Next-generation sequencing (NGS) is increasingly used in infectious disease diagnostics, with three main approaches: whole-genome sequencing (WGS), targeted NGS (tNGS), and metagenomic NGS (mNGS). WGS provides detailed genomic information for pathogen identification and antimicrobial resistance (AMR) detection, supporting outbreak investigations and guiding patient management. tNGS targets specific genes, such as 16S rRNA, for microbial identification and can assess thousands of genes in parallel. mNGS sequences all nucleic acids in a sample, enabling detection of any pathogen without prior knowledge, making it useful for rare or unusual pathogens. However, mNGS faces challenges such as contamination, false positives, and limitations in detecting pathogens in certain clinical contexts, like neuroinvasive viruses where the window of detection in cerebrospinal fluid has passed. mNGS also has lower sensitivity for detecting AMR genes compared to tNGS, and accurate detection requires comprehensive genomic databases. Despite these challenges, mNGS is valuable for identifying pathogens in samples where traditional methods fail, and its use is expanding to various sample types. tNGS is more sensitive for AMR detection but is limited by the specific targets included in the assay. Both approaches require high-quality databases and careful interpretation to avoid false results. As NGS technology becomes more automated and cost-effective, its role in clinical microbiology is growing, with potential to improve patient care and reduce healthcare costs. However, the clinical utility of NGS-based diagnostics depends on proper implementation, interpretation, and integration into clinical workflows.Next-generation sequencing (NGS) is increasingly used in infectious disease diagnostics, with three main approaches: whole-genome sequencing (WGS), targeted NGS (tNGS), and metagenomic NGS (mNGS). WGS provides detailed genomic information for pathogen identification and antimicrobial resistance (AMR) detection, supporting outbreak investigations and guiding patient management. tNGS targets specific genes, such as 16S rRNA, for microbial identification and can assess thousands of genes in parallel. mNGS sequences all nucleic acids in a sample, enabling detection of any pathogen without prior knowledge, making it useful for rare or unusual pathogens. However, mNGS faces challenges such as contamination, false positives, and limitations in detecting pathogens in certain clinical contexts, like neuroinvasive viruses where the window of detection in cerebrospinal fluid has passed. mNGS also has lower sensitivity for detecting AMR genes compared to tNGS, and accurate detection requires comprehensive genomic databases. Despite these challenges, mNGS is valuable for identifying pathogens in samples where traditional methods fail, and its use is expanding to various sample types. tNGS is more sensitive for AMR detection but is limited by the specific targets included in the assay. Both approaches require high-quality databases and careful interpretation to avoid false results. As NGS technology becomes more automated and cost-effective, its role in clinical microbiology is growing, with potential to improve patient care and reduce healthcare costs. However, the clinical utility of NGS-based diagnostics depends on proper implementation, interpretation, and integration into clinical workflows.