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On the Relation of Gene Essentiality to Intron Structure: A Computational and Deep Learning Approach

Ethan Schonfeld, Edward Vendrow, Joshua Vendrow, Elan Schonfeld
doi: https://doi.org/10.1101/2020.03.31.019125
Ethan Schonfeld
1Stanford University, Stanford, CA, USA
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  • For correspondence: eschon22@stanford.edu
Edward Vendrow
1Stanford University, Stanford, CA, USA
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Joshua Vendrow
2University of California, Los Angeles, Los Angeles, CA, USA
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Elan Schonfeld
3Glenbrook North High School, Northbrook, IL, USA
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Abstract

Identification and study of human-essential genes has become of practical importance with the realization that disruption or loss of nearby essential genes can introduce latent-vulnerabilities to cancer cells. Essential genes have been studied by copy-number-variants and deletion events, which are associated with introns. The premise of our work is that introns of essential genes have characteristic properties that are distinct from the introns of nonessential genes. We provide support for the existence of characteristic properties by training a deep learning model on introns of essential and nonessential genes and demonstrated that introns alone can be used to classify essential and nonessential genes with high accuracy (AUC of 0.846). We further demonstrated that the accuracy of the same deep-learning model limited to first introns will perform at an increased level, thereby demonstrating the critical importance of introns and particularly first introns in gene essentiality. Using a computational approach, we identified several novel properties of introns of essential genes, finding that their structure protects against deletion and intron-loss events, and that these traits are especially centered on the first intron. We showed that GC density is increased in the first introns of essential genes, allowing for increased enhancer activity, protection against deletions, and improved splice-site recognition. Furthermore, we found that first introns of essential genes are of remarkably smaller size than their nonessential counterparts, and to protect against common 3’ end deletion events, essential genes carry an increased number of (smaller) introns. To demonstrate the importance of the seven features we identified, we trained a feature–based model using only information from these features and achieved high accuracy (AUC of 0.787).

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Having identified the above features that differentiate introns of essential genes from introns of nonessential genes, we trained a feature-driven deep-learning model to predict gene essentiality so as to determine the importance of the identified features. By only training the model with information on seven features we identified [Average intron size, Number of introns in gene, Intronic bp in gene, GC density (first intron), GC density (later introns), GC count (not including GC motifs) (first intron), GC count (not including GC motifs) (later introns)], we can determine the importance of these identified features in essentiality.

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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 May 19, 2020.
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On the Relation of Gene Essentiality to Intron Structure: A Computational and Deep Learning Approach
Ethan Schonfeld, Edward Vendrow, Joshua Vendrow, Elan Schonfeld
bioRxiv 2020.03.31.019125; doi: https://doi.org/10.1101/2020.03.31.019125
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On the Relation of Gene Essentiality to Intron Structure: A Computational and Deep Learning Approach
Ethan Schonfeld, Edward Vendrow, Joshua Vendrow, Elan Schonfeld
bioRxiv 2020.03.31.019125; doi: https://doi.org/10.1101/2020.03.31.019125

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