DeepVariant-on-Spark: Small-Scale Genome Analysis Using a Cloud-Based Computing Framework
المؤلفون المشاركون
Huang, Po-Jung
Chang, Jui-Huan
Lin, Hou-Hsien
Li, Yu-Xuan
Lee, Chi-Ching
Su, Chung-Tsai
Li, Yun-Lung
Chang, Ming-Tai
Weng, Sid
Cheng, Wei-Hung
Chiu, Cheng-Hsun
Tang, Petrus
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-7، 7ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-09-01
دولة النشر
مصر
عدد الصفحات
7
التخصصات الرئيسية
الملخص EN
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.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Huang, Po-Jung& Chang, Jui-Huan& Lin, Hou-Hsien& Li, Yu-Xuan& Lee, Chi-Ching& Su, Chung-Tsai…[et al.]. 2020. DeepVariant-on-Spark: Small-Scale Genome Analysis Using a Cloud-Based Computing Framework. Computational and Mathematical Methods in Medicine،Vol. 2020, no. 2020, pp.1-7.
https://search.emarefa.net/detail/BIM-1139555
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Huang, Po-Jung…[et al.]. DeepVariant-on-Spark: Small-Scale Genome Analysis Using a Cloud-Based Computing Framework. Computational and Mathematical Methods in Medicine No. 2020 (2020), pp.1-7.
https://search.emarefa.net/detail/BIM-1139555
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Huang, Po-Jung& Chang, Jui-Huan& Lin, Hou-Hsien& Li, Yu-Xuan& Lee, Chi-Ching& Su, Chung-Tsai…[et al.]. DeepVariant-on-Spark: Small-Scale Genome Analysis Using a Cloud-Based Computing Framework. Computational and Mathematical Methods in Medicine. 2020. Vol. 2020, no. 2020, pp.1-7.
https://search.emarefa.net/detail/BIM-1139555
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1139555
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر