Interpretable prioritization of splice variants in diagnostic next-generation sequencing.

Authors

Daniel Danis, The Jackson LaboratoryFollow
Julius O B Jacobsen
Leigh Carmody, The Jackson LaboratoryFollow
Michael Gargano, The Jackson LaboratoryFollow
Julie A McMurry
Ayushi Hegde, The Jackson LaboratoryFollow
Melissa A Haendel
Giorgio Valentini
Damian Smedley
Peter N Robinson, The Jackson LaboratoryFollow

Document Type

Article

Publication Date

9-2-2021

Publication Title

American journal of human genetics

Keywords

JGM, Algorithms, Base Sequence, Computational Biology, Data Curation, Exome, Exons, Genetic Diseases, Inborn, High-Throughput Nucleotide Sequencing, Humans, Introns, Mutation, RNA Splice Sites, RNA Splicing, Software, Whole Exome Sequencing

JAX Source

Am J Hum Genet 2021 Sep 2; 108(9):1564-1577

Volume

108

Issue

9

First Page

1564

Last Page

1577

ISSN

1537-6605

PMID

34289339

DOI

https://doi.org/10.1016/j.ajhg.2021.06.014

Abstract

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 Information-content Random-forest Learning of Splice variants (SQUIRLS) algorithm. SQUIRLS generates a small set of interpretable features for machine learning by calculating the information-content of wild-type 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 splice-altering 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 settings.

Recommended Citation

Danis D, Jacobsen J, Carmody L, Gargano M, McMurry J, Hegde A, Haendel M, Valentini G, Smedley D, Robinson P. Interpretable prioritization of splice variants in diagnostic next-generation sequencing. Am J Hum Genet 2021 Sep 2; 108(9):1564-1577