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COWID: an efficient cloud-based genomics workflow for scalable identification of SARS-COV-2.

Authors :
Lim, Hendrick Gao-Min
Fann, Yang C
Lee, Yuan-Chii Gladys
Source :
Briefings in Bioinformatics. Sep2023, Vol. 24 Issue 5, p1-12. 12p.
Publication Year :
2023

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
14675463
Volume :
24
Issue :
5
Database :
Academic Search Index
Journal :
Briefings in Bioinformatics
Publication Type :
Academic Journal
Accession number :
172331646
Full Text :
https://doi.org/10.1093/bib/bbad280