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Project 8: Analysis of tested guide RNAs used for gene editing via CRISPR to predict optimal future guide sequences #8

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abaghela opened this Issue Aug 2, 2017 · 2 comments

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@abaghela
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abaghela commented Aug 2, 2017

Analysis of tested guide RNA’s used for gene editing via CRISPR to predict optimal future guide sequences

Despite the apparent simplicity of this sequence matching strategy to target the CRISPR machinery to the correct location in the DNA, all sgRNAs do not work equivalently well (discussed in Doench et al., 2016). It is hypothesized that there are other aspects/features of the sgRNAs themselves, as well as the target and the surrounding DNA sequences in the genome, that effect how well a given sgRNA will work (i.e., how efficiently it will target the CRISPR machinery to the correct place in the genome versus targetting it somewhere else). This project aims to use a machine learning approach to identify the key features for optimal sgRNA sequences to make targetting of the CRISPR gene editing machinery more efficient and effective. These features could then be used to rank and predict success/failure of future/newly designed sgRNAs. Such a tool would improve gene editing success rates, reduce off-target effects and thus potentially help fast track the use of CRISPR technology for human health applications.

Team Lead: Matthew Emery | matthew.emery44@gmail.com | @lstmemery | Grad Student | UBC

@abaghela abaghela changed the title from Project 8: Analysis of tested guide RNA’s used for gene editing via CRISPR to predict optimal future guide sequences to Project 8: Analysis of tested guide RNA used for gene editing via CRISPR to predict optimal future guide sequences Aug 24, 2017

@abaghela abaghela changed the title from Project 8: Analysis of tested guide RNA used for gene editing via CRISPR to predict optimal future guide sequences to Project 8: Analysis of tested guide RNAs used for gene editing via CRISPR to predict optimal future guide sequences Aug 24, 2017

@lstmemery

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lstmemery commented Oct 2, 2017

Could we get scikit-learn and pytorch preinstalled on our ORCA machines?

@jakelever

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jakelever commented Oct 10, 2017

Hey team lead, we've been gathering Github IDs for your team members. As you've likely been notified, we've created a project repo for you that you are now the admin of and have added the team members to this. We've received almost everyone's Github ID and will continue to add members as we got their Github IDs.

Project repo: https://github.com/hackseq/2017_project_8

Feel free to rename the repo as appropriate. Note that the repo currently has an MIT license. Amend this as required. It'd be a great idea to start a discussion on this repo with information to get your team members started (e.g. some small suggested reading, things to look up, etc). We will also be adding everyone to Slack and creating a specific channel for each project. This may be an easier way to communicate.

Thanks, Jake
obo the Hackseq organising committee

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