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Development and Evaluation of Quality Metrics for Bioinformatics Analysis of Viral Insertion Site Data Generated Using High Throughput Sequencing
Integration of viral vectors into a host genome is associated with insertional mutagenesis and subjects in clinical gene therapy trials must be monitored for this adverse event. Several PCR based methods such as ligase-mediated (LM) PCR, linear-amplification-mediated (LAM) PCR and non-restrictive (n...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5423470/ https://www.ncbi.nlm.nih.gov/pubmed/28548067 http://dx.doi.org/10.3390/biomedicines2020195 |
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author | Gao, Hongyu Hawkins, Troy Jasti, Aparna Chen, Yu-Hsiang Mockaitis, Keithanne Dinauer, Mary Cornetta, Kenneth |
author_facet | Gao, Hongyu Hawkins, Troy Jasti, Aparna Chen, Yu-Hsiang Mockaitis, Keithanne Dinauer, Mary Cornetta, Kenneth |
author_sort | Gao, Hongyu |
collection | PubMed |
description | Integration of viral vectors into a host genome is associated with insertional mutagenesis and subjects in clinical gene therapy trials must be monitored for this adverse event. Several PCR based methods such as ligase-mediated (LM) PCR, linear-amplification-mediated (LAM) PCR and non-restrictive (nr) LAM PCR were developed to identify sites of vector integration. Coupling the power of next-generation sequencing technologies with various PCR approaches will provide a comprehensive and genome-wide profiling of insertion sites and increase throughput. In this bioinformatics study, we aimed to develop and apply quality metrics to viral insertion data obtained using next-generation sequencing. We developed five simple metrics for assessing next-generation sequencing data from different PCR products and showed how the metrics can be used to objectively compare runs performed with the same methodology as well as data generated using different PCR techniques. The results will help researchers troubleshoot complex methodologies, understand the quality of sequencing data, and provide a starting point for developing standardization of vector insertion site data analysis. |
format | Online Article Text |
id | pubmed-5423470 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-54234702017-05-23 Development and Evaluation of Quality Metrics for Bioinformatics Analysis of Viral Insertion Site Data Generated Using High Throughput Sequencing Gao, Hongyu Hawkins, Troy Jasti, Aparna Chen, Yu-Hsiang Mockaitis, Keithanne Dinauer, Mary Cornetta, Kenneth Biomedicines Article Integration of viral vectors into a host genome is associated with insertional mutagenesis and subjects in clinical gene therapy trials must be monitored for this adverse event. Several PCR based methods such as ligase-mediated (LM) PCR, linear-amplification-mediated (LAM) PCR and non-restrictive (nr) LAM PCR were developed to identify sites of vector integration. Coupling the power of next-generation sequencing technologies with various PCR approaches will provide a comprehensive and genome-wide profiling of insertion sites and increase throughput. In this bioinformatics study, we aimed to develop and apply quality metrics to viral insertion data obtained using next-generation sequencing. We developed five simple metrics for assessing next-generation sequencing data from different PCR products and showed how the metrics can be used to objectively compare runs performed with the same methodology as well as data generated using different PCR techniques. The results will help researchers troubleshoot complex methodologies, understand the quality of sequencing data, and provide a starting point for developing standardization of vector insertion site data analysis. MDPI 2014-05-06 /pmc/articles/PMC5423470/ /pubmed/28548067 http://dx.doi.org/10.3390/biomedicines2020195 Text en © 2014 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/). |
spellingShingle | Article Gao, Hongyu Hawkins, Troy Jasti, Aparna Chen, Yu-Hsiang Mockaitis, Keithanne Dinauer, Mary Cornetta, Kenneth Development and Evaluation of Quality Metrics for Bioinformatics Analysis of Viral Insertion Site Data Generated Using High Throughput Sequencing |
title | Development and Evaluation of Quality Metrics for Bioinformatics Analysis of Viral Insertion Site Data Generated Using High Throughput Sequencing |
title_full | Development and Evaluation of Quality Metrics for Bioinformatics Analysis of Viral Insertion Site Data Generated Using High Throughput Sequencing |
title_fullStr | Development and Evaluation of Quality Metrics for Bioinformatics Analysis of Viral Insertion Site Data Generated Using High Throughput Sequencing |
title_full_unstemmed | Development and Evaluation of Quality Metrics for Bioinformatics Analysis of Viral Insertion Site Data Generated Using High Throughput Sequencing |
title_short | Development and Evaluation of Quality Metrics for Bioinformatics Analysis of Viral Insertion Site Data Generated Using High Throughput Sequencing |
title_sort | development and evaluation of quality metrics for bioinformatics analysis of viral insertion site data generated using high throughput sequencing |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5423470/ https://www.ncbi.nlm.nih.gov/pubmed/28548067 http://dx.doi.org/10.3390/biomedicines2020195 |
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