Gene Editing Technology: CRISPR-Cas9
By creating a machine-learning algorithm that predicts how human and mouse cells respond to CRISPR-induced breaks in DNA, a team of researchers discovered that cells often repair broken genes in ways that are precise and predictable, sometimes even returning mutated genes back to their healthy version. In addition, the researchers put this predictive power to the test and successfully corrected mutations in cells taken from patients with one of two rare genetic disorders.
The CRISPR-Cas9 system, a microbial adaptive immune system, was recently exploited for modulating DNA sequences within the endogenous genome in many organisms. This system has emerged as a technology of choice for genome editing with promising therapeutic and research advancements. However, these exciting developments were not paralleled by deep understanding of CRISPR-Cas9 cleavage efficiency. Indeed, while numerous studies have been conducted in order to define better guidelines to determine CRISPR-Cas9 specificity, much ambiguity remains surrounding its mechanism of action. Here, we present a machine-learning based algorithm that was trained on genome-wide experimental data. The algorithm considers a broad range of features that describe different attributes that potentially impact the cleavage efficacy of CRISPR-Cas9 including genomic attributes, RNA thermodynamics, and those concerning sequence similarity. These result in a predictive model that can be used both to predict the cleavage propensity of a new genomic site according to the genomic context, as well as to learn on the importance of different features on CRISPR-Cas9 efficiency and selectivity.
Machine learning model understanding Basic Biology understanding
Structure of the Talk:
- Why and Goal of using DL in Genomics
- Guinness Record (Whole Genome Sequencing in 19.5hrs)
- Need for Gene Editing Techniques
- Brief history
- Technology making Headlines,
- Introduction to CRISPR-Cas9 technology
- Issues with the Technology,
- On-site error
- Regression with Boosting,
- Off-site error
- Naïve Bayes
- On-site error
- CRISPR Clinical Trials
Important Links: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005807 https://www.nature.com/articles/s41551-017-0178-6 https://www.broadinstitute.org/videos/machine-learning-based-crispr-guide-design https://www.microsoft.com/en-us/research/project/crispr/
Seven years of work experience with biotechnology giant Novozymes A/S as Senior Technology Innovation Specialist. Eight years of basic biotech research and 9 years of Patent R&D. I have worked in central government research institutes like CDFD, CCMB (Hyderabad), NCCS(Pune) and CSIR-URDIP (Pune).
Biotechnologist with research experience in microbial genetic and human genomics and trained patent professional working in the area of Innovation, patinformatics (patent R&D) for adaptable Intellectual property research proposition. From past two years, trying to incorporate data science like machine learning and deep learning in my analysis.