public read 1470531478825 These datasets are described in Walter et al. PLoS One. 2016 [accepted]. DOI: 10.1371/journal.pone.0161168. "Taxonomic and Functional Metagenomic Signature of Turfs in the Abrolhos Reef System (Brazil)". Turfs are widespread assemblages (consisting of microbes and algae) that inhabit reef systems. They are the most abundant benthic component in the Abrolhos reef system (Brazil), representing greater than half the coverage of the entire benthic community. Their presence is associated with a reduction in three-dimensional coral reef complexity and decreases the habitats available for reef biodiversity. Despite their importance, the taxonomic and functional diversity of turfs remain unclear. We performed a metagenomics and pigments profile characterization of turfs from the Abrolhos reefs. Turf microbiome primarily encompassed Proteobacteria (mean 40.57% ± s.d. 10.36, N = 1.548,192), Cyanobacteria (mean 35.04% ± s.d. 15.5, N = 1.337,196), and Bacteroidetes (mean 11.12% ± s.d. 4.25, N = 424,185). Oxygenic and anoxygenic phototrophs, chemolithotrophs, and aerobic anoxygenic phototrophic (AANP) bacteria showed a conserved functional trait of the turf microbiomes. Genes associated with oxygenic photosynthesis, AANP, sulfur cycle (S oxidation, and DMSP consumption), and nitrogen metabolism (N2 fixation, ammonia assimilation, dissimilatory nitrate and nitrite ammonification) were found in the turf microbiomes. Principal component analyses of the most abundant taxa and functions showed that turf microbiomes differ from the other major Abrolhos benthic microbiomes (i.e., corals and rhodoliths) and seawater. Taken together, these features suggest that turfs have a homogeneous functional core across the Abrolhos Bank, which holds diverse microbial guilds when comparing with other benthic organisms. Fasta files are available at https://marinebiodiversity.lncc.br/files/index.php/s/3HqRMATuGDQWDU6 Abrolhos Bank Coral Reefs Metagenome Turf Abrolhos reefs -38.625 -36.5 -17.0 -19.0 Prof Fabiano Thompson Federal University of Rio de Janeiro Assistent Professor

Av. Carlos Chagas Fo. S/N - CCS - IB - BIOMAR - Lab. de Microbiologia - BLOCO A (Anexo) A3 - sl 102 Cidade Universitária Rio de Janeiro Rio de Janeiro 21941-599 Brazil

+552139386567 fabianothompson1@gmail.com MsC Juline Walter Federal University of Rio de Janeiro PhD student

Av. Carlos Chagas Fo. S/N - CCS - IB - BIOMAR - Lab. de Microbiologia - BLOCO A (Anexo) A3 - sl 102 Cidade Universitária Rio de Janeiro Rio de Janeiro 21941-599 Brazil

+552139386567 juline.walter@gmail.com

DNA Sample Preparation Kit (Illumina, San Diego, CA, USA). The size distribution of the libraries was evaluated using a 2100 Bioanalyzer (Agilent, Santa Clara, CA, USA), and DNA quantification was obtained using 7500 Real Time PCR (Applied Biosystems, Foster City, CA, USA) and KAPA Library Quantification Kits (Kapa Biosystems, Wilmington, MA, USA). Paired-end sequencing (2 × 250 bp) was performed on a MiSeq machine (Illumina, San Diego, CA, USA).

The fastq files generated by Illumina sequencing were qualitatively evaluated using FASTQC (v.0.11.2, http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) [32]. The sequences were preprocessed with PRINSEQ (v0.20.4, http://edwards.sdsu.edu/cgi-bin/prinseq/prinseq.cgi) [33] to remove low quality DNA sequences (Phred score < 30), duplicates, and short sequences (< 35 bp). Paired-ended Illumina reads were merged using She-Ra software with default parameters and a quality metric score of 0.5 [34]. Sequence annotation was conducted via Metagenome Rapid Annotation using the Subsystem Technology (MG-RAST) webserver (http://metagenomics.nmpdr.org/) [35], using the following cut-off parameters: e-value lower of 1e-5, 60% of minimum sequence identity and at least 15 bp alignment length. Taxonomic annotation was performed using the GenBank database, (http://www.ncbi.nlm.nih.gov/) and functional annotation was completed using the SEED database [36]. The statistical analyses were performed with R version 3.0.3 [37], except where indicated. The abundance and multivariate figures were plotted with the ggplot2 and reshape packages [38], [39]. To test the hypotheses that the taxonomic (H1) and functional (H2) composition of the turf are conserved in space and time, and that the abundance of key genes (e.g., photosynthesis and chemosynthesis) are different among turf and other benthic holobionts (corals and rhodoliths) and seawater (H3), Permutational Multivariate Analysis of Variance (PERMANOVA) was performed using the “adonis” function of Vegan package [40] (Bray-Curtis distances and 999 permutations). A collection of 22 metagenomes corresponding to corals, rhodoliths and seawater were retrieved from MG-RAST: eight metagenomes from coral (healthy and diseased) [41], six from rhodolith [42], [43], and eight from seawater [14] (Table 1). All the metagenome samples were from the Abrolhos Bank (Table 1) and were annotated with same databases to diminish possible annotation biases.