HPV-EM: an accurate HPV detection and genotyping EM algorithm

Matthew J. Inkman, Kay Jayachandran, Thomas M. Ellis, Fiona Ruiz, Michael D. McLellan, Christopher A. Miller, Yufeng Wu, Akinyemi I. Ojesina, Julie K. Schwarz, Jin Zhang

Research output: Contribution to journalArticlepeer-review

Abstract

Accurate HPV genotyping is crucial in facilitating epidemiology studies, vaccine trials, and HPV-related cancer research. Contemporary HPV genotyping assays only detect < 25% of all known HPV genotypes and are not accurate for low-risk or mixed HPV genotypes. Current genomic HPV genotyping algorithms use a simple read-alignment and filtering strategy that has difficulty handling repeats and homology sequences. Therefore, we have developed an optimized expectation–maximization algorithm, designated HPV-EM, to address the ambiguities caused by repetitive sequencing reads. HPV-EM achieved 97–100% accuracy when benchmarked using cell line data and TCGA cervical cancer data. We also validated HPV-EM using DNA tiling data on an institutional cervical cancer cohort (96.5% accuracy). Using HPV-EM, we demonstrated HPV genotypic differences in recurrence and patient outcomes in cervical and head and neck cancers.

Original languageEnglish
Article number14340
JournalScientific reports
Volume10
Issue number1
DOIs
StatePublished - Dec 1 2020

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