dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2009). Considering each feature (genus, function) as an OTU, alpha and beta diversity were computed using Mothur software  (Schloss et al. , 2009). UniFrac distances based on shared and unshared features were computed for each compared pair of samples. To inspect how environmental factors impact metacommunity composition, subsampled sites were first plotted based on their feature abundances with non-metric multidimensional scaling (NMDS) using Bray-Curtis distance between samples. Hence, the OPA-nc sample was used as control reference to compute the differential abundance of genera. Then, the computed distance matrix was clustered with Ward's method based on minimum variance. Clusters of genus abundance were distinguished with different colors on the NMDS plot. Next, mixed metals metadata were projected on NMDS axes by fitting a regression model.
The significance of the "regression coefficient" of the model was computed using a random permutation test (1,000 iterations). Then the regression coefficient between the randomized response and the fitted values from the model was computed. The NMDS model was run using the VEGAN package  (Oksanen et al. , 2016)  in the R statistical environment (R Foundation for Statistical  Computing, 2008). To test for a correlation between taxonomical and functional composition, a Canonical Correlation Analysis using the CCA  (González et al. , 2008)  and mixOmics  (Rohart et al. , 2017)  packages in R were applied. With CCA, the function-taxon cross-correlation was computed by maximizing the linear combinations between the two matrix vectors. Then a regularization function of CCA from mixOmics was used to deal with the high number of features (genus, function) compared to the low number of samples (60 subsamples).
ORFs-Based Approach (Figure 1) To improve annotation accuracy in terms of length and coverage, an Open Reading Frame (ORF) prediction approach was used after de novo assembly. Collinear metagenomic reads belonging to the same genetic unit were merged into contiguous sequences (contigs). Firstly, de novo assemblies of raw reads were performed using the RAY Meta  (Boisvert et al. , 2012)  assembler. Secondly, to explore contig features and gene contents, contigs were submitted to the MG-RAST webserver  (Glass et al. , 2010)  and ORF prediction was conducted using the FragGeneScan tool  (Rho et al. , 2010). Afterwards, contigs were annotated with the BLAT tool implemented in MG-RAST against the SEED database using stringent filtering parameters (1e -12 as p-value, 85% identity as threshold, 50 base pairs as minimum alignment length).
Statistical summaries of annotated contigs are available in Supplementary File S2. Customized microbial annotations from the MG-RAST webserver were improved using the RESTful API tool  (Wilke et al. , 2015). An additional similarity research step based on BLASTx (parameters; identity threshold of 85%, e-value of 10 -12 and minimum alignment length of 50 base pairs)  (Camacho et al. , 2009)  was performed on contigs against the BacMet database  (Pal et al. , 2014)  for annotating all polymetallic resistance genes (hereafter termed PMRGs). After annotations, contig coverage information determined by the Ray Meta assembler was added to normalize abundance information. Then, both abundance matrices of taxon and function coverage (both normalized and non-normalized) were analyzed with the STAMP software using a differential proportion comparisons test  (Parks and Beiko, 2010).
In a second additional workflow analysis, the ORFs were locally annotated with Diamond as described above in the "Reads-based approach" section. BLAT and Diamond provided similar annotation results. To measure alpha and beta diversity within and between communities, abundance matrices were adapted for the Mothur software. At the third step, metabolic abundance was analyzed using MG-RAST metabolite annotations. The metabolic differential abundance was surveyed using iPATH  (Yamada et al. , 2011) ; this tool offers the visualization of shared and specific pathways between pairs of samples.
PMRG plot coordinates, clusters and dot labels are shown in Supplementary File S4. Thresholds of 75% of identity, minimum alignment length of 50 base pairs and e-value of 10 -12 parameters were strictly respected. PMRG were annotated by performing Blastn of ORFs against BacMet database using Diamond software. the diazotrophic Cyanothece, and the filamentous Anabaena in TUR-hc. Lake metacommunity abundance shifts were further documented using bootstrapped hierarchical classification and PCA. At the genus level, both methods showed similar pattern of clustering with high statistical support (bootstrap values above 75%; more than 95% of explained variation by the first two PCA components), with BAR-mc, LAR-hc, and TUR-hc grouped separately from OPA-nc, DAS-lc (Figures  2B-D ).
, 2013). To further reinforce the taxonomic composition analysis, we examined genera abundance and ORF distributions. Similar ratios of ORFs/Genus were observed in the five studied metagenomes (Supplementary Figure  S2 ). The number of annotated ORFs in all metagenomes was comparable. Furthermore, random subsampling analysis without replacement produced similar results (slightly different in topology) compared to the ORFs approach, with remarkable clustering fidelity of subsampled replicates from each metagenome (Supplementary Figure  S4a ). Here, the subsampling approach revealed consistency in the molecular signal of each lake. We acknowledge that the subsampling approach used in our analysis cannot replace real biological replications, but it is rather an indicator of the metagenomic data robustness to the metacommunity structure.
