Biomarker identification from next-generation sequencing data for pathogen bacteria characterization and surveillance

Area of Science:

• Bioinformatics
• Genomics
• Microbiology

Background:

• Next-generation sequencing (NGS) generates vast amounts of data crucial for pathogen characterization.
• Identifying specific genes and biomarkers is essential for effective disease surveillance and control.
• Existing analytical methods may require optimization for comprehensive gene analysis.

Purpose of the Study:

• To develop a robust analytical pipeline for specific gene analysis using NGS data.
• To establish a method for biomarker discovery from genomic sequences.
• To create a bioinformatics tool for pathogen genetic diversity assessment.

Main Methods:

• The study utilized reference sequences of Salmonella enterica strains and NGS reads from various serotypes.
• Key steps included reference sequence retrieval, NGS read processing, multiple sequence alignment, and phylogenetic analysis.
• Data mining techniques were employed for biomarker discovery.

Main Results:

• An effective four-step analytical pipeline was successfully established.
• The pipeline integrates sequence retrieval, alignment, phylogenetic analysis, and data mining.
• The process facilitated the identification of potential marker sequences.

Conclusions:

• The developed pipeline serves as an effective bioinformatics tool for genetic diversity analysis.
• It enables the discovery of marker sequences crucial for pathogen characterization.
• This approach enhances pathogen surveillance capabilities through detailed genetic insights.