DeepVariant-on-Spark: Small-Scale Genome Analysis Using a Cloud-Based Computing Framework

Autor: Hou-Hsien Lin, Po-Jung Huang, Ming-Tai Chang, Jui-Huan Chang, Chi-Ching Lee, Cheng-Hsun Chiu, Yu-Xuan Li, Sid Weng, Petrus Tang, Yun-Lung Li, Wei-Hung Cheng, Chung-Tsai Su
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Article Subject
Computer science
Cost-Benefit Analysis
Population
Computer applications to medicine. Medical informatics
R858-859.7
Cloud computing
General Biochemistry
Genetics and Molecular Biology
03 medical and health sciences
Deep Learning
Spark (mathematics)
Resource allocation (computer)
Humans
education
030304 developmental biology
0303 health sciences
education.field_of_study
General Immunology and Microbiology
Whole Genome Sequencing
business.industry
Genome
Human
Applied Mathematics
Deep learning
030305 genetics & heredity
Computational Biology
Genetic Variation
High-Throughput Nucleotide Sequencing
General Medicine
Genome project
Cloud Computing
Data science
Pipeline (software)
Modeling and Simulation
Scalability
Artificial intelligence
Neural Networks
Computer
business
Software
Research Article
Zdroj: Computational and Mathematical Methods in Medicine, Vol 2020 (2020)
Computational and Mathematical Methods in Medicine
ISSN: 1748-670X
DOI: 10.1155/2020/7231205
Popis: 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.
Databáze: OpenAIRE
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