ACKNOWLEDGMENTS We thank  Prof. Connie Lovejoy ,  Dr. Anne Dalziel ,  Dr. Martin Llewellyn ,  Dr. Amanda Xuereb , and  Dr. Mohamed Alburaki  for reading comments,  Eric Normandeau  for python scripts of data subsampling. We thank all Ph. D. students in ND laboratory for reading and comments. We also thank  Hayan Hmidan  for generating geographical maps and  Dr. Sebastien Boutin  for helps in water sampling. This project was funded by a grant from the  Natural Science and Engineering Research Council of Canada  ( NSERC  grant no. 6663 ) obtained by ND.
The MATOU is a catalog of 116 million unigenes W290 Nucleic Acids Research, 2018, Vol. 46, Web Server issue obtained from poly-A+ cDNA sequencing of different filter size fractions ranging from 0. 8 to 2000 m (9). About half of the unigenes have a predicted taxonomic assignation representing genes from >8000 mostly eukaryotic organisms. Such environmental genomics datasets are increasingly used in marine biology, ecology and evolutionary studies to understand the mechanisms by which genes influence phenotype in the wild. In parallel to the holistic systems biology approaches that tackle these datasets as a whole, biologists also frequently apply reductionist approaches that target specific marker genes known or hypothesized to play a role in processes such as metabolic pathways, biotic and abiotic interactions.
Testing such candidate marker gene hypotheses requires detailed analyses of voluminous genomic data in their precise environmental context, a task which requires extensive expertise in interrogating heterogeneous data types (i. e. sequencing reads, assemblies, genes together with their taxonomic and functional annotations, environmental variables) to provide integrated interpretations. In order to allow biologists to easily mine such large and complex datasets without the requirement for either significant hardware or programming skills, we make freely available a web service to visualize the geolocalized abundances of taxonomically annotated plankton genes in the context of environmental features.
The query submission interface The user-provided query (Figure  1A ) may be a nucleotide or amino acid sequence in FASTA format, or a hidden Markov model profile (HMM; 10). Users can search for sequences similar to their query in either one of two marine gene catalogues (Figure  1B ): the OM-RGC  (8)  or the MATOU  (9). Users can select different methods to identify sequence similarity (Figure  1C ) depending on the type of query (nucleotide/amino acid sequence or HMM) and the user prefered trade-off between accuracy and speed of the search  (11). For protein sequence queries, similarity search tools are BLASTP  (12)  and Diamond  (13). For nucleotide sequence queries, users can choose to carry out the similarity search against a nucleotide database (BLASTN), or to translate the nucleotide query to search against a protein database (BLASTX, DIAMOND BLASTX).
For more sensitive sequence similarity searches, users can provide an HMM profile file instead of a FASTA sequence (either prebuilt, e. g. by Pfam (  14 ), or custom-built from protein alignments using the hmmer package, http://hmmer. org). Prebuilt Pfam HMM profiles are also searchable from the OGA submission form by simply filling the corresponding field with the Pfam accession ID. For both sequence similarity and HMM searches, the e-value threshold may be customized (Figure  1D ), as well as the number of interactive panels in the results page (Figure  1E ). The email field (Figure  1F ) is strictly optional since a bookmarkable URL for the results is immediately provided to the user upon submission. Results are usually returned to the user within 30 seconds, but for slower queries (e. g.
The world maps of Figure  2A  display quan-W292 Nucleic Acids Research, 2018, Vol. 46, Web Server issue titative geographical distributions of the hits as filled circles with sizes proportional to their combined abundance at the user-selected sampling depth, whilst circle colors indicate the size fractionation applied to the sample (e. g. [0. 2-3 m] represents plankton collected on 0. 2 m pore membranes after a 3 m prefiltration step). The meaning of the acronyms and references to source databases are provided in a user guide hyperlinked on the results page. The sideto-side display of multiple maps enables abundances comparisons between distinct size fractions and/or sampling depths. Co-variation of gene abundances and environmental features can be examined on bubble plots (Figure  2C ) for user selectable combinations of sampling depth and size fractions.
Gene catalogs The building of the OM-RGC and MATOU gene catalogs included in the first release of OGA are detailed in their corresponding release articles (8 and 9 respectively). Briefly, to construct OM-RGC, 7. 2 terabases of plankton metagenome shotgun sequencing reads were assembled for 243 Tara Oceans samples. Genes were predicted in the assemblies and were clustered at 80% sequence identity together with genes from publicly available marine genomic and metagenomic datasets to generate a non-redundant set of 40 million reference genes. These genes were translated and taxonomically annotated by retrieving the last common ancestor of homologs identified in reference protein sequence databases. The MATOU catalog was obtained from the assembly of 16. 5 terabases of plankton metatranscriptome (cDNA sequences corresponding to polyA+ enriched RNA), representing 441 Tara Oceans samples.
