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Picking the best CRISPR-Cas9 targets for functional gene knockout: a machine learning algorithm based on both specificity and functionality
EP26581
Picking the best CRISPR-Cas9 targets for functional gene knockout: a machine learning algorithm based on both specificity and functionality
Submitted on 25 Oct 2017

Shawn McClelland, Emily M. Anderson, Žaklina Strezoska, Elena Maksimova, Annaleen Vermeulen, Steve Lenger, Tyler Reed, and Anja van Brabant Smith
Dharmacon part of Horizon Discovery Group
This poster was presented at GDI conference 2017
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Poster Abstract
Functional gene knockout is an important tool for understanding a gene’s role in a system or for specifically manipulating a known system to achieve a
desired outcome.
Not all gene cleavage events result in functional knockout of the target protein.
Here we share important advancements that have helped to achieve the goal of picking the best crRNA targets for functional gene knockout, and not just formation of indels (insertions or the deletions of bases in the DNA).

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