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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
Poster Title: 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
Author(s): Shawn McClelland, Emily M. Anderson, Žaklina Strezoska, Elena Maksimova, Annaleen Vermeulen, Steve Lenger, Tyler Reed, and Anja van Brabant Smith
Affiliations: Dharmacon part of Horizon Discovery Group
This poster was presented at GDI conference 2017
Poster Views: 844
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Poster Information
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).
Summary: The CRISPR-Cas9 system has the potential to significantly advance basic and applied research.References: Report abuse »
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