Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.10.27.354092v1?rss=1 Authors: Sowunmi, K., Soyebo, T. A., Okosesi, E. A., Adesiyan, A. L., Oladimeji, K. A., Ajibola, O. A., Ogunlana, Y. O., Agboola, O. W., Kaur, G., Atoromola, H., Oladipupo, T. A. Abstract: The development of precise DNA editing nucleases that induce double-strand breaks (DSBs) - including zinc finger nucleases, TALENs, and CRISPR/Cas systems - has revolutionized gene editing and genome engineering. Endogenous DNA DSB repair mechanisms are often leveraged to enhance editing efficiency and precision. While the non-homologous end joining (NHEJ) and homologous recombination (HR) DNA DSB repair pathways have already been the topic of an excellent deal of investigation, an alternate pathway, microhomology-mediated end joining (MMEJ), remains relatively unexplored. However, the MMEJ pathway's ability to supply reproducible and efficient deletions within the course of repair makes it a perfect pathway to be used in gene knockouts. (Microhomology Evoked Deletion Judication EluciDation) may be a random forest machine learning-based method for predicting the extent to which the location of a targeted DNA DSB are going to be repaired using the MMEJ repair pathway. On an independent test set of 24 HeLa cell DSB sites, MEDJED achieved a Pearson coefficient of correlation (PCC) of 81.36%, Mean Absolute Error (MAE) of 10.96%, and Root Mean Square Error (RMSE) of13.09%. This performance demonstrates MEDJED's value as a tool for researchers who wish to leverage MMEJ to supply efficient and precise gene knock outs. Copy rights belong to original authors. Visit the link for more info