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      Comparative Analysis of Viromes Identified in Multiple Macrofungi

      , ,
      Viruses
      MDPI AG

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          Abstract

          Macrofungi play important roles in the soil elemental cycle of terrestrial ecosystems. Fungal viruses are common in filamentous fungi, and some of them can affect the growth and development of hosts. However, the composition and evolution of macrofungal viruses are understudied. In this study, ninety strains of Trametes versicolor, Coprinellus micaceus, Amanita strobiliformis, and Trametes hirsuta were collected in China. Four mixed pools were generated by combining equal quantities of total RNA from each strain, according to the fungal species, and then subjected to RNA sequencing. The sequences were assembled, annotated, and then used for phylogenetic analysis. Twenty novel viruses or viral fragments were characterized from the four species of macrofungi. Based on the phylogenetic analysis, most of the viral contigs were classified into ten viral families or orders: Barnaviridae, Benyviridae, Botourmiaviridae, Deltaflexiviridae, Fusariviridae, Hypoviridae, Totiviridae, Mitoviridae, Mymonaviridae, and Bunyavirales. Of these, ambi-like viruses with circular genomes were widely distributed among the studied species. Furthermore, the number and overall abundance of viruses in these four species of macrofungi (Basidiomycota) were found to be much lower than those in broad-host phytopathogenic fungi (Ascomycota: Sclerotinia sclerotiorum, and Botrytis cinerea). By employing metatranscriptomic analysis in this study, for the first time, we demonstrated the presence of multiple mycoviruses in Amanita strobiliformis, Coprinellus micaceus, Trametes hirsute, and Trametes versicolor, significantly contributing to research on mycoviruses in macrofungi.

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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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            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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              Salmon: fast and bias-aware quantification of transcript expression using dual-phase inference

              We introduce Salmon, a method for quantifying transcript abundance from RNA-seq reads that is accurate and fast. Salmon is the first transcriptome-wide quantifier to correct for fragment GC content bias, which we demonstrate substantially improves the accuracy of abundance estimates and the reliability of subsequent differential expression analysis. Salmon combines a new dual-phase parallel inference algorithm and feature-rich bias models with an ultra-fast read mapping procedure.
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                Author and article information

                Contributors
                Journal
                VIRUBR
                Viruses
                Viruses
                MDPI AG
                1999-4915
                April 2024
                April 12 2024
                : 16
                : 4
                : 597
                Article
                10.3390/v16040597
                f43f2c7c-bdc3-4c51-81d8-62b4e10e528e
                © 2024

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

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