dataesr/scientific-paragraphs-categorization huggingface dataset:
In contrast to the sulfur AMGs, phylogenies suggest that P-II AMGs originated from diverse viruses (6 VCs including the abundant VC_2 and VC_12), and were acquired at least 4 times independently from different host phyla (Bacteroidetes, Proteobacteria, and possibly Verrucomicrobia, Extended Data Fig. 7 ). Finally, while a single amoC AMG offers only preliminary evaluation of its evolutionary history, this amoC-encoding contig appears to represent novel and rare unclassified archaeal dsDNA viruses (VC_623), which presumably infect ammoniaoxidizing Thaumarchaeota, known for their major role in global nitrification  37  (Extended Data Fig. 8 ). Finally, we investigated the ecology of viruses encoding these new AMGs by mapping their distribution across the GOV dataset. Seven AMG clades were geographically restricted (DsrC-unc, DsrC-1, DsrC-2, DsrC-4, P-II-2, P-II-3, and amoC), and 5 were widespread throughout the epipelagic (DsrC-3, DsrC-5, SoxYZ, P-II-1) or mesopelagic (P-II-4) ocean (Fig.
Overall, this systematically collected and processed GOV dataset brings critically-needed and unprecedented global ecological context to new and known, surface and deep ocean viruses. Global diversity analyses identified and mapped abundant dsDNA viruses at the population-and VC-level, and indicated that these are near-completely sampled in epipelagic waters and dominated by few viral groups, mostly newly described. The characterization of new viral-encoded AMGs, their viral carriers, possible impacted hosts, and biogeographical patterns revealed that viral manipulation of cellular processes involves much more than photosynthesis and carbon metabolism  [25] [26] [27] [28]  , to also now include nitrogen and sulfur cycling throughout the epipelagic ocean. These advances are foundational for interpretation of new (meta)genomic datasets and selection of relevant experimental systems to develop, and, together with myriad experimental, informatic and theoretical advances already occurring  5, 15, [42] [43] [44]  , will accelerate the field towards understanding and predicting the roles and global impacts of viruses in nature.
Sequencing was done on a HiSeq 2000 system (101 bp, paired end reads) at the Genoscope facilities (Paris, France). Temperature, salinity, and oxygen data were collected from each station using a CTD (Sea-Bird Electronics, Bellevue, WA, USA; SBE 911plus with Searam recorder) and dissolved oxygen sensor (Sea-Bird Electronics; SBE 43). Nutrient concentrations were determined using segmented flow analysis  49  and included nitrite, phosphate, nitrite plus nitrate, and silica. Nutrient concentrations below the detection limit (0. 02 µmol kg -1 ) are reported as 0. 02 µmol kg -1. All data from the Tara Oceans expedition are available from ENA (for nucleotide) and from PANGAEA (for environmental, biogeochemical, taxonomic and morphological data)  [50] [51] [52].
Dataset generation Contigs assembly An overview of the contigs generation process is provided in Supplementary Fig. 7. The first step consisted in the generation of a set of contigs using as many reads as possible from 104 oceanic viromes, including 74 epipelagic and 16 mesopelagic samples from the Tara Oceans expedition  6  , and 1 mesopelagic and 13 bathypelagic from the Malaspina expedition  7. This set of contigs was generated through an iterative cross-assembly  15  (using MOCAT  54  and Idba_ud 55 , Supplementary Fig. 7 ) as follows: (i) high-quality (HQ) reads were first assembled sample by sample with the MOCAT pipeline as described in  21  , (ii) all reads not mapping (Bowtie 2  56  , options --sensitive, -X 2000, and --nondeterministic, other parameters at default) to a MOCAT contig (by which we denote 'scaftigs', that is, contigs that were extended and linked using the paired-end information of sequencing read  57  ) were assembled sample by sample with Idba_ud (iterative k-mer assembly, with k-mer increasing from 20 to 100 by step of 20), (iii) all reads remaining unmapped to any contig were then pooled by Longhurst province (i.