The subsequent contigs obtained for each assembled sample were then clustered at 95% sequence identity to construct a catalog of 116. 8 million transcribed sequences. Due to the difficulty of accurate eukaryotic gene calling from low coverage metatranscriptomes, the proteic version of the MATOU catalog was obtained by six frame translation of the nucleic MATOU gene catalog using the sixpack package from the EMBOSS suite  (16). About half of the unigenes were taxonomically annotated using a similar last common ancestor approach as described above for the OM-RGC. The vast majority of unigenes were assigned to eukaryota, but a minor proportion (<5%) was annotated as putative bacterial sequences.
Gene abundances The abundance of each catalog gene in specific biosamples was estimated by evaluating the coverage of raw sequencing reads mapped to the gene's nucleotide sequence. Tara Oceans sequencing reads from 243 metagenomes corresponding to the smallest size fractions (0-0. 22, 0. 1-0. 22, 0. 22-0. 45, 0. 45-0. 8, 0. 22-1. 6 and 0. 22-3 m) and GOS reads from 38 metagenomes (from two size fractions, 0. 1-0. 8 and 0. 8-3 m, 6) were mapped onto OM-RGC, whilst the reads from 441 metatranscriptomes corresponding to the largest size fractions (0. 8-5, 5-20, 20-180 and 180-2000 m) were mapped onto MATOU. When OM-RGC is queried, abundance estimates may be expressed in one of two available normalization schemes: (i) the gene's read coverage is divided by the sum of the total gene coverages for the sample ('percent of total genes per sample'), or (ii) the gene's read coverage is divided by the median of the coverages of a set of 10 universal single copy marker genes ('average copies per cell') that were previously benchmarked for their suitability for metagenomics data analysis  (17).
Environmental context For Tara oceans biosamples, contextual environmental parameters are linked to the sequence datasets via barcodes assigned to each Tara Oceans sample  (18). These metadata serve to geo-localize the samples and provide biogeochemical characteristics of the sampled seawater. Environmental parameters used by the OGA web service are obtained from PANGAEA (https://doi. org/10. 1594/PANGAEA. 875582), the open access library which archives and distributes georeferenced data from earth system research. For GOS biosamples, environmental data was extracted manually from Table  1  of Rusch et al. (6). The environmental parameters provided by OGA (Figure  3 ) are either classical oceanographic measures obtained in situ (e. g. depth, salinity, temperature, oxygen, chlorophyll a, etc.
CASE STUDY Upon phosphorus deficiency, bacterioplankton have established a widespread strategy of replacing membrane phospholipids with alternative non-phosphorus lipids. Sebastián et al. (19)  have shown that this response is conserved among diverse marine heterotrophic bacteria. Several experiments of mutagenesis and complementation have then confirmed the roles of the phospholipase C (PlcP) and a glycosyltransferase in lipid modelling. Analyses of metagenome datasets such as GOS and Tara Oceans have confirmed that PlcP is abundant in low phosphate concentrations areas. We reproduced the analysis of Sebastián et al. (19)  using OGA to verify that the web service conforms to the published results. We used the same phospholipase C (EAQ46983) as a BLASTP query sequence with the author's e-value threshold of 1e -40 to search for similar sequences in the OM-RGC catalog (the same metagenome dataset used by Sebastián et al.
CONCLUSION/PERSPECTIVES By hiding the complex and time consuming integration of heterogeneous data sources behind a user-friendly minimalist web form, the Ocean Gene Atlas web server has the potential to broaden the access to the rapidly accumulating environmental marine genomics datasets. Enabling marine biologists to mine such data--without specific high performance hardware or programming skills--is one of the keys to extract knowledge and understanding from these valuable but underexploited resources. With the current first release of OGA, users can search by sequence similarity genes and proteins in two of the largest marine gene catalogs representing all three eukaryotic, prokaryotic and viral lineages. These two first catalogs will be periodically updated as further marine environmental genomics databases are publically released.
The prerequisites for inclusion in OGA are the availability of the three core resources: gene sequence catalogs, gene abundance estimates in biosamples, and geolocalized environmental context of biosamples. Leveraging the total 2100 Tara Oceans biosamples sequenced so far  (20) , our short term roadmap is to (i) extend the Tara Oceans datasets by including further sampling sites from the Tara Polar Circle expedition, (ii) complement the eukaryotic MATOU metatranscriptome catalog with corresponding metagenomic abundances and (iii) complement the OM-RGC with prokaryotic metatranscriptomes. In addition, we envisage adding the increasingly available pangenomes relevant to the marine biome, such as the large scale Prochlorococcus metapangenome  (21). Such target OGA datasets would allow users with the powerful option to focus queries to their pangenome of interest, in order to track their species specific distribution across the world oceans.
