ABSTRACT
Although sequencing a human genome has become affordable, identifying genetic variants from whole-genome sequence data is still a hurdle for researchers without adequate computing equipment or bioinformatics support. GATK is a gold standard method for the identification of genetic variants and has been widely used in genome projects and population genetic studies for many years. This was until the Google Brain team developed a new method, DeepVariant, which utilizes deep neural networks to construct an image classification model to identify genetic variants. However, the superior accuracy of DeepVariant comes at the cost of computational intensity, largely constraining its applications. Accordingly, we present DeepVariant-on-Spark to optimize resource allocation, enable multi-GPU support, and accelerate the processing of the DeepVariant pipeline. To make DeepVariant-on-Spark more accessible to everyone, we have deployed the DeepVariant-on-Spark to the Google Cloud Platform (GCP). Users can deploy DeepVariant-on-Spark on the GCP following our instruction within 20 minutes and start to analyze at least ten whole-genome sequencing datasets using free credits provided by the GCP. DeepVaraint-on-Spark is freely available for small-scale genome analysis using a cloud-based computing framework, which is suitable for pilot testing or preliminary study, while reserving the flexibility and scalability for large-scale sequencing projects.
Subject(s)
Cloud Computing , Deep Learning , Genetic Variation , Whole Genome Sequencing/statistics & numerical data , Cloud Computing/economics , Computational Biology/methods , Cost-Benefit Analysis , Genome, Human , High-Throughput Nucleotide Sequencing/economics , High-Throughput Nucleotide Sequencing/standards , High-Throughput Nucleotide Sequencing/statistics & numerical data , Humans , Neural Networks, Computer , Software , Whole Genome Sequencing/economics , Whole Genome Sequencing/standardsABSTRACT
The Japanese encephalitis virus (JEV) is the major cause of an acute encephalitis syndrome in many Asian countries, despite the fact that an effective vaccine has been developed. Virus-like particles (VLPs) are self-assembled multi-subunit protein structures which possess specific epitope antigenicities related to corresponding native viruses. These properties mean that VLPs are considered safe antigens that can be used in clinical applications. In this study, we developed a novel baculovirus/mosquito (BacMos) expression system which potentially enables the scalable production of JEV genotype III (GIII) VLPs (which are secreted from mosquito cells). The mosquito-cell-derived JEV VLPs comprised 30-nm spherical particles as well as precursor membrane protein (prM) and envelope (E) proteins with densities that ranged from 30% to 55% across a sucrose gradient. We used IgM antibody-capture enzyme-linked immunosorbent assays to assess the resemblance between VLPs and authentic virions and thereby characterized the epitope specific antigenicity of VLPs. VLP immunization was found to elicit a specific immune response toward a balanced IgG2a/IgG1 ratio. This response effectively neutralized both JEV GI and GIII and elicited a mixed Th1/Th2 response in mice. This study supports the development of mosquito cell-derived JEV VLPs to serve as candidate vaccines against JEV.
