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Project 7: Improving multi-locus sequence typing software #7
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This project looks interesting to me. Would it be possible to provide more information such as relevant papers, computational tools/libraries that you are planning to use, etc.? Perhaps in a github project page? |
@hyounesy You could take a look at the MLST caller that @pedrofeijao and @Leonardini developed: https://github.com/WGS-TB/MentaLiST |
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_7 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 |
Improving multi-locus sequence typing software
The study of the genomes of Borrelia burgdorferi, the organism that causes Lyme disease and is spread to animal hosts by ticks such as Ixodes scapularis, has been hindered by two challenges - the difficulty of capturing the whole genome of Borrelia from an infected tick, and the presence of multiple strains in a single tick vector. The first challenge was recently overcome by my colleagues and collaborators on the East Coast. However, the second challenge still remains. This project will aim at addressing this challenge by applying innovative computational biology techniques and data structures. In particular, there will be two phases to this project. In the first phase, we will develop algorithms to accurately identify those variants of a particular gene that are present inside a given sample, by using a library of known variants and a variety of tools for genome analysis, and calibrate those algorithms using simulated data. In the second phase, we will develop novel methods to accurately estimate the fractions of each variant present in a given sample, and calibrate those using simulation data as well. If successful, this project would be the first to manage to detect multiple variants within a single tick using whole-genome capture data from Borrelia. This could have significant implications on our understanding of the ecology and host specificity of Borrelia.
Team Lead: Leonid Chindelevitch | [email protected] | @Leonardini | Professor | Simon Fraser University
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