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Supplementary Methods
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Sampling, DNA extraction, and sequencing
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Over 80 peat cores were collected in July 2012 from the Bog Lake fen (Fen, 47° 30' 22.62", -
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93° 29' 20.46") and S1 bog (47° 30' 22.3914", -93° 27' 11.772") sites in the Marcell
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Experimental Forest (MEF) in northern Minnesota, USA. Biogeochemical characterization
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(microbial community composition, functional potential, and organic matter properties, etc.) of
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these samples has been described elsewhere (Lin et al 2014a, Lin et al 2014b, Tfaily et al 2014).
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Two 75-100 cm depth intervals from Bog Lake Fen and S1 bog T3M (a mid-point of the transect
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#3) sites were selected for metagenomic sequencing because of the high proportion (up to 60%
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of total community) of Archaea detected in these soils based on quantitative real time PCR and
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amplicon sequencing data (Lin et al 2014b). The bog and fen sites differed in their vegetation
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cover, porosity of peat column, organic matter properties, and thus microbial community
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composition (Lin et al 2014b, Tfaily et al 2014). The bog is an acidic (pH 3.5-4.0) and nutrient-
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deficient environment that receives water inputs primarily from precipitation. In contrast, Bog
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Lake Fen (pH≈4.5-4.8) is a poor fen, which contains a higher coverage of vascular plants along
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with Sphagnum. Core sections were homogenized and subsampled in sterile bags. Samples for
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DNA extractions were frozen at -20oC within 2 hours of sampling and then transferred to a -80oC
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freezer at the end of the sampling day.
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Genomic DNA was extracted from triplicate peat soil subsamples using a MoBio PowerSoil
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DNA extraction kit (MoBio, Carlsbad, CA) following the manufacturer’s protocol and using 0.5
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g of peat per extraction. Extracted DNA samples were pooled. Libraries for metagenomic
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sequencing were generated using the Nextera DNA sample preparation kit (Illumina, Inc. San
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Diego, CA). Libraries were size-selected using E-Gels (Life Technologies, Inc.) for an insert size
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range of 400-800 bp. Libraries were then quantified and quality checked using the Invitrogen
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Qubit and Agilent Bioanalyzer. All libraries were pooled in an equimolar ratio and sequenced
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using an Illumina HiSeq2000 instrument, generating paired-end reads of 150 bases in length.
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Metagenome assembly, binning, and annotation
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Low quality reads were trimmed using Sickle (v. 1.29) with a quality score threshold of Q=3
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(Joshi NA and Fass JN, unpublished, https://github.com/najoshi/sickle), and then assembled
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using IDBA_ud v. 1.0.9 (Peng et al 2012), with a min and max kmer size of 40 and 70,
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respectively, and a 5 kmer step size. Contigs greater than 2 kb were retained for further analysis,
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and taxonomic binning. The coverage of each contig was determined by mapping reads using
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Bowtie v. 1.0.0 with default settings (Langmead et al 2009). The clustering of contigs into
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genome bins was based on agreements among contig tetranucleotide frequency composition,
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read coverage, G+C content, and predicted protein UBLAST (usearch v. 7.0.1001, Edgar RC,
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unpublished, http://drive5.com/usearch/) hits to the UniProt UniRef90 database, following gene
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prediction with Prodigal in metagenomics mode (Hyatt et al 2012). To aid binning, Emergent
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Self Organizing Maps (ESOM) were created using Databionics ESOM tools (http://databionic-
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esom.sourceforge.net/) and tetranucleotide frequencies predicted after fragmenting contigs into 5
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kb lengths (Dick et al 2009). Contigs fragmented into 2 kb lengths were then projected onto to
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these maps. Genome completeness was determined by searching for 31 bacterial single copy
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marker genes or 104 archaeal single copy marker genes, using the ‘MarkerScanner.pl’ script of
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AMPHORA2 (Wu and Scott 2012).
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Each assembled genome was uploaded to the Integrated Microbial Genomes (IMG) system at
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DOE's Joint Genome Institute (JGI) for annotation (Markowitz et al 2014). For comparison,
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genome sequences were also uploaded to the RAST server and annotated by RAST v4.0
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(Overbeek et al 2014). Metabolic reconstruction and pathway mapping of each assembled
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genome was completed using the KEGG Automatic Annotation Server (KAAS) (Moriya et al
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2007). Identification of glycoside hydrolases (GH) was performed by searching against the Pfam
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database using HMMER v.3.0 (Finn et al 2014), including targets of GH families, 66
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carbohydrate active enzymes, 34 carbohydrate binding modules, 3 polysaccharide lyases, and 5
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carbohydrate esterases as listed in (Tveit et al 2013). Representative genes in fermentation
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pathways were counted to determine the genomic potential for producing lactate, H2, ethanol,
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propionate, CO2, acetate and butyrate by fermentation (Kirchman et al 2014).
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Phylogenetic analysis of 16S rRNA genes and concatenated marker genes
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Expectation maximization iterative reconstruction of genes from the environment (EMIRGE)
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was used to reconstruct full-length 16S rRNA gene sequences from the metagenomic data
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(Miller et al 2011), with sequence clustering at 97% identity. Taxonomy was determined by
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searching representative sequences against a dereplicated version of the SILVA 108 databases
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using SINA v1.2.11 (Miller et al 2011, Pruesse et al 2012). The 16S rRNA gene fragments
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present in contigs were detected and retrieved by using Metaxa (Bengtsson et al 2011). 16S
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rRNA gene sequences reconstructed by EMIRGE were matched to genomes by aligning 16S
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rRNA gene sequences from both EMIRGE and Metaxa using SINA v1.2.11 (Pruesse et al 2012),
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along with sequences from their closest relatives and sequences previously found in amplicon
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sequencing from the same sample (Lin et al 2014b). MEGA6 (Tamura et al 2013) was used to
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construct the Maximum Likelihood tree using the Kimura 2-parameter model for nucleotide
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evolution, with a bootstrap test of 500 replication. The tree branches of Crenarchaeota and
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Thaumarchaeota were clustered and collapsed into taxonomic groups according to the
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crenarchaeotal taxonomic framework used in (Ochsenreiter et al 2003) and (Kubo et al 2012).
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To construct a concatenated core gene tree for the two near complete genome bins, a distance
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tree was first created based on 16S rRNA gene sequences from genomes of all Crenarchaeota,
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Thaumarchaeota, and Thermoplasmata available in the IMG database (Markowitz et al 2014).
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This 16S tree was used to identify the closest neighbors for the two near complete genomes.
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Genome sequences of the two assembled genomes and their closest neighbors were then
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downloaded and searched against 104 archaeal marker proteins using AMPHORA2. Marker
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proteins identified in each downloaded genome were aligned and concatenated after removing
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unaligned fragments. All aligned and concatenated protein sequences of each genome were then
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used to construct the concatenated core gene tree using FastTree v2.1.3 with default parameters
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(Price et al 2010).
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