A Novel CRISPR-MultiTargeter Multi-agent Reinforcement learning (CMT-MARL) algorithm to identify editable target regions using a Hybrid scoring from multiple similar sequences

Article Type

Research Article

Publication Title

Applied Intelligence

Abstract

Genome edit is a modern technology to serve mankind. The main idea is derived from RNA mediated Nuclease, which is the CRISPR/Cas9 natural process of the bacterial genome. In this paper, we have developed an algorithm CMT-MARL for finding the multiple editable target site from the similar sequences. Among different types of genes, there are many common regions, which are important concerning the production of proteins or any other biological function in the organisms. Tracing multiple target sites is important for the case of gene duplication, gene fusion, finding mutations from co-expressed genes and transcripts from genes. The complexity to find out common editable targets from similar kind of sequences using brute force method is O(ln), where l is the genome sequence length and n is the number of sequences. If n goes higher then the complexity of the problem reaches to some infeasible computational time. We have applied Reinforcement learning Algorithm using Eligibility Trace and Monte Carlo method to tackle this problem. The time complexity of the algorithm CMT-MARL is O(nl2). Finally we have compared our result set with existing algorithm “CRISPR- MultiTargeter” [1] (http://www.multicrispr.net/) concerning the goodness of editing. We have used the data set from Ensembl BioMart (http://www.ensembl.org). We have run our methodology in Mouse, Rat, Zebrafish, Chicken and Human genes. Finally, we locate the optimal regions for editing diseased or duplicated genes concerning our hybrid score mechanism with all types of biological factors.

First Page

9562

Last Page

9579

DOI

https://10.1007/s10489-022-03871-z

Publication Date

4-1-2023

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