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ABCNet: Predicting Chromosomal Compartments Directly from Reference Genomes

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dc.contributor.advisor Kremer, Stefan
dc.contributor.author Kirchhof, Matthew
dc.date.accessioned 2021-04-22T19:14:41Z
dc.date.available 2021-04-22T19:14:41Z
dc.date.copyright 2021-04
dc.date.created 2021-04-12
dc.identifier.uri https://hdl.handle.net/10214/25225
dc.description.abstract Previous research has shown that the 3D organization of a cell’s genome is crucial to its functionality, directly contributing to gene regulation. Better understanding what gives rise to a genomes 3D organization, as well as the effects it has on a cell’s functionality, has the potential to lead to various breakthroughs surrounding disease, cell differentiation and more. In 2009, a biochemical assay used to capture the conformation of a genome (called Hi-C) was published, allowing us to better understand how a genome might interact with itself. From this, we can categorize regions of the genome into two compartments, those that are open (A) and those that are closed (B), where A compartments are gene rich, transcriptionally active and more loosely packed together than B compartments. This thesis presents ABCNet, a convolutional neural network (CNN) designed to predict the A/B compartments of a genome directly from its genomic sequence. Unlike other neural networks, ABCNet does not rely on a predetermined set of extracted genomic features and/or elements to make its predictions, while still matching their accuracy. Furthermore, analysis into ABCNet’s latent space hints at important genomic features indicative of its 3D genome organization. en_US
dc.language.iso en en_US
dc.publisher University of Guelph en_US
dc.subject CNN en_US
dc.subject Chromosome Conformation Capture en_US
dc.subject Convolutional Neural Network en_US
dc.subject Machine Learning en_US
dc.subject AB Compartments en_US
dc.subject Biology en_US
dc.subject DNA sequence en_US
dc.subject ABCNet en_US
dc.subject Hi-C en_US
dc.title ABCNet: Predicting Chromosomal Compartments Directly from Reference Genomes en_US
dc.type Thesis en_US
dc.degree.programme Computer Science en_US
dc.degree.name Master of Science en_US
dc.degree.department School of Computer Science en_US
dc.rights.license All items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated.
dc.degree.grantor University of Guelph en_US


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