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      Exploring the Diversity of the Human Blood Virome

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      Viruses
      MDPI AG

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          Abstract

          Metagenomics is greatly improving our ability to discover new viruses, as well as their possible associations with disease. However, metagenomics has also changed our understanding of viruses in general. The vast expansion of currently known viral diversity has revealed a large fraction of non-pathogenic viruses, and offers a new perspective in which viruses function as important components of many ecosystems. In this vein, studies of the human blood virome are often motivated by the search for new viral diseases, especially those associated with blood transfusions. However, these studies have revealed the common presence of apparently non-pathogenic viruses in blood, particularly human anelloviruses and, to a lower extent, human pegiviruses (HPgV). To shed light on the diversity of the human blood virome, we subjected pooled plasma samples from 587 healthy donors in Spain to a viral enrichment protocol, followed by massive parallel sequencing. This showed that anelloviruses were clearly the major component of the blood virome and showed remarkable diversity. In total, we assembled 332 complete or near-complete anellovirus genomes, 50 of which could be considered new species. HPgV was much less frequent, but we, nevertheless, recovered 17 different isolates that we subsequently used for characterizing the diversity of this virus. In-depth investigation of the human blood virome should help to elucidate the ecology of these viruses, and to unveil potentially associated diseases.

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          MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms.

          The Molecular Evolutionary Genetics Analysis (Mega) software implements many analytical methods and tools for phylogenomics and phylomedicine. Here, we report a transformation of Mega to enable cross-platform use on Microsoft Windows and Linux operating systems. Mega X does not require virtualization or emulation software and provides a uniform user experience across platforms. Mega X has additionally been upgraded to use multiple computing cores for many molecular evolutionary analyses. Mega X is available in two interfaces (graphical and command line) and can be downloaded from www.megasoftware.net free of charge.
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            MUSCLE: multiple sequence alignment with high accuracy and high throughput.

            We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
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              MultiQC: summarize analysis results for multiple tools and samples in a single report

              Motivation: Fast and accurate quality control is essential for studies involving next-generation sequencing data. Whilst numerous tools exist to quantify QC metrics, there is no common approach to flexibly integrate these across tools and large sample sets. Assessing analysis results across an entire project can be time consuming and error prone; batch effects and outlier samples can easily be missed in the early stages of analysis. Results: We present MultiQC, a tool to create a single report visualising output from multiple tools across many samples, enabling global trends and biases to be quickly identified. MultiQC can plot data from many common bioinformatics tools and is built to allow easy extension and customization. Availability and implementation: MultiQC is available with an GNU GPLv3 license on GitHub, the Python Package Index and Bioconda. Documentation and example reports are available at http://multiqc.info Contact: phil.ewels@scilifelab.se
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                Author and article information

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                Journal
                VIRUBR
                Viruses
                Viruses
                MDPI AG
                1999-4915
                November 2021
                November 21 2021
                : 13
                : 11
                : 2322
                Article
                10.3390/v13112322
                34835128
                b159348a-397e-4ac7-9d7c-31dd13df9927
                © 2021

                https://creativecommons.org/licenses/by/4.0/

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