CoreProbe: A Novel Algorithm for Estimating Relative Abundance Based on Metagenomic Reads.

Area of Science:

• Microbiology
• Bioinformatics
• Computational Biology

Background:

• High-throughput sequencing generates vast metagenomic data, necessitating efficient microbial abundance estimation.
• Accurate microbial profiling is crucial for understanding microbial community composition and function.
• Existing methods' accuracy and efficiency depend on algorithms and reference sequence selection.

Purpose of the Study:

• To develop a novel approach for accurate and efficient estimation of relative microbial abundance in metagenomic samples.
• To introduce the microbial core genome as a reference sequence for improved microbial identification.
• To provide a user-friendly computational tool for metagenomic data analysis.

Main Methods:

• Utilized the microbial core genome as the reference sequence for potential microbes.
• Constructed finite mixture and latent Dirichlet models.
• Employed the Gibbs sampling algorithm for relative abundance estimation.
• Implemented the approach in the CoreProbe software package.

Main Results:

• The proposed method demonstrated improved efficiency while maintaining high accuracy.
• The approach is well-suited for high-throughput metagenomic data analysis.
• Simulation results validated the effectiveness of the core genome reference strategy.

Conclusions:

• The CoreProbe package offers an accurate and efficient pipeline for estimating microbial relative abundance.
• This method enhances the exploration of microbial communities from metagenomic data.
• The tool is freely available, promoting wider accessibility in microbial research.