RESUMEN
Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes1. Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases.
Asunto(s)
Exoma/genética , Genes Esenciales/genética , Variación Genética/genética , Genoma Humano/genética , Adulto , Encéfalo/metabolismo , Enfermedades Cardiovasculares/genética , Estudios de Cohortes , Bases de Datos Genéticas , Femenino , Predisposición Genética a la Enfermedad/genética , Estudio de Asociación del Genoma Completo , Humanos , Mutación con Pérdida de Función/genética , Masculino , Tasa de Mutación , Proproteína Convertasa 9/genética , ARN Mensajero/genética , Reproducibilidad de los Resultados , Secuenciación del Exoma , Secuenciación Completa del GenomaRESUMEN
Translating whole-exome sequencing (WES) for prospective clinical use may have an impact on the care of patients with cancer; however, multiple innovations are necessary for clinical implementation. These include rapid and robust WES of DNA derived from formalin-fixed, paraffin-embedded tumor tissue, analytical output similar to data from frozen samples and clinical interpretation of WES data for prospective use. Here, we describe a prospective clinical WES platform for archival formalin-fixed, paraffin-embedded tumor samples. The platform employs computational methods for effective clinical analysis and interpretation of WES data. When applied retrospectively to 511 exomes, the interpretative framework revealed a 'long tail' of somatic alterations in clinically important genes. Prospective application of this approach identified clinically relevant alterations in 15 out of 16 patients. In one patient, previously undetected findings guided clinical trial enrollment, leading to an objective clinical response. Overall, this methodology may inform the widespread implementation of precision cancer medicine.
Asunto(s)
Algoritmos , Exoma/genética , Neoplasias/genética , Medicina de Precisión/métodos , Análisis de Secuencia de ADN/métodos , Biología Computacional/métodos , Bases de Datos Genéticas , Células HEK293 , Humanos , Massachusetts , Mutagénesis Sitio-Dirigida , Neoplasias/patología , Medicina de Precisión/tendencias , Estadísticas no ParamétricasRESUMEN
UNLABELLED: In this paper, we review the central concepts and implementations of tools for working with network structures in Bioconductor. Interfaces to open source resources for visualization (AT&T Graphviz) and network algorithms (Boost) have been developed to support analysis of graphical structures in genomics and computational biology. AVAILABILITY: Packages graph, Rgraphviz, RBGL of Bioconductor (www.bioconductor.org).
Asunto(s)
Algoritmos , Gráficos por Computador , Regulación de la Expresión Génica/fisiología , Almacenamiento y Recuperación de la Información/métodos , Modelos Biológicos , Transducción de Señal/fisiología , Factores de Transcripción/metabolismo , Interfaz Usuario-Computador , Simulación por Computador , InternetRESUMEN
The Bioconductor project is an initiative for the collaborative creation of extensible software for computational biology and bioinformatics. The goals of the project include: fostering collaborative development and widespread use of innovative software, reducing barriers to entry into interdisciplinary scientific research, and promoting the achievement of remote reproducibility of research results. We describe details of our aims and methods, identify current challenges, compare Bioconductor to other open bioinformatics projects, and provide working examples.
