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Rapid screening and identification of viral pathogens in metagenomic data

BACKGROUND: Virus screening and viral genome reconstruction are urgent and crucial for the rapid identification of viral pathogens, i.e., tracing the source and understanding the pathogenesis when a viral outbreak occurs. Next-generation sequencing (NGS) provides an efficient and unbiased way to ide...

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Autores principales: Song, Shiyang, Ma, Liangxiao, Xu, Xintian, Shi, Han, Li, Xuan, Liu, Yuanhua, Hao, Pei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8668262/
https://www.ncbi.nlm.nih.gov/pubmed/34903237
http://dx.doi.org/10.1186/s12920-021-01138-z
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author Song, Shiyang
Ma, Liangxiao
Xu, Xintian
Shi, Han
Li, Xuan
Liu, Yuanhua
Hao, Pei
author_facet Song, Shiyang
Ma, Liangxiao
Xu, Xintian
Shi, Han
Li, Xuan
Liu, Yuanhua
Hao, Pei
author_sort Song, Shiyang
collection PubMed
description BACKGROUND: Virus screening and viral genome reconstruction are urgent and crucial for the rapid identification of viral pathogens, i.e., tracing the source and understanding the pathogenesis when a viral outbreak occurs. Next-generation sequencing (NGS) provides an efficient and unbiased way to identify viral pathogens in host-associated and environmental samples without prior knowledge. Despite the availability of software, data analysis still requires human operations. A mature pipeline is urgently needed when thousands of viral pathogen and viral genome reconstruction samples need to be rapidly identified. RESULTS: In this paper, we present a rapid and accurate workflow to screen metagenomics sequencing data for viral pathogens and other compositions, as well as enable a reference-based assembler to reconstruct viral genomes. Moreover, we tested our workflow on several metagenomics datasets, including a SARS-CoV-2 patient sample with NGS data, pangolins tissues with NGS data, Middle East Respiratory Syndrome (MERS)-infected cells with NGS data, etc. Our workflow demonstrated high accuracy and efficiency when identifying target viruses from large scale NGS metagenomics data. Our workflow was flexible when working with a broad range of NGS datasets from small (kb) to large (100 Gb). This took from a few minutes to a few hours to complete each task. At the same time, our workflow automatically generates reports that incorporate visualized feedback (e.g., metagenomics data quality statistics, host and viral sequence compositions, details about each of the identified viral pathogens and their coverages, and reassembled viral pathogen sequences based on their closest references). CONCLUSIONS: Overall, our system enabled the rapid screening and identification of viral pathogens from metagenomics data, providing an important piece to support viral pathogen research during a pandemic. The visualized report contains information from raw sequence quality to a reconstructed viral sequence, which allows non-professional people to screen their samples for viruses by themselves (Additional file 1). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-021-01138-z.
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spelling pubmed-86682622021-12-14 Rapid screening and identification of viral pathogens in metagenomic data Song, Shiyang Ma, Liangxiao Xu, Xintian Shi, Han Li, Xuan Liu, Yuanhua Hao, Pei BMC Med Genomics Software BACKGROUND: Virus screening and viral genome reconstruction are urgent and crucial for the rapid identification of viral pathogens, i.e., tracing the source and understanding the pathogenesis when a viral outbreak occurs. Next-generation sequencing (NGS) provides an efficient and unbiased way to identify viral pathogens in host-associated and environmental samples without prior knowledge. Despite the availability of software, data analysis still requires human operations. A mature pipeline is urgently needed when thousands of viral pathogen and viral genome reconstruction samples need to be rapidly identified. RESULTS: In this paper, we present a rapid and accurate workflow to screen metagenomics sequencing data for viral pathogens and other compositions, as well as enable a reference-based assembler to reconstruct viral genomes. Moreover, we tested our workflow on several metagenomics datasets, including a SARS-CoV-2 patient sample with NGS data, pangolins tissues with NGS data, Middle East Respiratory Syndrome (MERS)-infected cells with NGS data, etc. Our workflow demonstrated high accuracy and efficiency when identifying target viruses from large scale NGS metagenomics data. Our workflow was flexible when working with a broad range of NGS datasets from small (kb) to large (100 Gb). This took from a few minutes to a few hours to complete each task. At the same time, our workflow automatically generates reports that incorporate visualized feedback (e.g., metagenomics data quality statistics, host and viral sequence compositions, details about each of the identified viral pathogens and their coverages, and reassembled viral pathogen sequences based on their closest references). CONCLUSIONS: Overall, our system enabled the rapid screening and identification of viral pathogens from metagenomics data, providing an important piece to support viral pathogen research during a pandemic. The visualized report contains information from raw sequence quality to a reconstructed viral sequence, which allows non-professional people to screen their samples for viruses by themselves (Additional file 1). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-021-01138-z. BioMed Central 2021-12-14 /pmc/articles/PMC8668262/ /pubmed/34903237 http://dx.doi.org/10.1186/s12920-021-01138-z Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Software
Song, Shiyang
Ma, Liangxiao
Xu, Xintian
Shi, Han
Li, Xuan
Liu, Yuanhua
Hao, Pei
Rapid screening and identification of viral pathogens in metagenomic data
title Rapid screening and identification of viral pathogens in metagenomic data
title_full Rapid screening and identification of viral pathogens in metagenomic data
title_fullStr Rapid screening and identification of viral pathogens in metagenomic data
title_full_unstemmed Rapid screening and identification of viral pathogens in metagenomic data
title_short Rapid screening and identification of viral pathogens in metagenomic data
title_sort rapid screening and identification of viral pathogens in metagenomic data
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8668262/
https://www.ncbi.nlm.nih.gov/pubmed/34903237
http://dx.doi.org/10.1186/s12920-021-01138-z
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