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

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 algori...

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Autores principales: Inkman, Matthew J., Jayachandran, Kay, Ellis, Thomas M., Ruiz, Fiona, McLellan, Michael D., Miller, Christopher A., Wu, Yufeng, Ojesina, Akinyemi I., Schwarz, Julie K., Zhang, Jin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7459114/
https://www.ncbi.nlm.nih.gov/pubmed/32868873
http://dx.doi.org/10.1038/s41598-020-71300-7
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author Inkman, Matthew J.
Jayachandran, Kay
Ellis, Thomas M.
Ruiz, Fiona
McLellan, Michael D.
Miller, Christopher A.
Wu, Yufeng
Ojesina, Akinyemi I.
Schwarz, Julie K.
Zhang, Jin
author_facet Inkman, Matthew J.
Jayachandran, Kay
Ellis, Thomas M.
Ruiz, Fiona
McLellan, Michael D.
Miller, Christopher A.
Wu, Yufeng
Ojesina, Akinyemi I.
Schwarz, Julie K.
Zhang, Jin
author_sort Inkman, Matthew J.
collection PubMed
description 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.
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spelling pubmed-74591142020-09-01 HPV-EM: an accurate HPV detection and genotyping EM algorithm Inkman, Matthew J. Jayachandran, Kay Ellis, Thomas M. Ruiz, Fiona McLellan, Michael D. Miller, Christopher A. Wu, Yufeng Ojesina, Akinyemi I. Schwarz, Julie K. Zhang, Jin Sci Rep Article 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. Nature Publishing Group UK 2020-08-31 /pmc/articles/PMC7459114/ /pubmed/32868873 http://dx.doi.org/10.1038/s41598-020-71300-7 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Inkman, Matthew J.
Jayachandran, Kay
Ellis, Thomas M.
Ruiz, Fiona
McLellan, Michael D.
Miller, Christopher A.
Wu, Yufeng
Ojesina, Akinyemi I.
Schwarz, Julie K.
Zhang, Jin
HPV-EM: an accurate HPV detection and genotyping EM algorithm
title HPV-EM: an accurate HPV detection and genotyping EM algorithm
title_full HPV-EM: an accurate HPV detection and genotyping EM algorithm
title_fullStr HPV-EM: an accurate HPV detection and genotyping EM algorithm
title_full_unstemmed HPV-EM: an accurate HPV detection and genotyping EM algorithm
title_short HPV-EM: an accurate HPV detection and genotyping EM algorithm
title_sort hpv-em: an accurate hpv detection and genotyping em algorithm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7459114/
https://www.ncbi.nlm.nih.gov/pubmed/32868873
http://dx.doi.org/10.1038/s41598-020-71300-7
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