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Updated: Nov 24, 2025

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

PStrain: an iterative microbial strains profiling algorithm for shotgun metagenomic sequencing data.

Shuai Wang¹, Yiqi Jiang¹, Shuaicheng Li¹

¹Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong.

Bioinformatics (Oxford, England)

|December 21, 2020

Summary

This summary is machine-generated.

PStrain enhances microbial strain profiling by analyzing genotype frequencies and read data. This method significantly improves strain abundance and genotype inference, revealing differences in Bacteroides coprocola in colorectal cancer patients.

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Last Updated: Nov 24, 2025

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

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Published on: October 15, 2019

Exploring the Root Microbiome: Extracting Bacterial Community Data from the Soil, Rhizosphere, and Root Endosphere

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Purifying the Impure: Sequencing Metagenomes and Metatranscriptomes from Complex Animal-associated Samples

Purifying the Impure: Sequencing Metagenomes and Metatranscriptomes from Complex Animal-associated Samples

Published on: December 22, 2014

Area of Science:

Microbiology
Genomics
Bioinformatics

Background:

Microbial communities are crucial for human health and disease.
Strain-level genomic variations influence microbial functions.
Shotgun metagenomic sequencing is vital for microbial community analysis, but strain-level resolution is challenging due to sequence similarity.

Purpose of the Study:

To develop an advanced computational method for accurate microbial strain profiling.
To leverage single nucleotide variant (SNV) data and read information for improved strain resolution.
To investigate microbial strain differences in colorectal cancer (CRC) using the developed method.

Main Methods:

PStrain utilizes genotype frequencies and read data covering multiple SNVs within MetaPhlAn2 marker genes.
It employs an iterative optimization approach for strain profiling.
The method was validated against existing state-of-the-art techniques.

Main Results:

PStrain significantly improved the inference of strain abundances by 87.75% and genotypes by 59.45% on average.
Application to CRC cohorts revealed significant differences in Bacteroides coprocola strains between cancer and control samples.
This is the first report implicating B. coprocola strain variations in CRC gut microbiota.

Conclusions:

PStrain offers a substantial advancement in microbial strain profiling accuracy.
The findings highlight the potential role of specific B. coprocola strains in colorectal cancer.
Further research into strain-level microbial variations in disease is warranted.