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LABRADOR—A Computational Workflow for Virus Detection in High-Throughput Sequencing Data

High-throughput sequencing (HTS) allows detection of known and unknown viruses in samples of broad origin. This makes HTS a perfect technology to determine whether or not the biological products, such as vaccines are free from the adventitious agents, which could support or replace extensive testing...

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Detalles Bibliográficos
Autores principales: Fabiańska, Izabela, Borutzki, Stefan, Richter, Benjamin, Tran, Hon Q., Neubert, Andreas, Mayer, Dietmar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8704571/
https://www.ncbi.nlm.nih.gov/pubmed/34960810
http://dx.doi.org/10.3390/v13122541
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author Fabiańska, Izabela
Borutzki, Stefan
Richter, Benjamin
Tran, Hon Q.
Neubert, Andreas
Mayer, Dietmar
author_facet Fabiańska, Izabela
Borutzki, Stefan
Richter, Benjamin
Tran, Hon Q.
Neubert, Andreas
Mayer, Dietmar
author_sort Fabiańska, Izabela
collection PubMed
description High-throughput sequencing (HTS) allows detection of known and unknown viruses in samples of broad origin. This makes HTS a perfect technology to determine whether or not the biological products, such as vaccines are free from the adventitious agents, which could support or replace extensive testing using various in vitro and in vivo assays. Due to bioinformatics complexities, there is a need for standardized and reliable methods to manage HTS generated data in this field. Thus, we developed LABRADOR—an analysis pipeline for adventitious virus detection. The pipeline consists of several third-party programs and is divided into two major parts: (i) direct reads classification based on the comparison of characteristic profiles between reads and sequences deposited in the database supported with alignment of to the best matching reference sequence and (ii) de novo assembly of contigs and their classification on nucleotide and amino acid levels. To meet the requirements published in guidelines for biologicals’ safety we generated a custom nucleotide database with viral sequences. We tested our pipeline on publicly available HTS datasets and showed that LABRADOR can reliably detect viruses in mixtures of model viruses, vaccines and clinical samples.
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spelling pubmed-87045712021-12-25 LABRADOR—A Computational Workflow for Virus Detection in High-Throughput Sequencing Data Fabiańska, Izabela Borutzki, Stefan Richter, Benjamin Tran, Hon Q. Neubert, Andreas Mayer, Dietmar Viruses Protocol High-throughput sequencing (HTS) allows detection of known and unknown viruses in samples of broad origin. This makes HTS a perfect technology to determine whether or not the biological products, such as vaccines are free from the adventitious agents, which could support or replace extensive testing using various in vitro and in vivo assays. Due to bioinformatics complexities, there is a need for standardized and reliable methods to manage HTS generated data in this field. Thus, we developed LABRADOR—an analysis pipeline for adventitious virus detection. The pipeline consists of several third-party programs and is divided into two major parts: (i) direct reads classification based on the comparison of characteristic profiles between reads and sequences deposited in the database supported with alignment of to the best matching reference sequence and (ii) de novo assembly of contigs and their classification on nucleotide and amino acid levels. To meet the requirements published in guidelines for biologicals’ safety we generated a custom nucleotide database with viral sequences. We tested our pipeline on publicly available HTS datasets and showed that LABRADOR can reliably detect viruses in mixtures of model viruses, vaccines and clinical samples. MDPI 2021-12-18 /pmc/articles/PMC8704571/ /pubmed/34960810 http://dx.doi.org/10.3390/v13122541 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Protocol
Fabiańska, Izabela
Borutzki, Stefan
Richter, Benjamin
Tran, Hon Q.
Neubert, Andreas
Mayer, Dietmar
LABRADOR—A Computational Workflow for Virus Detection in High-Throughput Sequencing Data
title LABRADOR—A Computational Workflow for Virus Detection in High-Throughput Sequencing Data
title_full LABRADOR—A Computational Workflow for Virus Detection in High-Throughput Sequencing Data
title_fullStr LABRADOR—A Computational Workflow for Virus Detection in High-Throughput Sequencing Data
title_full_unstemmed LABRADOR—A Computational Workflow for Virus Detection in High-Throughput Sequencing Data
title_short LABRADOR—A Computational Workflow for Virus Detection in High-Throughput Sequencing Data
title_sort labrador—a computational workflow for virus detection in high-throughput sequencing data
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8704571/
https://www.ncbi.nlm.nih.gov/pubmed/34960810
http://dx.doi.org/10.3390/v13122541
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