Asunto(s)
Cuidados Críticos , Secuenciación de Nanoporos/métodos , Trastornos del Neurodesarrollo/diagnóstico , Adolescente , Preescolar , Femenino , Humanos , Lactante , Recién Nacido , Masculino , Persona de Mediana Edad , Mutación , Secuenciación de Nanoporos/economía , Trastornos del Neurodesarrollo/genética , Análisis de Secuencia de ADN/métodos , Estado Epiléptico/genéticaRESUMEN
Long-read sequencing technology has enabled variant detection in difficult-to-map regions of the genome and enabled rapid genetic diagnosis in clinical settings. Rapidly evolving third-generation sequencing platforms like Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) are introducing newer platforms and data types. It has been demonstrated that variant calling methods based on deep neural networks can use local haplotyping information with long-reads to improve the genotyping accuracy. However, using local haplotype information creates an overhead as variant calling needs to be performed multiple times which ultimately makes it difficult to extend to new data types and platforms as they get introduced. In this work, we have developed a local haplotype approximate method that enables state-of-the-art variant calling performance with multiple sequencing platforms including PacBio Revio system, ONT R10.4 simplex and duplex data. This addition of local haplotype approximation simplifies long-read variant calling with DeepVariant.
Asunto(s)
Haplotipos , Secuenciación de Nucleótidos de Alto Rendimiento , Haplotipos/genética , Humanos , Secuenciación de Nucleótidos de Alto Rendimiento/métodos , Análisis de Secuencia de ADN/métodos , Polimorfismo de Nucleótido Simple , Genoma Humano , Algoritmos , Variación Genética , Redes Neurales de la ComputaciónRESUMEN
Long-read sequencing technology has enabled variant detection in difficult-to-map regions of the genome and enabled rapid genetic diagnosis in clinical settings. Rapidly evolving third-generation sequencing platforms like Pacific Biosciences (PacBio) and Oxford nanopore technologies (ONT) are introducing newer platforms and data types. It has been demonstrated that variant calling methods based on deep neural networks can use local haplotyping information with long-reads to improve the genotyping accuracy. However, using local haplotype information creates an overhead as variant calling needs to be performed multiple times which ultimately makes it difficult to extend to new data types and platforms as they get introduced. In this work, we have developed a local haplotype approximate method that enables state-of-the-art variant calling performance with multiple sequencing platforms including PacBio Revio system, ONT R10.4 simplex and duplex data. This addition of local haplotype approximation makes DeepVariant a universal variant calling solution for long-read sequencing platforms.
RESUMEN
Whole-genome sequencing (WGS) can identify variants that cause genetic disease, but the time required for sequencing and analysis has been a barrier to its use in acutely ill patients. In the present study, we develop an approach for ultra-rapid nanopore WGS that combines an optimized sample preparation protocol, distributing sequencing over 48 flow cells, near real-time base calling and alignment, accelerated variant calling and fast variant filtration for efficient manual review. Application to two example clinical cases identified a candidate variant in <8 h from sample preparation to variant identification. We show that this framework provides accurate variant calls and efficient prioritization, and accelerates diagnostic clinical genome sequencing twofold compared with previous approaches.