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      Identification of a novel hepacivirus in Mongolian gerbil ( Meriones unguiculatus) from Shaanxi, China

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Highlights

          • •

            The first hepacivirus detected in Mongolian gerbils from a plague zones in China.

          • •

            A novel hepacivirus closely related to hepacivirus E and F.

          • •

            Mongolian gerbils could be a potential animal model for hepacivirus pathogenicity.

          • •

            Extending the genetic diversity and host range of hepaciviruses.

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          Most cited references16

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Anthony M. Bolger, Marc Lohse, Bjoern Usadel (2014)
          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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            IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

            Lam-Tung Nguyen, Heiko Schmidt, Arndt von Haeseler … (2014)
            Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
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              Fast and sensitive protein alignment using DIAMOND.

              Benjamin Buchfink, Chao Xie, Daniel Huson (2015)
              The alignment of sequencing reads against a protein reference database is a major computational bottleneck in metagenomics and data-intensive evolutionary projects. Although recent tools offer improved performance over the gold standard BLASTX, they exhibit only a modest speedup or low sensitivity. We introduce DIAMOND, an open-source algorithm based on double indexing that is 20,000 times faster than BLASTX on short reads and has a similar degree of sensitivity.
              Article 0 comments Cited 3639 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Yang-xin Sun
                Wei-feng Shi
                Ci-xiu Li
                Journal
                Journal ID (nlm-ta): Virol Sin
                Journal ID (iso-abbrev): Virol Sin
                Title: Virologica Sinica
                Publisher: Wuhan Institute of Virology, Chinese Academy of Sciences
                ISSN (Print): 1674-0769
                ISSN (Electronic): 1995-820X
                Publication date PMC-release: 19 January 2022
                Publication date Collection: April 2022
                Publication date (Electronic): 19 January 2022
                Volume: 37
                Issue: 2
                Pages: 307-310
                Affiliations
                [a ]Department of Plague and Brucellosis, Shaanxi Center for Disease Control and Prevention, Xi’ An 710054, China
                [b ]Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, China
                [c ]Key Laboratory of Etiology and Epidemiology of Emerging Infectious Diseases in Universities of Shandong, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271000, China
                [d ]Department of Pathogen Biology, School of Basic Medicine, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271000, China
                [e ]Shaanxi Center for Disease Control and Prevention, Xi’An, 710054, China
                [f ]School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271000, China
                Author notes
                [1]

                Cui-hong An and Juan Li contributed equally to this work.

                Article
                Publisher Item ID: S1995-820X(22)00016-5
                DOI: 10.1016/j.virs.2022.01.016
                PMC ID: 9170912
                PubMed ID: 35248515
                SO-VID: 713358d5-14ab-452e-9152-d2b78261b14d
                Copyright © © 2022 The Authors
                License:

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                Date received : 28 July 2021
                Date accepted : 10 October 2021
                Categories
                Subject: Letter

                Data availability:

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