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COWID: an efficient cloud-based genomics workflow for scalable identification of SARS-COV-2.
Lim, Hendrick Gao-Min; Fann, Yang C; Lee, Yuan-Chii Gladys.
Affiliation
  • Lim HG; Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan 11031.
  • Fann YC; Department of Medical Research, Tzu Chi Hospital Indonesia, Pantai Indah Kapuk, Greater Jakarta, Indonesia 14470.
  • Lee YG; IT and Bioinformatics Program, Division of Intramural, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA 20892.
Brief Bioinform ; 24(5)2023 09 20.
Article in En | MEDLINE | ID: mdl-37738400
ABSTRACT
Implementing a specific cloud resource to analyze extensive genomic data on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a challenge when resources are limited. To overcome this, we repurposed a cloud platform initially designed for use in research on cancer genomics (https//cgc.sbgenomics.com) to enable its use in research on SARS-CoV-2 to build Cloud Workflow for Viral and Variant Identification (COWID). COWID is a workflow based on the Common Workflow Language that realizes the full potential of sequencing technology for use in reliable SARS-CoV-2 identification and leverages cloud computing to achieve efficient parallelization. COWID outperformed other contemporary methods for identification by offering scalable identification and reliable variant findings with no false-positive results. COWID typically processed each sample of raw sequencing data within 5 min at a cost of only US$0.01. The COWID source code is publicly available (https//github.com/hendrick0403/COWID) and can be accessed on any computer with Internet access. COWID is designed to be user-friendly; it can be implemented without prior programming knowledge. Therefore, COWID is a time-efficient tool that can be used during a pandemic.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: COVID-19 Type of study: Diagnostic_studies / Prognostic_studies Limits: Humans Language: En Journal: Brief Bioinform Journal subject: BIOLOGIA / INFORMATICA MEDICA Year: 2023 Type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: COVID-19 Type of study: Diagnostic_studies / Prognostic_studies Limits: Humans Language: En Journal: Brief Bioinform Journal subject: BIOLOGIA / INFORMATICA MEDICA Year: 2023 Type: Article