ArticleAmerican journal of human genetics2021
Interpretable prioritization of splice variants in diagnostic next-generation sequencing.
Article in American journal of human genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed, 67 citations in OpenAlex.
- 'Missing' disease-causing variants in Alport syndrome.Nature reviews. Nephrology · 2026Review
- Harnessing artificial intelligence for genomic variant prediction: advances, challenges, and future directions.GigaScience · 2026Review
- A practical framework for predicting splicing single nucleotide variants in exome sequencing.NAR genomics and bioinformatics · 2025Article
- Genome-wide functional annotation and interpretation of splicing variants: toward RNA-targeted therapies.Journal of human genetics · 2025Review
- Clinical utility of DNA-methylation signatures in routine diagnostics for neurodevelopmental disorders.European journal of human genetics : EJHG · 2025Article
- Predicting the impact of rare variants on RNA splicing in CAGI6.Human genetics · 2025Article
- Genomic reanalysis of a pan-European rare-disease resource yields new diagnoses.Nature medicine · 2025Article
- Rare disease genomics and precision medicine.Genomics & informatics · 2024Review
- Burden re-analysis of neurodevelopmental disorder cohorts for prioritization of candidate genes.European journal of human genetics : EJHG · 2024Article
- Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors.Human genomics · 2024Article
- Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors.bioRxiv : the preprint server for biology · 2024Article
- A Mild Presentation of X-Linked Hypophosphatemia Caused by a Non-Canonical Splice Site Variant in theGenes · 2024Article
- Genomes in clinical care.NPJ genomic medicine · 2024Review
- Improved detection of aberrant splicing with FRASER 2.0 and the intron Jaccard index.American journal of human genetics · 2023Article
- Explainable artificial intelligence for omics data: a systematic mapping study.Briefings in bioinformatics · 2023Review
- PDIVAS: Pathogenicity predictor for Deep-Intronic Variants causing Aberrant Splicing.BMC genomics · 2023Article
- A deep intronic TCTN2 variant activating a cryptic exon predicted by SpliceRover in a patient with Joubert syndrome.Journal of human genetics · 2023Article
- Resurrecting the alternative splicing landscape of archaic hominins using machine learning.Nature ecology & evolution · 2023Article
- Identification and in-silico characterization of splice-site variants from a large cardiogenetic national registry.European journal of human genetics : EJHG · 2023Article
- Improved detection of aberrant splicing using the Intron Jaccard Index.medRxiv : the preprint server for health sciences · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
10 authors at 5 institutions in 3 countries.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.