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      New viral biogeochemical roles revealed through metagenomic analysis of Lake Baikal

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          Abstract

          Background

          Lake Baikal is the largest body of liquid freshwater on Earth. Previous studies have described the microbial composition of this habitat, but the viral communities from this ecosystem have not been characterized in detail.

          Results

          Here, we describe the viral diversity of this habitat across depth and seasonal gradients. We discovered 19,475 bona fide viral sequences, which are derived from viruses predicted to infect abundant and ecologically important taxa that reside in Lake Baikal, such as Nitrospirota, Methylophilaceae, and Crenarchaeota. Diversity analysis revealed significant changes in viral community composition between epipelagic and bathypelagic zones. Analysis of the gene content of individual viral populations allowed us to describe one of the first bacteriophages that infect Nitrospirota, and their extensive repertoire of auxiliary metabolic genes that might enhance carbon fixation through the reductive TCA cycle. We also described bacteriophages of methylotrophic bacteria with the potential to enhance methanol oxidation and the S-adenosyl-L-methionine cycle.

          Conclusions

          These findings unraveled new ways by which viruses influence the carbon cycle in freshwater ecosystems, namely, by using auxiliary metabolic genes that act upon metabolisms of dark carbon fixation and methylotrophy. Therefore, our results shed light on the processes through which viruses can impact biogeochemical cycles of major ecological relevance.

          Supplementary information

          The online version contains supplementary material available at 10.1186/s40168-020-00936-4.

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          Most cited references39

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          Fast and sensitive protein alignment using DIAMOND.

          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.
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            HMMER web server: 2015 update

            The HMMER website, available at http://www.ebi.ac.uk/Tools/hmmer/, provides access to the protein homology search algorithms found in the HMMER software suite. Since the first release of the website in 2011, the search repertoire has been expanded to include the iterative search algorithm, jackhmmer. The continued growth of the target sequence databases means that traditional tabular representations of significant sequence hits can be overwhelming to the user. Consequently, additional ways of presenting homology search results have been developed, allowing them to be summarised according to taxonomic distribution or domain architecture. The taxonomy and domain architecture representations can be used in combination to filter the results according to the needs of a user. Searches can also be restricted prior to submission using a new taxonomic filter, which not only ensures that the results are specific to the requested taxonomic group, but also improves search performance. The repertoire of profile hidden Markov model libraries, which are used for annotation of query sequences with protein families and domains, has been expanded to include the libraries from CATH-Gene3D, PIRSF, Superfamily and TIGRFAMs. Finally, we discuss the relocation of the HMMER webserver to the European Bioinformatics Institute and the potential impact that this will have.
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              VirSorter: mining viral signal from microbial genomic data

              Viruses of microbes impact all ecosystems where microbes drive key energy and substrate transformations including the oceans, humans and industrial fermenters. However, despite this recognized importance, our understanding of viral diversity and impacts remains limited by too few model systems and reference genomes. One way to fill these gaps in our knowledge of viral diversity is through the detection of viral signal in microbial genomic data. While multiple approaches have been developed and applied for the detection of prophages (viral genomes integrated in a microbial genome), new types of microbial genomic data are emerging that are more fragmented and larger scale, such as Single-cell Amplified Genomes (SAGs) of uncultivated organisms or genomic fragments assembled from metagenomic sequencing. Here, we present VirSorter, a tool designed to detect viral signal in these different types of microbial sequence data in both a reference-dependent and reference-independent manner, leveraging probabilistic models and extensive virome data to maximize detection of novel viruses. Performance testing shows that VirSorter’s prophage prediction capability compares to that of available prophage predictors for complete genomes, but is superior in predicting viral sequences outside of a host genome (i.e., from extrachromosomal prophages, lytic infections, or partially assembled prophages). Furthermore, VirSorter outperforms existing tools for fragmented genomic and metagenomic datasets, and can identify viral signal in assembled sequence (contigs) as short as 3kb, while providing near-perfect identification (>95% Recall and 100% Precision) on contigs of at least 10kb. Because VirSorter scales to large datasets, it can also be used in “reverse” to more confidently identify viral sequence in viral metagenomes by sorting away cellular DNA whether derived from gene transfer agents, generalized transduction or contamination. Finally, VirSorter is made available through the iPlant Cyberinfrastructure that provides a web-based user interface interconnected with the required computing resources. VirSorter thus complements existing prophage prediction softwares to better leverage fragmented, SAG and metagenomic datasets in a way that will scale to modern sequencing. Given these features, VirSorter should enable the discovery of new viruses in microbial datasets, and further our understanding of uncultivated viral communities across diverse ecosystems.
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                Author and article information

                Contributors
                fhernandes@umh.es
                Journal
                Microbiome
                Microbiome
                Microbiome
                BioMed Central (London )
                2049-2618
                19 November 2020
                19 November 2020
                2020
                : 8
                : 163
                Affiliations
                [1 ]GRID grid.26811.3c, ISNI 0000 0001 0586 4893, Evolutionary Genomics Group, Dpto. Producción Vegetal y Microbiología, , Universidad Miguel Hernández, ; Aptdo. 18., Ctra. Alicante-Valencia N-332, s/n, San Juan de Alicante, 03550 Alicante, Spain
                [2 ]GRID grid.10914.3d, ISNI 0000 0001 2227 4609, Department of Marine Microbiology and Biogeochemistry, , NIOZ Royal Netherlands Institute for Sea Research, ; Den Burg, The Netherlands
                [3 ]GRID grid.5477.1, ISNI 0000000120346234, Utrecht University, ; Utrecht, The Netherlands
                [4 ]GRID grid.415877.8, ISNI 0000 0001 2254 1834, Limnological Institute, , Siberian Branch of the Russian Academy of Sciences, ; Irkutsk, Russia
                [5 ]GRID grid.18763.3b, ISNI 0000000092721542, Research Center for Molecular Mechanisms of Aging and Age-related Diseases, , Moscow Institute of Physics and Technology, ; Dolgoprudny, Russia
                Author information
                http://orcid.org/0000-0001-6430-7069
                Article
                936
                10.1186/s40168-020-00936-4
                7678222
                33213521
                c22f012e-7b40-412e-834e-2e002485f7e5
                © The Author(s) 2020

                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/. The Creative Commons Public Domain Dedication waiver ( http://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.

                History
                : 20 May 2020
                : 12 October 2020
                Funding
                Funded by: Ministerio de Ciencia e Innovación
                Award ID: CGL2016-76273-P
                Funded by: FundRef http://dx.doi.org/10.13039/501100003359, Generalitat Valenciana;
                Award ID: PROMETEU/2019/009
                Award ID: APOSTD/2018/186
                Award ID: APOSTD/2019/009
                Funded by: Ministry for Science and Education of Russia
                Award ID: 5top100-program
                Funded by: FundRef http://dx.doi.org/10.13039/501100011596, Conselleria d'Educació, Investigació, Cultura i Esport;
                Award ID: ACIF/2016/050
                Funded by: State Assignment 0345-2019-0007
                Award ID: State Assignment 0345-2019-0007
                Categories
                Research
                Custom metadata
                © The Author(s) 2020

                lake baikal,bacteriophages,metagenomes,auxiliary metabolic genes,nitrospira,reductive tca cycle,methylotrophy,s-adenosyl-l-methionine cycle

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