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PHDtools: A platform for pathogen detection and multi-dimensional genetic signatures decoding to realize pathogen

Dongyan Xiong1, Xiaoxu Zhang2, Bohan Xu3

  • 1CAS Key Laboratory of Special Pathogens and Biosafety, Center for Biosafety Mega-Science, Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan 430071, China; University of Chinese Academy of Sciences, Beijing 100049, China; Centre for Novostics, Hong Kong Science Park, Pak Shek Kok, New Territories, Hong Kong SAR, China; Department of Chemical Pathology, Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China.

Gene
|February 26, 2024
PubMed
Summary

A new platform, PHDtools, aids clinical labs in analyzing pathogen genomics for emerging diseases. It identified co-infections and Omicron variant evolution, guiding antiviral and vaccine development.

Keywords:
GenomicsMulti-dimensionalNGSPathogen detectionVariation identification

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Area of Science:

  • Genomics and Bioinformatics
  • Infectious Disease Surveillance
  • Viral Evolution

Background:

  • Emerging infectious diseases necessitate rapid pathogen identification and genomic characterization.
  • High-throughput sequencing generates complex data requiring specialized bioinformatics tools.
  • Clinical laboratories often lack the bioinformatics expertise to analyze large genomic datasets.

Purpose of the Study:

  • To develop an accessible, user-friendly online platform (PHDtools) for analyzing metagenomic data.
  • To enable pathogen identification and multi-dimensional genomic signature analysis, including intra-/inter-host and lineage variations.
  • To support clinical laboratories in decoding complex genomic big data for emerging disease management.

Main Methods:

  • Development of an interactive online platform, PHDtools, with 15 distinct analytical functions.
  • Utilized metagenomic next-generation sequencing (mNGS) data analysis.
  • Applied PHDtools to analyze genomic data from 172 imported COVID-19 cases.

Main Results:

  • Identified co-infections in 27 patients: SARS-CoV-2 with influenza virus (n=9) or human picobirnavirus (n=19).
  • Determined Omicron sub-lineages and observed increased mutations in non-structural and M genes.
  • Detected positive selection (Ka/Ks > 1) in intra-host variations of E and M genes, suggesting adaptive evolution.

Conclusions:

  • PHDtools is accurate, user-friendly, and convenient for clinical users with limited bioinformatics background.
  • Clinical monitoring using PHDtools revealed potential evolution features of SARS-CoV-2.
  • Development of antiviral agents and vaccines should consider gene variations beyond the S gene.