architecture of each gene is built using a Louvain based graph partitioning algorithm parameterized by the chromatin extrusion model and CTCF-CTCF interactions. Cis-Regulatory Elements are defined using chromatin accessibility peaks which are mapped to Transcriptional Start Sites based on inclusion within the same neighborhood. Deep Neural Networks are trained in order to provide a statistical model mimicking transcription factor binding, giving the ability to identify all Transcription Factors within a given chromatin accessibility peak. By in-silico mutating and re-applying the neural network we are able to gauge the impact of a transition mutation on the binding of any transcription factor. The annotated output can be visualized in a variety of 1D, 2D, 3D and 4D ways overlaid with existing bodies of knowledge such as GWAS results or PDB structures. Once a particular CRE of interest has been identified a Base Editor mediated transition mutation can then be performed in a relevant model for further study.

中文翻译：

---

真核细胞：真核细胞的量化几何模型

生活继续震撼着我们，使我们感到惊奇，这使我们意识到，真理远比小说更陌生。http://Euplotid.io是真核细胞的量化几何模型，它试图从最小的单位量子开始，量化导致活细胞产生的令人难以置信的复杂性。从最底层开始，我们能够构建各个部分，这些部分在进行分层和组合组合时会产生甚至单个细胞生物都可以表现出的新兴复杂行为。Euplotid由一组量化的几何3D构建块和不断发展的dockerized生物信息流水线组成，使用户能够从DNA相互作用，染色质可及性和RNA测序开始，构建每个基因的本地调控体系结构并与之交互。使用最新的计算工具对读数进行定量，然后对结果进行归一化，质量检查和存储。使用基于Louvain的图分区算法（由染色质挤出模型和CTCF-CTCF相互作用参数化）建立每个基因的局部调控体系。使用染色质可及性峰定义顺式调控元件，该峰基于包含在同一邻域中的物质映射到转录起始位点。训练了深度神经网络以提供模仿转录因子结合的统计模型，从而能够识别给定染色质可及性峰内的所有转录因子。通过计算机内突变并重新应用神经网络，我们能够评估过渡突变对任何转录因子结合的影响。带注释的输出可以以各种1D，2D，3D和4D方式可视化，并覆盖现有知识体系，例如GWAS结果或PDB结构。一旦确定了感兴趣的特定CRE，就可以在相关模型中进行碱基编辑器介导的转移突变，以供进一步研究。