PT  - JOURNAL ARTICLE
AU  - Danis, Daniel
AU  - Jacobsen, Julius O.B.
AU  - Carmody, Leigh
AU  - Gargano, Michael
AU  - McMurry, Julie A
AU  - Hegde, Ayushi
AU  - Haendel, Melissa A
AU  - Valentini, Giorgio
AU  - Smedley, Damian
AU  - Robinson, Peter N
TI  - Interpretable prioritization of splice variants in diagnostic next-generation sequencing
AID  - 10.1101/2021.01.28.428499
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.01.28.428499
4099  - http://biorxiv.org/content/early/2021/01/28/2021.01.28.428499.short
4100  - http://biorxiv.org/content/early/2021/01/28/2021.01.28.428499.full
AB  - A critical challenge in genetic diagnostics is the computational assessment of candidate splice variants, specifically the interpretation of nucleotide changes located outside of the highly conserved dinucleotide sequences at the 5′ and 3′ ends of introns. To address this gap, we developed the Super Quick Informationcontent Random-forest Learning of Splice variants (SQUIRLS) algorithm. SQUIRLS generates a small set of interpretable features for machine learning by calculating the information-content (IC) of wildtype and variant sequences of canonical and cryptic splice sites, assessing changes in candidate splicing regulatory sequences, and incorporating characteristics of the sequence such as exon length, disruptions of the AG exclusion zone, and conservation. We curated a comprehensive collection of disease-associated splicealtering variants at positions outside of the highly conserved AG/GT dinucleotides at the termini of introns. SQUIRLS trains two random-forest classifiers for the donor and for the acceptor and combines their outputs by logistic regression to yield a final score. We show that SQUIRLS transcends previous state of the art accuracy in classifying splice variants as assessed by rank analysis in simulated exomes and is significantly faster than competing methods. SQUIRLS provides tabular output files for incorporation into diagnostic pipelines for exome and genome analysis, as well as visualizations that contextualize predicted effects of variants on splicing to make it easier to interpret splice variants in diagnostic settingsCompeting Interest StatementThe authors have declared no competing interest.