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      Novel taxa of Acidobacteriota implicated in seafloor sulfur cycling

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          Abstract

          Acidobacteriota are widespread and often abundant in marine sediments, yet their metabolic and ecological properties are poorly understood. Here, we examined metabolisms and distributions of Acidobacteriota in marine sediments of Svalbard by functional predictions from metagenome-assembled genomes (MAGs), amplicon sequencing of 16S rRNA and dissimilatory sulfite reductase ( dsrB) genes and transcripts, and gene expression analyses of tetrathionate-amended microcosms. Acidobacteriota were the second most abundant dsrB-harboring (averaging 13%) phylum after Desulfobacterota in Svalbard sediments, and represented 4% of dsrB transcripts on average. Meta-analysis of dsrAB datasets also showed Acidobacteriota dsrAB sequences are prominent in marine sediments worldwide, averaging 15% of all sequences analysed, and represent most of the previously unclassified dsrAB in marine sediments. We propose two new Acidobacteriota genera, Candidatus Sulfomarinibacter (class Thermoanaerobaculia, “subdivision 23”) and Ca. Polarisedimenticola (“subdivision 22”), with distinct genetic properties that may explain their distributions in biogeochemically distinct sediments. Ca. Sulfomarinibacter encode flexible respiratory routes, with potential for oxygen, nitrous oxide, metal-oxide, tetrathionate, sulfur and sulfite/sulfate respiration, and possibly sulfur disproportionation. Potential nutrients and energy include cellulose, proteins, cyanophycin, hydrogen, and acetate. A Ca. Polarisedimenticola MAG encodes various enzymes to degrade proteins, and to reduce oxygen, nitrate, sulfur/polysulfide and metal-oxides. 16S rRNA gene and transcript profiling of Svalbard sediments showed Ca. Sulfomarinibacter members were relatively abundant and transcriptionally active in sulfidic fjord sediments, while Ca. Polarisedimenticola members were more relatively abundant in metal-rich fjord sediments. Overall, we reveal various physiological features of uncultured marine Acidobacteriota that indicate fundamental roles in seafloor biogeochemical cycling.

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            Fast and accurate short read alignment with Burrows–Wheeler transform

            Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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              DADA2: High resolution sample inference from Illumina amplicon data

              We present DADA2, a software package that models and corrects Illumina-sequenced amplicon errors. DADA2 infers sample sequences exactly, without coarse-graining into OTUs, and resolves differences of as little as one nucleotide. In several mock communities DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.
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                Author and article information

                Contributors
                alexander.loy@univie.ac.at
                kwasmund@gmail.com
                Journal
                ISME J
                ISME J
                The ISME Journal
                Nature Publishing Group UK (London )
                1751-7362
                1751-7370
                12 May 2021
                12 May 2021
                November 2021
                : 15
                : 11
                : 3159-3180
                Affiliations
                [1 ]GRID grid.10420.37, ISNI 0000 0001 2286 1424, Division of Microbial Ecology, Centre for Microbiology and Environmental Systems Science, , University of Vienna, ; Vienna, Austria
                [2 ]GRID grid.411461.7, ISNI 0000 0001 2315 1184, Department of Microbiology, , University of Tennessee, ; Knoxville, TN USA
                [3 ]GRID grid.10420.37, ISNI 0000 0001 2286 1424, Joint Microbiome Facility of the Medical University of Vienna and the University of Vienna, ; Vienna, Austria
                [4 ]GRID grid.22937.3d, ISNI 0000 0000 9259 8492, Department of Laboratory Medicine, , Medical University of Vienna, ; Vienna, Austria
                [5 ]GRID grid.10420.37, ISNI 0000 0001 2286 1424, Division of Computational Systems Biology, Centre for Microbiology and Environmental Systems Science, , University of Vienna, ; Vienna, Austria
                [6 ]GRID grid.465498.2, Austrian Polar Research Institute, ; Vienna, Austria
                [7 ]GRID grid.5117.2, ISNI 0000 0001 0742 471X, Center for Microbial Communities, Department of Chemistry and Bioscience, , Aalborg University, ; Aalborg, Denmark
                [8 ]GRID grid.421147.5, ISNI 0000 0000 8528 5498, Present Address: Division of Natural Sciences, , Maryville College, ; Maryville, TN USA
                Author information
                http://orcid.org/0000-0002-8236-2384
                http://orcid.org/0000-0002-7442-0599
                http://orcid.org/0000-0002-0846-1202
                http://orcid.org/0000-0002-0592-7791
                http://orcid.org/0000-0003-0914-6375
                http://orcid.org/0000-0001-8923-5882
                http://orcid.org/0000-0001-6706-7291
                Article
                992
                10.1038/s41396-021-00992-0
                8528874
                33981000
                b73f804d-e84d-41f1-bee1-9142334f0a72
                © The Author(s) 2021

                Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 1 October 2020
                : 5 April 2021
                : 15 April 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100002428, Austrian Science Fund (Fonds zur Förderung der Wissenschaftlichen Forschung);
                Award ID: P29426
                Award ID: P25111
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s), under exclusive licence to International Society for Microbial Ecology 2021

                Microbiology & Virology
                environmental microbiology,environmental sciences
                Microbiology & Virology
                environmental microbiology, environmental sciences

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