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PfaSTer: a machine learning-powered serotype caller for

Jonathan T Lee1, Xingpeng Li2, Craig Hyde2

  • 1Vaccine Research & Development, Pfizer Inc., 401 N. Middletown Rd, Pearl River, NY 10965, USA.

Microbial Genomics
|June 6, 2023
PubMed
Summary

PfaSTer is a novel machine learning tool that accurately identifies Streptococcus pneumoniae serotypes from whole genome sequences. This method offers a reliable and accessible approach for global disease surveillance without requiring high-coverage sequencing data.

Keywords:
Streptococcus pneumoniaemachine learningserotype

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Area of Science:

  • Microbiology
  • Genomics
  • Bioinformatics

Background:

  • Streptococcus pneumoniae causes significant global morbidity and mortality.
  • Pneumococcal vaccines have led to shifts in serotype prevalence, necessitating ongoing surveillance.
  • Whole genome sequencing (WGS) is a valuable tool for tracking pneumococcal serotypes via the capsular polysaccharide biosynthetic operon (cps).

Purpose of the Study:

  • To develop a machine learning-based method for rapid and accurate serotype prediction from assembled S. pneumoniae genome sequences.
  • To overcome limitations of existing WGS-based serotyping tools that require high-coverage sequencing data.
  • To provide a robust and accessible tool for global pneumococcal surveillance.

Main Methods:

  • PfaSTer utilizes k-mer analysis for dimensionality reduction combined with a Random Forest classifier.
  • The method predicts 65 prevalent S. pneumoniae serotypes from assembled genome sequences.
  • Confidence in predictions is determined using the model's statistical framework, eliminating the need for coverage assessments.

Main Results:

  • PfaSTer accurately identifies 65 prevalent pneumococcal serotypes from assembled WGS data.
  • The method demonstrates high concordance (>97%) when compared to biochemical testing and other in silico serotyping tools.
  • PfaSTer provides prediction confidence without requiring high-coverage sequencing reads, enhancing accessibility.

Conclusions:

  • PfaSTer is a robust, open-source machine learning tool for rapid in silico serotyping of Streptococcus pneumoniae.
  • This method facilitates global surveillance by overcoming accessibility and data-sharing challenges associated with high-coverage sequencing requirements.
  • PfaSTer enables reliable monitoring of pneumococcal serotype distribution crucial for public health.