Dataset of publicly available viral genomes and genome fragments Genomes of viruses associated with a bacterial or archaeal host were downloaded from NCBI RefSeq (1,680 sequences, v70, 05-26-2015). To complete this dataset of reference genomes, viral genomes and genome fragments available in Genbank, but not yet in Refseq were downloaded (July 2015) and manually curated to select only bacterial and archaeal viruses (1,017 sequences). These included viral genomes not yet added to RefSeq, as well as genome fragments from fosmid libraries generated from seawater samples  12, 13. Mycophage sequences (available from http://phagesdb. org  62 ) and not already in RefSeq were downloaded (July 2015) and included as well (734 sequences). Finally, 12,498 viral genome fragments from the VirSorter Curated Dataset, identified in publicly available microbial genome sequencing projects, were added to the database  9.
Genome (fragments) clustering through gene-content based network analysis Proteins predicted from 14,650 large GOV contigs (≥10kb and ≥10 genes), were added to all proteins from the publicly available viral genomes and genomes fragments gathered, and compared through all-vs-all blastp, with a threshold of 10 -5 on e-value and 50 on bit score. Protein clusters were then defined using MCL (using default parameters for clustering of proteins, similarity scores as log-transformed e-value, and MCL inflation of 2  63  ). vContact (https://bitbucket. org/MAVERICLab/ vcontact) was then used to calculate a similarity score between every pair of genome and/or contigs based on the number shared of PCs between the two sequences (as in  8, 9  ), and then compute a MCL clustering of the genomes/contigs based on these similarity scores (thresholds of 1 on similarity score, MCL inflation of 2).
Viral contigs annotation A functional annotation of all GOV predicted proteins was based on a comparison to the PFAM domain database (v27  64  ) with HmmSearch  65  (threshold of 30 on bit score and 1e-3 on e-value), and additional putative structural proteins were identified through a BLAST comparison to protein clusters detected in viral metaproteomics dataset  66. This metaproteomics dataset led to the annotation of 13,547 hypothetical proteins lacking a PFAM annotation. A taxonomic annotation was performed based on a blastp of the predicted proteins against proteins from archaeal and bacterial viruses from NCBI RefSeq and Genbank (threshold of 50 on bit score and 10 -3 on e-value). VCs were affiliated based on isolate genome members, when available. When multiple isolates were included in the VC, the VC was affiliated to the corresponding subfamily or genus of these isolates (excluding all "unclassified" cases).
"Phage proteomic tree" (i. e. "whole-genome comparison tree") computation and visualization All publicly available complete genomes (see above), all complete (circular) and near-complete (extrachromosomal genome fragment >50kb with a terminase) from the VirSorter Curated Dataset, and all complete and near-complete GOV contigs were compared to generate a phage proteomic tree, as previously described  12, 73. Briefly, a proteomic similarity score was calculated for each pair of genome based on a all-vs-all tblastx similarity as the sum of bit scores of significant hits between two genomes (e-value ≤ 0. 001, bit score ≥30, identity percentage ≥ 30). To normalize for different genome sizes, each genome was also compared to itself to generate a self-score, and the distance between two different genomes was calculated as a Dice coefficient (as in  12  ), i.
The selection of abundant VCs within a sample was based on the contribution of the VC to the sample diversity as measured by the Simpson index. For each sample, the overall Simpson index was first calculated with all VCs. Then, VCs were sorted by decreasing relative abundance and progressively added to a new calculation of the Simpson index. VCs considered as abundant were the ones which, once cumulated, represented 80% of the sample diversity (i. e. a Simpson index greater or equal to 80% of the sample total Simpson index, Extended Data Fig. 1C ). The 38 VCs identified as abundant in at least 2 different stations were selected as "recurrently abundant VCs in the GOV dataset" (Fig. 2  and Extended Data Fig. 3 ).
Host prediction and diversity A genome database of putative hosts for the epi-and mesopelagic GOV viruses was generated, including all archaea and bacteria genomes annotated as "marine" from NCBI RefSeq and WGS (both times only sequences ≥5kb, 184,663 sequences from 4,452 genomes, downloaded in August 2015), and all contigs ≥5kb from the 139 Tara Oceans microbial metagenomes corresponding to the bacteria and archaea size fraction (791,373 sequences)  21. For these microbial metagenomic contigs, a first blastn was computed to compare them to all GOV contigs, and exclude from the putative host dataset all metagenomic contigs with a significant similarity to a viral GOV sequence (thresholds of 50 on bit score, 0. 001 on e-value, and 70% on identity percentage) on ≥90% of their length, as these are likely sequences of viral origin sequenced in the bacteria and archaea size fraction (these represented 2.
