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Identification of Rare Bacterial Pathogens by 16S rRNA Gene Sequencing and MALDI-TOF MS

Identification of Rare Bacterial Pathogens by 16S rRNA Gene Sequencing and MALDI-TOF MS

Published on: July 11, 2016

Comparative analysis of machine learning algorithms on the microbial strain-specific AMP prediction.

Boris Vishnepolsky¹, Maya Grigolava¹, Grigol Managadze¹

¹Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi 0160, Georgia.

Briefings in Bioinformatics

|June 20, 2022

Summary

Developing novel antimicrobial peptide (AMP) prediction models improves microbial strain specificity. This approach enhances predictions for drug-resistant pathogens by incorporating genomic data, addressing a critical global health challenge.

Keywords:

AMP prediction antimicrobial peptides machine learning

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

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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

Area of Science:

Microbiology
Computational Biology
Biochemistry

Background:

Antimicrobial peptides (AMPs) show promise against drug-resistant microbes.
Existing AMP prediction tools lack microbial strain specificity (MSS).
Limited data hinders the development of MSS predictive models.

Purpose of the Study:

To develop improved microbial strain-specific predictive models for AMPs (MSSPM).
To enable accurate AMP predictions for microbial strains lacking experimental peptide data.

Main Methods:

Developed a novel approach integrating AMP sequence properties and target microbial genome characteristics.
Explored various feature engineering techniques and machine learning (ML) algorithms.
Compared model performance using AMP sequence features alone versus combined features.

Main Results:

Random Forest and AdaBoost algorithms demonstrated superior predictive performance.
Incorporating microbial genome characteristics significantly enhanced model performance.
The novel approach enables predictions for previously uncharacterized microbial strains.

Conclusions:

The developed MSSPM approach effectively improves AMP prediction accuracy.
Genomic data integration is crucial for enhancing MSS predictive models.
The tool is accessible via the DBAASP database for broader application.