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Inference of the Human Polyadenylation Code

Michael K. K. Leung, Andrew Delong, Brendan J. Frey
doi: https://doi.org/10.1101/130591
Michael K. K. Leung
aDepartment of Electrical and Computer Engineering, University of Toronto, Toronto, M5S 3G4, Canada
bDeep Genomics, MaRS Centre, Heritage Building, Suite 320, Toronto, ON, M5G 1L7, Canada
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Andrew Delong
aDepartment of Electrical and Computer Engineering, University of Toronto, Toronto, M5S 3G4, Canada
bDeep Genomics, MaRS Centre, Heritage Building, Suite 320, Toronto, ON, M5G 1L7, Canada
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Brendan J. Frey
aDepartment of Electrical and Computer Engineering, University of Toronto, Toronto, M5S 3G4, Canada
bDeep Genomics, MaRS Centre, Heritage Building, Suite 320, Toronto, ON, M5G 1L7, Canada
cBanting and Best Department of Medical Research, University of Toronto, Toronto, M5S 3E1, Canada
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Abstract

Processing of transcripts at the 3’-end involves cleavage at a polyadenylation site followed by the addition of a poly(A)-tail. By selecting which polyadenylation site is cleaved, alternative polyadenylation enables genes to produce transcript isoforms with different 3’-ends. To facilitate the identification and treatment of disease-causing mutations that affect polyadenylation and to understand the underlying regulatory processes, a computational model that can accurately predict polyadenylation patterns based on genomic features is desirable. Previous works have focused on identifying candidate polyadenylation sites and classifying sites which may be tissue-specific. What is lacking is a predictive model of the underlying mechanism of site selection, competition, and processing efficiency in a tissue-specific manner. We develop a deep learning model that trains on 3’-end sequencing data and predicts tissue-specific site selection among competing polyadenylation sites in the 3’ untranslated region of the human genome.

Two neural network architectures are evaluated: one built on hand-engineered features, and another that directly learns from the genomic sequence. The hand-engineered features include polyadenylation signals, cis-regulatory elements, n-mer counts, nucleosome occupancy, and RNA-binding protein motifs. The direct-from-sequence model is inferred without prior knowledge on polyadenylation, based on a convolutional neural network trained with genomic sequences surrounding each polyadenylation site as input. Both models are trained using the TensorFlow library.

The proposed polyadenylation code can predict site selection among competing polyadenylation sites in different tissues. Importantly, it does so without relying on evolutionary conservation. The model can distinguish pathogenic from benign variants that appear near annotated polyadenylation sites in ClinVar and inspect the genome to find candidate polyadenylation sites. We also provide an analysis on how different features affect the model’s performance.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted April 27, 2017.
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Inference of the Human Polyadenylation Code
Michael K. K. Leung, Andrew Delong, Brendan J. Frey
bioRxiv 130591; doi: https://doi.org/10.1101/130591
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Inference of the Human Polyadenylation Code
Michael K. K. Leung, Andrew Delong, Brendan J. Frey
bioRxiv 130591; doi: https://doi.org/10.1101/130591

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