Nucleotide composition similarity: comparison of tetranucleotide frequency Bacterial and archaeal viruses tend to have a genome composition close to the genome composition of their host, a signal that can be used to predict viral-host pairs  9, 76. Here, canonical tetranucleotide frequencies were observed for all viral and host sequences using Jellyfish  80  , and mean absolute error (i. e. average of absolute differences) between tetranucleotide frequency vectors were computed with in-house Perl and Python scripts for each pair of viral and host sequence. A GOV viral contig was then assigned to the closest sequence (i. e. lowest distance d) from the pool of NCBI genomes if d<0. 001 (because both the tetranucleotide frequency signal and the taxonomic affiliation of these complete genomes are more robust than for metagenomic contigs), and otherwise assigned to the closest (i.
Summarizing host prediction at the VC level Overall, 3,675 GOV contigs could be linked to a putative host group among the 24,353 GOV contigs associated with an epi-or mesopelagic viral population. To summarize these affiliations at the VC level, a Poisson distribution was used to estimate the number of expected false positive associations for each VC -host group combination based on (i) the global probability of obtaining a host prediction across all pairs of viral and host sequences tested and for all methods (p=5. 8x10 -0  ), (ii) the number of potential predictions generated for the VC, corresponding to 3 times the number of sequences in the VC (to take into account the three methods), and (iii) the number of sequences from the host group in the database. By comparing the number of links observed between a VC and a host group to this expected value, which takes into account the bias in database (i.
Identification and annotation of putative AMGs Detection of AMGs Predicted proteins from all GOV viral contigs were compared to the PFAM domain database (hmmsearch  65  , threshold of 40 on bit score and 0. 001 on e-value), and all PFAM domains detected were classified into 8 categories: "structural", "DNA replication, recombination, repair, nucleotide metabolism", "transcription, translation, protein synthesis", "lysis", "membrane transport, membraneassociated", "metabolism", "other", and "unknown" (as in  24  ). Four AMGs (i. e. similar to a domain from the "metabolism" category) were then selected for further study because of their central role in sulfur (dsrC and soxYZ) or nitrogen (P-II, amoC) cycle, and the fact that these had never been detected in a surface ocean viral genome so far (dsrC/tusE-like genes have been detected in deep water viruses  14, 29  ).
Phylogenetic tree generation and contigs map comparison Sequences similar to these AMGs were recruited from the Tara Oceans microbial metagenomes 21 based on a blastp of all predicted proteins from microbial metagenome to the viral AMGs identified (threshold of 100 on bit score, 10 -5 on e-value, except for P-II where a threshold of 170 on bit score was used because of the high number of sequences recruited). The viral AMG sequences were also compared to NCBI nr database (blastp, threshold of 50 on bit score and 10 -3 on e-value) to recruit relevant reference sequences (up to 20 for each viral AMG sequence). These sets of viral AMGs and related protein sequences were then aligned with Muscle  83  , the alignment manually curated to remove poorly aligned positions with Jalview  84  , and two trees were computed from the same curated alignment: a maximum-likelihood tree with FastTree (v2.
Functional characterization of putative AMGs Conserved motifs were identified on the different AMGs based on the literature: dsrC conserved motifs were obtained from ref. 33, soxYZ conserved residues were identified from the PFAM domains PF13501 and PF08770, and P-II conserved motifs from PROSITE documentation PDOC00439. A 3D structure could also be predicted for P-II AMGs by I-TASSER  89  (default parameters), the quality of these predictions being confirmed with ProSA web server  90. To further confirm the functionality of these genes, selective constraint on these AMGs was evaluated through pN/pS calculation, as in ref. 91. Briefly, synonymous and non-synonymous SNPs were observed in each AMG, and compared to expected ratio of synonymous and non-synonymous SNPs under a neutral evolution model for this genes.
