Fungal Identification by NGS: Introduction, Principle, Test Methods, Test Procedure, Result-Interpretation, Clinical Significance, and Keynotes

Introduction

Fungal identification by NGS (Next-Generation Sequencing) uses advanced DNA sequencing. Moreover, it rapidly detects diverse fungal species. It enhances diagnostic accuracy. Additionally, it supports personalized treatment decisions.

Principle

NGS sequences fungal DNA directly. Consequently, it generates millions of short DNA reads. Then, bioinformatics tools match these reads to databases. Furthermore, it distinguishes closely related fungal species.

Test Methods

Researchers use targeted amplicon sequencing, and whole-genome sequencing, and focus on specific genetic markers like ITS regions. Thus, methods vary with clinical needs.

Test Procedure

Clinicians carefully collect fungal samples. Then, they extract fungal DNA using standard protocols. Next, they amplify target regions by PCR. Afterward, they prepare NGS libraries. Finally, the sequencer reads the DNA fragments.

Result Interpretation

The analysis produces sequence data. Subsequently, the software compares the data to reference databases. Thus, it identifies fungal species. Moreover, it quantifies read counts and coverage accurately.

Clinical Significance

NGS offers precise identification of fungal infections. Consequently, it assists in diagnosing rare or complex cases. Furthermore, it guides effective antifungal therapy. In addition, it may reduce patient morbidity.

Keynotes

• NGS offers high sensitivity for detecting fungal pathogens.
• Moreover, it achieves high specificity in species identification.
• It processes multiple samples simultaneously.
• Additionally, it detects rare and novel fungi.
• It sequences fungal DNA directly from clinical specimens.
• Then, it aligns short reads to comprehensive reference databases.
• Furthermore, it provides detailed species-level resolution.
• It overcomes the limitations of traditional culture-based methods.
• Consequently, it guides targeted antifungal therapy decisions.
• It demands robust bioinformatics pipelines for accurate analysis.
• In contrast, conventional methods require lengthy culture periods.
• Therefore, NGS improves overall diagnostic turnaround time.
• It supports epidemiological surveillance and outbreak investigations.
• Finally, it enables personalized treatment based on precise fungal identification.
• Moreover, it requires advanced laboratory infrastructure and trained personnel.

Further Readings

• https://www.mdpi.com/2075-4418/14/15/1664#:~:text=Next%2Dgeneration%20sequencing%20(NGS),results%20than%20traditional%20diagnostic%20methods.
• https://pmc.ncbi.nlm.nih.gov/articles/PMC9609632/
• https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-023-09691-w
• https://testguide.labmed.uw.edu/view/NGSITS
• https://www.sciencedirect.com/science/article/pii/S1525157820304062
• https://www.researchgate.net/publication/367538016_Fungal_Whole-Genome_Sequencing_for_Species_Identification_From_Test_Development_to_Clinical_Utilization
• https://testguide.labmed.uw.edu/view/FUNDNA
• https://www.mdpi.com/2309-608X/9/2/183
• https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2023.1329330/full
• https://encyclopedia.pub/entry/30018
• https://academic.oup.com/ofid/article/6/Supplement_2/S148/5604489