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1.
Am J Hum Genet ; 108(9): 1564-1577, 2021 09 02.
Artículo en Inglés | MEDLINE | ID: mdl-34289339

RESUMEN

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.


Asunto(s)
Algoritmos , Curaduría de Datos/métodos , Enfermedades Genéticas Congénitas/genética , Sitios de Empalme de ARN , Empalme del ARN , Programas Informáticos , Secuencia de Bases , Biología Computacional/métodos , Exoma , Exones , Enfermedades Genéticas Congénitas/diagnóstico , Enfermedades Genéticas Congénitas/patología , Secuenciación de Nucleótidos de Alto Rendimiento , Humanos , Intrones , Mutación , Secuenciación del Exoma
2.
Nucleic Acids Res ; 47(D1): D1018-D1027, 2019 01 08.
Artículo en Inglés | MEDLINE | ID: mdl-30476213

RESUMEN

The Human Phenotype Ontology (HPO)-a standardized vocabulary of phenotypic abnormalities associated with 7000+ diseases-is used by thousands of researchers, clinicians, informaticians and electronic health record systems around the world. Its detailed descriptions of clinical abnormalities and computable disease definitions have made HPO the de facto standard for deep phenotyping in the field of rare disease. The HPO's interoperability with other ontologies has enabled it to be used to improve diagnostic accuracy by incorporating model organism data. It also plays a key role in the popular Exomiser tool, which identifies potential disease-causing variants from whole-exome or whole-genome sequencing data. Since the HPO was first introduced in 2008, its users have become both more numerous and more diverse. To meet these emerging needs, the project has added new content, language translations, mappings and computational tooling, as well as integrations with external community data. The HPO continues to collaborate with clinical adopters to improve specific areas of the ontology and extend standardized disease descriptions. The newly redesigned HPO website (www.human-phenotype-ontology.org) simplifies browsing terms and exploring clinical features, diseases, and human genes.


Asunto(s)
Ontologías Biológicas , Biología Computacional/métodos , Anomalías Congénitas/genética , Predisposición Genética a la Enfermedad/genética , Bases del Conocimiento , Enfermedades Raras/genética , Anomalías Congénitas/diagnóstico , Bases de Datos Genéticas , Variación Genética , Humanos , Internet , Fenotipo , Enfermedades Raras/diagnóstico , Secuenciación Completa del Genoma/métodos
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