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      Defining the eco-enzymological role of the fungal strain Coniochaeta sp. 2T2.1 in a tripartite lignocellulolytic microbial consortium

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          ABSTRACT

          Coniochaeta species are versatile ascomycetes that have great capacity to deconstruct lignocellulose. Here, we explore the transcriptome of Coniochaeta sp. strain 2T2.1 from wheat straw-driven cultures with the fungus growing alone or as a member of a synthetic microbial consortium with Sphingobacterium multivorum w15 and Citrobacter freundii so4. The differential expression profiles of carbohydrate-active enzymes indicated an onset of (hemi)cellulose degradation by 2T2.1 during the initial 24 hours of incubation. Within the tripartite consortium, 63 transcripts of strain 2T2.1 were differentially expressed at this time point. The presence of the two bacteria significantly upregulated the expression of one galactose oxidase, one GH79-like enzyme, one multidrug transporter, one laccase-like protein (AA1 family) and two bilirubin oxidases, suggesting that inter-kingdom interactions (e.g. amensalism) take place within this microbial consortium. Overexpression of multicopper oxidases indicated that strain 2T2.1 may be involved in lignin depolymerization (a trait of enzymatic synergism), while S. multivorum and C. freundii have the metabolic potential to deconstruct arabinoxylan. Under the conditions applied, 2T2.1 appears to be a better degrader of wheat straw when the two bacteria are absent. This conclusion is supported by the observed suppression of its (hemi)cellulolytic arsenal and lower degradation percentages within the microbial consortium.

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          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.
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            RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies

            Motivation: Phylogenies are increasingly used in all fields of medical and biological research. Moreover, because of the next-generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogenetic analyses of large datasets under maximum likelihood. Since the last RAxML paper in 2006, it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community. Results: I present some of the most notable new features and extensions of RAxML, such as a substantial extension of substitution models and supported data types, the introduction of SSE3, AVX and AVX2 vector intrinsics, techniques for reducing the memory requirements of the code and a plethora of operations for conducting post-analyses on sets of trees. In addition, an up-to-date 50-page user manual covering all new RAxML options is available. Availability and implementation: The code is available under GNU GPL at https://github.com/stamatak/standard-RAxML. Contact: alexandros.stamatakis@h-its.org Supplementary information: Supplementary data are available at Bioinformatics online.
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              featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

              Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                FEMS Microbiology Ecology
                Oxford University Press (OUP)
                0168-6496
                1574-6941
                January 01 2020
                January 2020
                November 26 2019
                January 01 2020
                January 2020
                : 96
                : 1
                Affiliations
                [1 ]Microbiomes and Bioenergy Research Group, Department of Biological Sciences, Universidad de los Andes, Carrera 1 No 18A-12, Bogotá, Colombia
                [2 ]Cluster of Microbial Ecology, Groningen Institute for Evolutionary Life Sciences, University of Groningen, Nijenborgh 7 9747AG, Groningen, The Netherlands
                [3 ]Bioenergy Research Unit, National Center for Agricultural Utilization Research, USDA-ARS, Peoria, Illinois 61604, USA
                [4 ] U.S. Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
                [5 ]Bioagricultural Science and Pest Management Department, Colorado State University, Fort Collins, Colorado 80521, USA
                [6 ]Department of Plant and Microbial Biology, University of California Berkeley, Berkeley, California 94720-3102, USA
                Article
                10.1093/femsec/fiz186
                31769802
                f84ad1d7-c922-404f-9a07-dcd9e608459b
                © 2020

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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