5
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Viromes vs. mixed community metagenomes: choice of method dictates interpretation of viral community ecology

      Preprint
      research-article

      Read this article at

      Bookmark
          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.

          Abstract

          Viruses, the majority of which are uncultivated, are among the most abundant biological entities in microbiomes and ecosystems on Earth. From selfishly altering community-wide microbial physiology to driving microbiome dynamics, viruses (particularly bacteriophage) are fundamental members of microbiomes. While the number of studies leveraging viral metagenomics (viromics) for studying uncultivated viruses is growing, standards for viromics research are lacking. Viromics can leverage computational discovery of viruses from total metagenomes of all community members (hereafter metagenomes) or use physical separation of virus-specific fractions (hereafter viromes). However, differences in the recovery and interpretation of viruses from metagenomes and viromes obtained from the same samples remains understudied. Here, we compare viral communities from paired viromes and metagenomes obtained from 51 diverse samples across human gut, soil, freshwater, and marine ecosystems. Overall, viral communities obtained from viromes were more abundant and species rich than those obtained from metagenomes, although there were some exceptions. Despite this, metagenomes still contained many viral genomes not detected in viromes. We also found notable differences in the predicted lytic state of viruses detected in viromes vs. metagenomes at the time of sequencing. Other forms of variation observed include genome presence/absence, genome quality, and encoded protein content between viromes and metagenomes, but the magnitude of these differences varied by environment. Overall, our results here show that one’s choice of method can lead to differing interpretations of viral community ecology. We suggest that the choice of whether to target a metagenome or virome should be dependent on the environmental context and ecological questions being asked. However, our overall recommendation to researchers investigating viral ecology and evolution is to pair both approaches to maximize their respective benefits.

          Related collections

          Most cited references95

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Fast gapped-read alignment with Bowtie 2.

            As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              BLAST+: architecture and applications

              Background Sequence similarity searching is a very important bioinformatics task. While Basic Local Alignment Search Tool (BLAST) outperforms exact methods through its use of heuristics, the speed of the current BLAST software is suboptimal for very long queries or database sequences. There are also some shortcomings in the user-interface of the current command-line applications. Results We describe features and improvements of rewritten BLAST software and introduce new command-line applications. Long query sequences are broken into chunks for processing, in some cases leading to dramatically shorter run times. For long database sequences, it is possible to retrieve only the relevant parts of the sequence, reducing CPU time and memory usage for searches of short queries against databases of contigs or chromosomes. The program can now retrieve masking information for database sequences from the BLAST databases. A new modular software library can now access subject sequence data from arbitrary data sources. We introduce several new features, including strategy files that allow a user to save and reuse their favorite set of options. The strategy files can be uploaded to and downloaded from the NCBI BLAST web site. Conclusion The new BLAST command-line applications, compared to the current BLAST tools, demonstrate substantial speed improvements for long queries as well as chromosome length database sequences. We have also improved the user interface of the command-line applications.
                Bookmark

                Author and article information

                Contributors
                Role: ConceptualizationRole: MethodologyRole: SoftwareRole: ValidationRole: Formal AnalysisRole: InvestigationRole: Data curationRole: Writing — Original DraftRole: Writing — Review & EditingRole: Visualization
                Role: InvestigationRole: Data curationRole: Writing — Review & Editing
                Role: InvestigationRole: Data curationRole: Writing — Review & Editing
                Role: InvestigationRole: Data curationRole: Writing — Review & Editing
                Role: ConceptualizationRole: MethodologyRole: InvestigationRole: ResourcesRole: Data curationRole: Writing — Review & EditingRole: SupervisionRole: Project AdministrationRole: Funding Acquisition
                Journal
                bioRxiv
                BIORXIV
                bioRxiv
                Cold Spring Harbor Laboratory
                17 October 2023
                : 2023.10.15.562385
                Affiliations
                [1 ]Department of Bacteriology, University of Wisconsin-Madison, Madison, Wisconsin, USA
                [2 ]Microbiology Doctoral Training Program, University of Wisconsin-Madison, Madison, Wisconsin, USA
                [3 ]Freshwater and Marine Sciences Program, University of Wisconsin-Madison, Madison, Wisconsin, USA
                [4 ]Department of Integrative Biology, University of Wisconsin-Madison, Madison, Wisconsin, USA
                Author notes
                [* ]Correspondence: karthik@ 123456bact.wisc.edu
                Article
                10.1101/2023.10.15.562385
                10614762
                37904928
                61f37c8f-333b-4439-9fce-71c02f4dd627

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.

                History
                Funding
                This research was supported by National Institute of General Medical Sciences of the National Institutes of Health under award number R35GM143024, and by the National Science Foundation under grant numbers DBI2047598 and OCE2049478.
                Categories
                Article

                virus,bacteriophage,virome,viromics,metagenome
                virus, bacteriophage, virome, viromics, metagenome

                Comments

                Comment on this article