Journal
PATHOGENS
Volume 10, Issue 8, Pages -Publisher
MDPI
DOI: 10.3390/pathogens10081026
Keywords
bioinformatics; cervical cancer; deep sequencing; human papillomavirus; HPV genotyping; metagenome; next generation sequencing; taxonomic classification; virome
Categories
Funding
- DoD Congressionally Directed Medical Research Programs (CDMRP) [CDMRPL-16-0-DM160469]
- Department of Clinical Investigation Intramural Funding Program at Brooke Army Medical Center, Fort Sam Houston, Texas
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Through customization of a papillomavirus database and the CLC Microbial Genomics Module, automated workflows were developed for HPV taxonomic profiling and visualization, resulting in a fast method for virome profiling.
Next-generation sequencing (NGS) has actualized the human papillomavirus (HPV) virome profiling for in-depth investigation of viral evolution and pathogenesis. However, viral computational analysis remains a bottleneck due to semantic discrepancies between computational tools and curated reference genomes. To address this, we developed and tested automated workflows for HPV taxonomic profiling and visualization using a customized papillomavirus database in the CLC Microbial Genomics Module. HPV genomes from Papilloma Virus Episteme were customized and incorporated into CLC ready-to-use workflows for stepwise data processing to include: (1) Taxonomic Analysis, (2) Estimate Alpha/Beta Diversities, and (3) Map Reads to Reference. Low-grade (n = 95) and high-grade (n = 60) Pap smears were tested with ensuing collective runtimes: Taxonomic Analysis (36 min); Alpha/Beta Diversities (5 s); Map Reads (45 min). Tabular output conversion to visualizations entailed 1-2 keystrokes. Biodiversity analysis between low- (LSIL) and high-grade squamous intraepithelial lesions (HSIL) revealed loss of species richness and gain of dominance by HPV-16 in HSIL. Integrating clinically relevant, taxonomized HPV reference genomes within automated workflows proved to be an ultra-fast method of virome profiling. The entire process named HPV DeepSeq provides a simple, accurate and practical means of NGS data analysis for a broad range of applications in viral research.
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