The interpretation of pN/pS is similar as for dN/dS analyses, with the operation of purifying selection leading to pN/pS values < 1. Finally, AMG transcripts were searched in metatranscriptomic datasets generated through the Tara Oceans consortium (ENA Id ERS1092158, ERS488920, and ERS494518). Briefly, For 0. 2-1. 6 and 0. 22-3µm filters, bacterial rRNA depletion was carried out on 240-500 ng total RNA using Ribo-Zero Magnetic Kit for Bacteria (Epicentre, Madison, WI). The Ribo-Zero depletion protocol was modified to be adapted to low RNA input amounts  92. Depleted RNA was used to synthetize cDNA with SMARTer Stranded RNA-Seq Kit (Clontech, Mountain View, CA)  92. Metatranscriptomic libraries were quantified by qPCR using the KAPA Library Quantification Kit for Illumina Libraries (KapaBiosystems, Wilmington, MA) and library profiles were assessed using the DNA High Sensitivity LabChip kit on an Agilent Bioanalyzer (Agilent Technologies, Santa Clara, CA).
Scripts and data availability Scripts used in this manuscript are available on the Sullivan lab bitbucket under project "GOV_Ecogenomics" (https://bitbucket. org/MAVERICLab/gov_ecogenomics). All raw reads are available through ENA (Tara Oceans) or JGI (Malaspina) using the dataset Ids listed in Supplementary Table  1. Processed data are available through iVirus, including all sequences from assembled contigs, list of viral populations and associated annotated sequences as genbank files, viral clusters composition and characteristics, map comparisons of genomes and contigs of the 38 abundant VCs, and host predictions for viral contigs.
Extended Data Figure Extended Data Figure  3 : Summary of 34 of the 38 abundant viral clusters (VCs, the 4 other abundant VCs being the ubiquitous ones presented in Fig. 2 ). Predicted genome size is based on the set of isolates and circular contigs in the VC (NA corresponds to VCs without any circular contigs, or for which the relative standard deviation of estimated genome size across the different isolate(s) and/or circular contigs is greater than 15%). Host association values are based on the number of cluster members associated with each host group, the statistical significance of this number of predictions being evaluated by comparison with an expected number of associations calculated from a Poisson distribution. Host associations based on known isolates are indicated with a star (for associations based on cultivated isolates) or a dot (for associations based on the detection of a cluster member in a microbial genome from the VirSorter Curated Dataset).
e. including both Alpha and Beta diversity), (iv) the distribution of Chao indexes by sample for this group (Alpha diversity), and (v) the average Sorensen index between pairs of samples including at least one OTU of this group (Beta diversity). OTU counts were derived from the 109 epipelagic microbial metagenomes described in  21. B. Pearson correlations between host group relative abundance or diversity indexes (Global Chao, Average Chao across samples, and Average Sorensen across samples) and the number of VCs. Extended Data Figure  5 : Diversity, distribution, and genome context of dsrC genes in GOV contigs. A. Maximum-likelihood tree (from an amino-acid alignment) including the 11 viral DsrC and microbial sequences from microbial metagenomes and NCBI nr database. The presence of conserved C residues (named Cys-A & Cys-B, as in  33  ) is indicated with color circles next to each sequence or clade, and the corresponding type of DsrC-like protein is indicated by coloring the branch or clade.
Extended Data Figure  6 : Diversity, distribution, and genome context of soxYZ genes in GOV contigs. A. Bayesian tree (from an amino-acid alignment) including the 4 viral SoxYZ and microbial sequences from microbial metagenomes and NCBI nr database. The affiliation of microbial clades (either from the NCBI reference or from the LCA affiliation of metagenomic contigs) is indicated by coloring of the grouped clades or with a colored square next to the sequence. Viral AMG sequences are highlighted in blue, posterior probabilities are represented by proportional circles (all nodes with posterior probability < 0. 40 were collapsed). Clades including sulfur-oxidation proteobacteria are indicated on the tree. Each soxYZ AMG is associated with an abundance profile (on the right) displaying the relative abundance of the contig across the 91 epi-and mesopelagic samples (based on normalized coverage, i.
e. contig coverage / Gb of metagenome). B. Comparison of soxYZ-containing contigs maps. For contig GOV_bin_4310_contig-100_0, the second largest contig from the same bin (GOV_bin_4310_contig-100_1) is displayed. T4-like marker genes (PhoH, Gp23 and T4 baseplate) are indicated on the maps, alongside putative AMGs (Fe-S biosyn: Iron-sulfur cluster biosynthesis). Extended Data Figure  7 : Diversity, distribution, and genome context of P-II genes in GOV contigs. A. Maximum-likelihood tree (from an amino-acid alignment) including the 10 viral P-II and microbial sequences from microbial metagenomes and NCBI nr database. The affiliation of microbial clades (either from the NCBI reference or from the LCA affiliation of metagenomic contigs) is indicated by coloring of the grouped clades or with a colored square next to the sequence.
