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

Hendrick Gao Min Lim, Yang C. Fann, Yuan Chii Gladys Lee

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article numberbbad280
JournalBriefings in Bioinformatics
Volume24
Issue number5
DOIs
Publication statusPublished - Sept 1 2023

Keywords

  • cloud repurposing
  • cloud workflow
  • COVID-19
  • Illumina sequencing
  • parallel computation
  • SARS-CoV-2

ASJC Scopus subject areas

  • Information Systems
  • Molecular Biology

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