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Related Concept Videos

Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

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Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
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Related Experiment Video

Updated: Dec 15, 2025

RNAscope for In situ Detection of Transcriptionally Active Human Papillomavirus in Head and Neck Squamous Cell Carcinoma
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Bioinformatics Pipeline for Human Papillomavirus Short Read Genomic Sequences Classification Using Support Vector

Alexandre Lomsadze1, Tengguo Li2, Mangalathu S Rajeevan2

  • 1Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech, Atlanta, GA 30332, USA.

Viruses
|July 8, 2020
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Summary

A new machine learning algorithm accurately identifies human papillomaviruses (HPV) using enriched whole genome sequencing (eWGS) data. This bioinformatics pipeline offers a reliable method for HPV typing in clinical and epidemiological samples.

Keywords:
HPV typingHPV whole genome sequencingbioinformatics pipelineh classificationtarget enrichment

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Area of Science:

  • Genomics
  • Bioinformatics
  • Virology

Background:

  • Human papillomavirus (HPV) infections are a significant global health concern.
  • Accurate and comprehensive HPV typing is crucial for diagnosis, treatment, and epidemiological studies.
  • Existing HPV detection methods may have limitations in detecting all HPV types comprehensively.

Purpose of the Study:

  • To develop and validate a machine learning algorithm for HPV type detection.
  • To assess the performance of the algorithm using enriched whole genome sequencing (eWGS) data.
  • To compare the algorithm's HPV typing results with a standard method.

Main Methods:

  • Development of a machine learning algorithm (Support Vector Machine - SVM) trained on eWGS data.
  • Utilizing the Agilent SureSelect target enrichment system for capturing 191 HPV types.
  • Validation using control samples with known HPV types and comparison with HPV Linear Array (LA) assay on epidemiological samples.

Main Results:

  • The machine learning algorithm demonstrated good performance in detecting HPV types, especially with ≥25 HPV plasmid copies per sample.
  • Substantial agreement (97.4% concordance, kappa=0.783) was observed when compared to the standard HPV Linear Array (LA) assay.
  • The algorithm identified an additional 428 HPV types not detectable by the LA assay, highlighting its enhanced detection capability.

Conclusions:

  • The developed bioinformatics pipeline is an accurate tool for HPV type calling using eWGS data.
  • The eWGS approach combined with machine learning offers a sensitive and comprehensive method for HPV detection.
  • This technology has the potential to improve HPV diagnostics and epidemiological surveillance.