RT Journal Article
SR Electronic
T1 On the relation of gene essentiality to intron structure: a computational and deep learning approach
JF Life Science Alliance
JO Life Sci. Alliance
FD Life Science Alliance LLC
SP e202000951
DO 10.26508/lsa.202000951
VO 4
IS 6
A1 Ethan Schonfeld
A1 Edward Vendrow
A1 Joshua Vendrow
A1 Elan Schonfeld
YR 2021
UL https://www.life-science-alliance.org/content/4/6/e202000951.abstract
AB Essential genes have been studied by copy number variants and deletions, both associated with introns. The premise of our work is that introns of essential genes have distinct characteristic properties. We provide support for this by training a deep learning model and demonstrating that introns alone can be used to classify essentiality. The model, limited to first introns, performs at an increased level, implicating first introns in essentiality. We identify unique properties of introns of essential genes, finding that their structure protects against deletion and intron-loss events, especially centered on the first intron. We show 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. We find 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 train a feature-based model using only these features and achieve high performance.