Applications of machine learning to solve genetics problems

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.

doi:10.1101/2020.10.27.354092

bioRxiv · 10.1101/2020.10.27.354092

Applications of machine learning to solve genetics problems

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.

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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 pathways 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 MEDJEDs value as a tool for researchers who wish to leverage MMEJ to supply efficient and precise gene knock outs.

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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.. 2020-10-27. Applications of machine learning to solve genetics problems. https://doi.org/10.1101/2020.10.27.354092

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