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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
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.
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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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