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Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

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Updated: May 20, 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

ProbML: A Machine Learning-Based Genome Classifier for Identifying Probiotic Organisms.

Arjun Orkkatteri Krishnan¹, Lalit N Mudgal¹, Vishesh Soni¹

¹School of Biosciences and Bioengineering, Indian Institute of Technology Mandi, Mandi, Himachal Pradesh, India.

Molecular Nutrition & Food Research

|March 26, 2025

Summary

This summary is machine-generated.

This study introduces ProbML, a machine learning approach for identifying probiotic microorganisms from genomic data. ProbML significantly improves accuracy and speed compared to traditional methods, accelerating the discovery of beneficial microbes.

Keywords:

genome classification graphical user interface gut microbiota machine learning probiotics

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Last Updated: May 20, 2025

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Published on: October 15, 2019

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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Area of Science:

Microbiology
Bioinformatics
Computational Biology

Background:

Traditional probiotic identification is slow and costly.
Need for efficient and accurate methods using genomic data.

Purpose of the Study:

Develop a machine learning (ML) approach, ProbML, for rapid and accurate probiotic identification from prokaryotic whole genome sequences.
Compare ProbML performance against existing tools.

Main Methods:

Implemented and evaluated five ML algorithms on genomic data.
Utilized XGBoost models for probiotic classification.
Developed a GUI platform for ML-based probiotic classification and custom classifier generation.

Main Results:

XGBoost models achieved 100% accuracy on learning data and 95.45% on an independent test set.
ProbML outperformed existing tools (97.77% vs. 66.28% on test data).
Analyzed 4728 genomes, identifying 650 probiotics, including many new ones.

Conclusions:

ProbML offers a highly accurate and efficient method for probiotic discovery using genomic data.
The developed GUI platform enhances accessibility and customization for probiotic classification.
Genomic data combined with ML accelerates the identification of beneficial microorganisms.