{"title":"Parvovirus dark matter in the cloaca of wild birds.","authors":"Ziyuan Dai, Haoning Wang, Haisheng Wu, Qing Zhang, Likai Ji, Xiaochun Wang, Quan Shen, Shixing Yang, Xiao Ma, Tongling Shan, Wen Zhang","doi":"10.1093/gigascience/giad001","DOIUrl":null,"url":null,"abstract":"<p><p>With the development of viral metagenomics and next-generation sequencing technology, more and more novel parvoviruses have been identified in recent years, including even entirely new lineages. The Parvoviridae family includes a different group of viruses that can infect a wide variety of animals. In this study, systematic analysis was performed to identify the \"dark matter\" (datasets that cannot be easily attributed to known viruses) of parvoviruses and to explore their genetic diversity from wild birds' cloacal swab samples. We have tentatively defined this parvovirus \"dark matter\" as a highly divergent lineage in the Parvoviridae family. All parvoviruses showed several characteristics, including 2 major protein-coding genes and similar genome lengths. Moreover, we observed that the novel parvo-like viruses share similar genome organizations to most viruses in Parvoviridae but could not clustered with the established subfamilies in phylogenetic analysis. We also found some new members associated with the Bidnaviridae family, which may be derived from parvovirus. This suggests that systematic analysis of domestic and wild animal samples is necessary to explore the genetic diversity of parvoviruses and to mine for more of this potential dark matter.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":null,"pages":null},"PeriodicalIF":11.8000,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9896142/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"GigaScience","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/gigascience/giad001","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/2/3 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
With the development of viral metagenomics and next-generation sequencing technology, more and more novel parvoviruses have been identified in recent years, including even entirely new lineages. The Parvoviridae family includes a different group of viruses that can infect a wide variety of animals. In this study, systematic analysis was performed to identify the "dark matter" (datasets that cannot be easily attributed to known viruses) of parvoviruses and to explore their genetic diversity from wild birds' cloacal swab samples. We have tentatively defined this parvovirus "dark matter" as a highly divergent lineage in the Parvoviridae family. All parvoviruses showed several characteristics, including 2 major protein-coding genes and similar genome lengths. Moreover, we observed that the novel parvo-like viruses share similar genome organizations to most viruses in Parvoviridae but could not clustered with the established subfamilies in phylogenetic analysis. We also found some new members associated with the Bidnaviridae family, which may be derived from parvovirus. This suggests that systematic analysis of domestic and wild animal samples is necessary to explore the genetic diversity of parvoviruses and to mine for more of this potential dark matter.
期刊介绍:
GigaScience seeks to transform data dissemination and utilization in the life and biomedical sciences. As an online open-access open-data journal, it specializes in publishing "big-data" studies encompassing various fields. Its scope includes not only "omic" type data and the fields of high-throughput biology currently serviced by large public repositories, but also the growing range of more difficult-to-access data, such as imaging, neuroscience, ecology, cohort data, systems biology and other new types of large-scale shareable data.