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Application of a bioinformatic pipeline to RNA-seq data identifies novel virus-like sequence in human blood
Numerous reports have suggested that infectious agents could play a role in neurodegenerative diseases, but specific etiological agents have not been convincingly demonstrated. To search for candidate agents in an unbiased fashion, we have developed a bioinformatic pipeline that identifies microbial...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8661426/ https://www.ncbi.nlm.nih.gov/pubmed/33914880 http://dx.doi.org/10.1093/g3journal/jkab141 |
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author | Melnick, Marko Gonzales, Patrick LaRocca, Thomas J Song, Yuping Wuu, Joanne Benatar, Michael Oskarsson, Björn Petrucelli, Leonard Dowell, Robin D Link, Christopher D Prudencio, Mercedes |
author_facet | Melnick, Marko Gonzales, Patrick LaRocca, Thomas J Song, Yuping Wuu, Joanne Benatar, Michael Oskarsson, Björn Petrucelli, Leonard Dowell, Robin D Link, Christopher D Prudencio, Mercedes |
author_sort | Melnick, Marko |
collection | PubMed |
description | Numerous reports have suggested that infectious agents could play a role in neurodegenerative diseases, but specific etiological agents have not been convincingly demonstrated. To search for candidate agents in an unbiased fashion, we have developed a bioinformatic pipeline that identifies microbial sequences in mammalian RNA-seq data, including sequences with no significant nucleotide similarity hits in GenBank. Effectiveness of the pipeline was tested using publicly available RNA-seq data and in a reconstruction experiment using synthetic data. We then applied this pipeline to a novel RNA-seq dataset generated from a cohort of 120 samples from amyotrophic lateral sclerosis patients and controls, and identified sequences corresponding to known bacteria and viruses, as well as novel virus-like sequences. The presence of these novel virus-like sequences, which were identified in subsets of both patients and controls, were confirmed by quantitative RT-PCR. We believe this pipeline will be a useful tool for the identification of potential etiological agents in the many RNA-seq datasets currently being generated. |
format | Online Article Text |
id | pubmed-8661426 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-86614262021-12-10 Application of a bioinformatic pipeline to RNA-seq data identifies novel virus-like sequence in human blood Melnick, Marko Gonzales, Patrick LaRocca, Thomas J Song, Yuping Wuu, Joanne Benatar, Michael Oskarsson, Björn Petrucelli, Leonard Dowell, Robin D Link, Christopher D Prudencio, Mercedes G3 (Bethesda) Software and Data Resources Numerous reports have suggested that infectious agents could play a role in neurodegenerative diseases, but specific etiological agents have not been convincingly demonstrated. To search for candidate agents in an unbiased fashion, we have developed a bioinformatic pipeline that identifies microbial sequences in mammalian RNA-seq data, including sequences with no significant nucleotide similarity hits in GenBank. Effectiveness of the pipeline was tested using publicly available RNA-seq data and in a reconstruction experiment using synthetic data. We then applied this pipeline to a novel RNA-seq dataset generated from a cohort of 120 samples from amyotrophic lateral sclerosis patients and controls, and identified sequences corresponding to known bacteria and viruses, as well as novel virus-like sequences. The presence of these novel virus-like sequences, which were identified in subsets of both patients and controls, were confirmed by quantitative RT-PCR. We believe this pipeline will be a useful tool for the identification of potential etiological agents in the many RNA-seq datasets currently being generated. Oxford University Press 2021-04-29 /pmc/articles/PMC8661426/ /pubmed/33914880 http://dx.doi.org/10.1093/g3journal/jkab141 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of Genetics Society of America. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Software and Data Resources Melnick, Marko Gonzales, Patrick LaRocca, Thomas J Song, Yuping Wuu, Joanne Benatar, Michael Oskarsson, Björn Petrucelli, Leonard Dowell, Robin D Link, Christopher D Prudencio, Mercedes Application of a bioinformatic pipeline to RNA-seq data identifies novel virus-like sequence in human blood |
title | Application of a bioinformatic pipeline to RNA-seq data identifies novel virus-like sequence in human blood |
title_full | Application of a bioinformatic pipeline to RNA-seq data identifies novel virus-like sequence in human blood |
title_fullStr | Application of a bioinformatic pipeline to RNA-seq data identifies novel virus-like sequence in human blood |
title_full_unstemmed | Application of a bioinformatic pipeline to RNA-seq data identifies novel virus-like sequence in human blood |
title_short | Application of a bioinformatic pipeline to RNA-seq data identifies novel virus-like sequence in human blood |
title_sort | application of a bioinformatic pipeline to rna-seq data identifies novel virus-like sequence in human blood |
topic | Software and Data Resources |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8661426/ https://www.ncbi.nlm.nih.gov/pubmed/33914880 http://dx.doi.org/10.1093/g3journal/jkab141 |
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