Data Parameters and Units:
Sequence data has been submitted to NCBI: Accession number: Bioproject- PRJNA393318, Biosamples: SAMN07200029, SAMN07200030 Sample Name: April Samples, September Samples Environment: Marine Isolation Source: mesocosm sediment Collection date: April 2016, September 2016 ACER_mesocosm_Apr-Sept_otu_table_nochimera_FINAL.csv: Kingdom, Phylum, Class, Family, Genus, Date sampled (April or September), for samples defined by codes. Codes are plantpylotype.treatment.replicatenumber.mesocosmnumber for example: Mono.Control4.M3 - mono culture of Spartina, control treatment, replicate 4, mesocosm
Methods:
Sediments were collected from mesocosms with either black mangrove + a genotypical monoculture Spartina alterniflora (referred to as "monoculture" in the dataset file) or black mangrove + a genotypical polyculture of Spartina alterniflora (referred to as "polyculture" in the dataset file). The mesocosms treated Macondo surrogate obtained through GOMRI, weathered and emulsified [350 ml of weathered oil, 350 ml of seawater] were referred to as "oiled" in the dataset file. Data were collected in triplicates from control and amended mesocosms at three time points, before treatment in September 2015, 6 months after treatment in April 2016, and 11 months after treatment in September 2016. Genomic DNA was extracted from 7 g of sediment using the MP Biomedicals FastDNA Spin Kit (MP Biomedicals, Solon, OH) per the manufacturer’s protocol. DNA concentrations were measured via absorption at 260 nm using a NanoDrop ND-1000 (Thermo Scientific, Beverly, MA). Triplicate samples per treatment condition and plant phyletic group, collected in April and September 2016 were used for amplicon sequencing. Amplicon sequencing of the 16S rRNA gene performed using primers targeting the V3-V4 region (Klindworth et al., 2013) was carried out by HudsonAlpha (Huntsville, AL) on the Illumina MiSeq platform (300 bp paired end). Raw paired end reads from sequencing dataset were merged and quality filtered using USEARCH (Edgar, 2010, Edgar, 2013). QIIME 1.9.1. (Caporaso et al., 2010) was used for downstream analyses unless otherwise mentioned. Operational taxonomic unit (OTU) picking was performed using CD-HIT (Li & Godzik, 2006), a de novo OTU picking algorithm, with a 97% identity threshold. Representative sequences were chosen for each OTU and aligned against the Greengenes database with PyNAST (Caporaso et al., 2010). All OTUs whose representative sequences failed to align were discarded. Chimeric sequences were identified using ChimeraSlayer and filtered before building a phylogenetic tree to estimate microbial diversity of samples. A phylogenetic tree was built from the chimera-free aligned sequences using FastTree (Price et al., 2009), and taxonomy was assigned to each representative sequence using the RDP classifier (Wang et al., 2007). The number of sequences per sample ranged from 228,201 to 671,815. To obtain an equal number of sequences across samples, the amplicon OTU table was subsampled to an even depth of 228,201 randomly selected sequences per sample. Richness (number of observed OTUs), Faiths phylogenetic diversity, Simpson diversity indices, beta diversity using weighted Unifrac metric was generated using QIIME. Functional gene abundances of the sediment samples, KEGG pathway functions, and taxonomic contributions were predicted from the 16S rRNA profiles using the phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) tool (Langille et al., 2013). The Nearest Sequenced Taxon Index (NSTI) for each sample, and 95% confidence intervals for each gene category was calculated to quantify gene content prediction accuracy. Genomic DNA extracted from sediments will also be used to PCR amplify, clone and sequence nitrogen and sulfur cycling genes to determine genetic variability and gene phylogeny of sediment samples.
Provenance and Historical References:
Klindworth, A., Pruesse, E., Schweer, T., Peplies, J., Quast, C., Horn, M., and F.O. Glöckner (2013). Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Research, 41(1): e1. doi: 10.1093/nar/gks808 R.C. Edgar (2010). Search and clustering orders of magnitude faster than BLAST. Bioinformatics, 26(19): 2460-2461 doi:10.1093/bioinformatics/btq461 R.C. Edgar (2013). UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature Methods, 10: 996-998 doi:10.1038/nmeth.2604 Caporaso, J.G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F.D., Costello, E.K., Fierer, N., Peña, A.G., Goodrich, J.K., Gordon, J.I., Huttley, G.A., Kelley, S.T., Knights, D., Koenig, J.E., Ley, R.E., Lozupone, C.A., McDonald, D., Muegge, B.D., Pirrung, M., Reeder, J., Sevinsky, J.R., Turnbaugh, P.J., Walters, W.A., Widmann, J., Yatsunenko, T., Zaneveld, J., and R. Knight (2013). QIIME allows analysis of high-throughput community sequencing data. Nature Methods, 7: 335-336 doi:10.1038/nmeth.f.303 W. Li and A. Godzik (2006). Cd-hit: a fast program for clustering and comparing large sets of protein and nucleotide sequences. Bioinformatics, 22(13): 1658-1659 doi:10.1093/bioinformatics/btl158 Price, M.N., Dehal, P.S., and A.P. Arkin (2009). FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Molecular Biology and Evolution, 26(7): 1641-1650 doi:10.1093/molbev/msp077 Wang, Q., Garrity, G.M., Tiedje, J.M., and J.R. Cole (2007). Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Applied and Environmental Microbiology, 73(16): 5261-5267 doi:10.1128/AEM.00062-07 Langille, M.G.I, Zaneveld, J., Caporaso, J.G., McDonald, D., Knights, D., Reyes, J.A., Clemente, J.C., Burkepile, D.E., Vega Thurber, R.L., Knight, R., Beiko, R.G., and C. Huttenhower (2013). Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nature Biotechnology, 31: 814-821 doi:10.1038/nbt.2676
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