Diversity, distribution, and genome context of amoC gene in GOV contigs. A. Maximum-likelihood tree (from an amino-acid alignment) including the GOV amoC AMG and microbial sequences from microbial metagenomes and NCBI nr database. The affiliation of microbial clades (either from the NCBI reference or from the LCA affiliation of metagenomic contigs) is indicated by coloring of the grouped clades or with a colored square next to the sequence. Viral AMG sequence is highlighted in blue, internal nodes SH-like supports are represented by proportional circles (all nodes with support < 0. 40 were collapsed). B. Abundance profile displaying the relative abundance of the contig across the 91 epi-and mesopelagic samples (based on normalized coverage, i. e. contig coverage / Gb of metagenome). C. Map of the amoC-containing contig.
Acknowledgements We thank  Joshua Weitz  for advice on statistics,  Claus Pelikan  for help with the DsrAB phylogenetic tree,  Christiane Dahl  for valuable discussion regarding DsrC function, and members of the  Sullivan and Rich labs  for suggestions and comments on this manuscript. We acknowledge support from UA highperformance computing and the  Ohio Supercomputer Center. This viral research was funded by a  National Science Foundation  grant (# 1536989 ) and  Gordon and Betty Moore Foundation  grants (# 3790 , # GBMF2631 ) to MBS. SR was partially supported by the  University of Arizona Technology and Research Initiative Fund  through the  Water, Environmental and Energy Solutions Initiative  and the  Ecosystem Genomics Institute. BED was supported by the  Netherlands Organization for Scientific Research (NWO) Vidi  grant  864.
Leaves of either J. parasitica or of T. speciosa were offered ad libitum, and larvae were checked daily for food replacement and cleaning, as well as survival. Duration of larval development was recorded and pupae were weighed the morning following pupation using a small portable scale. A generalized linear model (GzLM) was done using SPSS (SPSS Inc. , Chicago, Illinois) to determine if the number of eggs laid on either J. parasitica or T. speciosa in the choice experiment was affected by either the Melinaea species tested or the host plant, and to test for a possible interaction between these two factors. Because the number of eggs for most of the species included many zero counts, a negative binomial distribution was used. The total number of eggs laid on both T. speciosa in the no-choice experiment was compared between Melinaea species using a non-parametric Kruskal-Wallis test.
Acknowledgments We thank the Peruvian authorities and  Dr. Gerardo Lamas  ( Museo de Historia Natural, Univesidad Mayor de San Marcos ) for research permits ( 236-2012-AG-DGFFS-DGEFFS  and  201-2013-MINAGRI-DGFFS/DGEFFS ), and  Mathieu Chouteau  and  Ronald Mori-Pezo  for their precious help in the field.
AORTIC has developed tangible products and activities to assist in the implementation of the general concepts of this plan. First, AORTIC produced a Handbook for Cancer Research in Africa that provides a general outline of Africa-specific research approaches for cancer  [9]. Topics that were identified as being key to the success of cancer research in Africa that were included in this volume were basic research principles, career considerations, developing and maintaining research partnerships, responsible conduct of research, research funding, community engagement research, biosampling and biobanking, pathology, data management and analysis, clinical trials, research advocacy, and research dissemination. Second, as specified in the cancer plan, AORTIC has initiated research training workshops on cancer in Africa.
In addition to these immediate goals, ACRA will improve research infrastructure in Africa, establish a trained African cancer research workforce, and generate data that impact on the health of populations in Africa and worldwide. The implementation of ACRA will involve identification of research "hubs" that have general capacity for cancer research including epidemiology, laboratory methods, clinical capacity, and other features. Characteristics of these hubs are being developed and hubs will be identified throughout Africa. There are a number of examples of research networks upon which the ACRA network will be built, and from which research hubs could be identified. Three existing prostate cancer networks serve as a focus around which urological cancer research may be formed: the Men of African Descent and Carcinoma of the Prostate (MAD-CaP) network, The African Caribbean Cancer Consortium (AC3), and the Prostate Cancer Transatlantic Consortium (CaPTC).