Main content

ABCNet: Predicting Chromosomal Compartments Directly from Reference Genomes

Show simple item record

dc.contributor.advisor Kremer, Stefan Kirchhof, Matthew 2021-04-22T19:14:41Z 2021-04-22T19:14:41Z 2021-04 2021-04-12
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 Computer Science en_US Master of Science en_US 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. University of Guelph en_US

Files in this item

Files Size Format View
Kirchhof_Matthew_202104_MSc.pdf 4.432Mb PDF View/Open

This item appears in the following Collection(s)

Show simple item record

The library is committed to ensuring that members of our user community with disabilities have equal access to our services and resources and that their dignity and independence is always respected. If you encounter a barrier and/or need an alternate format, please fill out our Library Print and Multimedia Alternate-Format Request Form. Contact us if you’d like to provide feedback:  (email address)