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Related Experiment Video

Updated: Jun 9, 2025

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

Advancing microbial diagnostics: a universal phylogeny guided computational algorithm to find unique sequences for

Gulshan Kumar Sharma¹, Rakesh Sharma², Kavita Joshi³

¹Malaviya National Institute of Technology, Jawahar Lal Nehru Marg, Jhalana Gram, Malviya Nagar, Jaipur, Rajasthan 302017, India.

Briefings in Bioinformatics

|October 23, 2024

Summary

We developed NAUniSeq, a novel algorithm for identifying unique microbial DNA sequences for diagnostics. This method efficiently finds specific markers, improving microorganism identification accuracy.

Keywords:

depth first search hash map k-mer phylogenetic tree taxonomy unique sequences

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Last Updated: Jun 9, 2025

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

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Published on: July 11, 2016

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

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Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies

Published on: August 20, 2021

Area of Science:

Bioinformatics
Genomics
Microbiology

Background:

Organismal sequence similarity can complicate diagnostic marker identification.
Identifying unique sequences in closely related organisms is computationally challenging.

Purpose of the Study:

To develop a universal algorithm, NAUniSeq, for identifying unique DNA sequences for microorganism diagnosis.
To improve the accuracy and efficiency of microbial strain identification.

Main Methods:

Developed a biologically inspired algorithm, NAUniSeq, utilizing phylogenetic trees.
Employed an alignment-free, k-mer-based approach for sequence comparison.
Implemented both graph-based and NoSQL (MongoDB) methods for sequence analysis.

Main Results:

Successfully generated unique diagnostic sequences for target microorganisms like Mycobacterium tuberculosis, Neisseria gonorrhoeae, and Monkeypox.
Demonstrated low cross-contamination and false positive rates through phylogenetic mapping.
Validated the algorithm's effectiveness in identifying microbial strains with high phylogenetic precision.

Conclusions:

NAUniSeq is a powerful, universal tool for generating accurate microbial diagnostic sequences.
The algorithm offers high phylogenetic precision, enhancing microorganism identification capabilities.