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Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons
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Detection of Pathogenic Microbe Composition Using Next-Generation Sequencing Data.

Haiyong Zhao1,2, Shuang Wang2, Xiguo Yuan2

  • 1School of Computer Science and Technology, Liaocheng University, Liaocheng, China.

Frontiers in Genetics
|December 17, 2020
PubMed
Summary
This summary is machine-generated.

PGMicroD accurately detects pathogenic microbial composition from next-generation sequencing (NGS) data. This novel computational method improves microbial abundance estimation, even with sequencing errors and noise.

Keywords:
16S rRNAmachine learningmicrobe abundance estimationmicrobe composition detectionnext-generation sequencing

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Computational Biology

Background:

  • Next-generation sequencing (NGS) offers high-resolution data for pathogenic microbe analysis.
  • Accurate detection of microbial composition and abundance is crucial but challenged by sequence similarity and errors.
  • Existing quantification methods lack versatility for noisy samples.

Purpose of the Study:

  • To introduce PGMicroD, a novel computational method for pathogenic microbial composition detection using NGS data.
  • To enhance the accuracy of microbial abundance estimation in complex samples.
  • To provide a versatile tool for analyzing noisy NGS data.

Main Methods:

  • PGMicroD filters mistakenly mapped reads and extracts 16S rRNA sequence features.
  • A Support Vector Machine classifier predicts microbial composition.
  • Multiple-mapped reads are assigned to predicted species for abundance estimation.

Main Results:

  • PGMicroD demonstrates superior performance compared to existing methods.
  • The method's effectiveness is validated using both simulated and real sequencing data.
  • PGMicroD successfully estimates microbial abundance in challenging datasets.

Conclusions:

  • PGMicroD offers a robust and accurate solution for pathogenic microbial detection from NGS data.
  • The method effectively handles sequencing errors and noise, improving abundance estimation.
  • PGMicroD represents a significant advancement in microbial composition analysis.