Robust Mutation Profiling of SARS‐CoV‐2 Variants from Multiple Raw Illumina Sequencing Data with Cloud Workflow

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

研究成果: 雜誌貢獻文章同行評審

摘要

Several variants of the novel severe acute respiratory syndrome coronavirus 2 (SARS‐ CoV‐2) are emerging all over the world. Variant surveillance from genome sequencing has become crucial to determine if mutations in these variants are rendering the virus more infectious, potent, or resistant to existing vaccines and therapeutics. Meanwhile, analyzing many raw sequencing data repeatedly with currently available code‐based bioinformatics tools is tremendously challenging to be implemented in this unprecedented pandemic time due to the fact of limited experts and computational resources. Therefore, in order to hasten variant surveillance efforts, we developed an installation‐free cloud workflow for robust mutation profiling of SARS‐CoV‐2 variants from multiple Illumina sequencing data. Herein, 55 raw sequencing data representing four early SARS‐CoV‐2 variants of concern (Alpha, Beta, Gamma, and Delta) from an open‐access database were used to test our workflow performance. As a result, our workflow could automatically identify mutated sites of the variants along with reliable annotation of the protein‐coding genes at cost‐effective and timely manner for all by harnessing parallel cloud computing in one execution under resource‐limitation settings. In addition, our workflow can also generate a consensus genome sequence which can be shared with others in public data repositories to support global variant surveillance efforts.
原文英語
文章編號686
期刊Genes
13
發行號4
DOIs
出版狀態已發佈 - 4月 2022

ASJC Scopus subject areas

  • 遺傳學
  • 遺傳學(臨床)

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