RT Journal Article
SR Electronic
T1 Euplotid: A quantized geometric model of the eukaryotic cell
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 170159
DO 10.1101/170159
A1 Diego Borges-Rivera
YR 2020
UL http://biorxiv.org/content/early/2020/06/04/170159.abstract
AB 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.Figure 0.1: Graphical AbstractCompeting Interest StatementThe authors have declared no competing interest.