FUNDING French government 'Investissements d'Avenir'  project  OCEANOMICS  [ ANR-11-BTBR-0008  to P. H. , T. V. , C. V. , M. L. , E. P. ]; ' Agence Nationale de la Recherche ' project  IM-PEKAB  [ ANR-15-CE02-0011  to E. V. ];  ETH Zurich and Helmut Horten Foundation  [to S. S. ];  European Regional Development Fund  project  FEDER [1166-39417 ]. Funding for open access charge:  French government 'Investissements d'Avenir'  project  OCEANOMICS  [ ANR-11-BTBR-0008 ].
DATA AVAILABILITY Ocean Gene Atlas is a freely-available web service at: http://tara-oceans. mio. osupytheas. fr/ocean-gene-atlas/ Source code is available at: https://github. com/hingamp/oga Shotgun sequences are available at the European Nucleotide Archive (ENA, https://www. ebi. ac. uk/ena) under accession number PRJEB402 (OM-RGC), PRJEB6609 (MATOU) and PRJNA13694 (GOS). The predicted genes from the OM-RGC are available at ENA under the accession numbers ERZ096909 to ERZ097151, and the protein sequences are available at: ftp://ftp. genome. jp/pub/ db/mgenes/Environmental/Tara. pep. gz. All MATOU resources are available at http://www. genoscope. cns. fr/tara/. Registry of all the samples from the Tara Oceans Expedition (2009-2013) are available at PANGAEA: https://doi. org/10. 1594/PANGAEA.
Selected variants Only variants suspected to result in complete deletion of BRCA2 exon 3 at the transcriptional level were selected for this study. The transcriptional impact was based on both the literature and on the results of functional analysis performed in the various participating laboratories. Intronic variants were validated by a dedicated minigene assay. We evaluated eight BRCA2 variants resulting in a complete deletion of exon 3 (see Supplementary Table  1  and Table  1 ): c. 68-?_316+?del, c. 156_157insAlu, c. 277_317-726delinsCCAT  [36]  c. 316+1G>T, c. 316+2T>C, c. 316+4del, c. 316+5G>A, c. 316+5G>C. Data from 39 families were therefore collected from France, Portugal, Denmark and Sweden. Genotype and clinical data from variant carriers and relatives were obtained via diagnostic clinical testing (for laboratories in which a variant was considered to be pathogenic) or otherwise via ethically approved research testing following informed consent of the participants.
Bioinformatics predictions of splicing alterations For each of the selected intronic variants, in silico predictions of their potential effects on splice sites were obtained by using the following in silico tools: SpliceSiteFinder-like (SSF, http://www. interactivebiosoftware. com), MaxEntScan (MES, http://genes. mit. edu/burgelab/maxent/Xmaxentscan_scoreseq. html) [60] , NNSplice (NNS), GeneSplicer (GS) and Human Splicing Finder (HSF, http://www. umd. be/HSF/) [61]. These algorithms were simultaneously interrogated by using the integrated software Alamut (Interactive Biosoftware, http://www. interactive-biosoftware. com).
ACKNOWLEDGMENTS We thank the many families who participated in this study. This work is supported by the efforts of laboratory and clinical staff from many centres around the world. This work was supported by the  French National Cancer Institute  and the  Direction Générale de l'Offre des Soins (INCa/DGOS) , the  Association Recherche contre le Cancer (ARC) , and the  Groupement des Entreprises Françaises dans la Lutte contre le Cancer (Gefluc). G. C. was funded by the  French INCa , and H. T. was sponsored by both the  Association Nationale de la Recherche et de la Technologie  ( ANRT, CIFRE PhD fellowship ), and by the  OpenHealth Institute. The work by A. C. and A. E. was supported by the  French National Institut for Cancer Research  grant #  INCa-DGOS_8706.
FUNDING La  Ligue Contre le Cancer and INCa (COVAR) ,  INCa  ( SMC, AP, SD ),  NHMRC   Senior Research Fellowship  (ABS). This work was supported by a translational research grant ( FASDEC)  from the  French National Cancer Institute  and the  Direction Générale de l'Offre des Soins (INCa/DGOS) , the  French Association pour la Recherche sur le Cancer (ARC) , and the  Groupement des Entreprises Francçaises dans la Lutte contre le Cancer (Gefluc). GC was sponsored by the  French INCa , and HT by the  Association Nationale de la Recherche et de la Technologie (ANRT, CIFRE PhD fellowship  to H. T. ) and the  OpenHealth Institute.
Some studies such as  [8] ,  [1]  aim at understanding the link between the robot's dialog patterns and the cultural background of the user. However, to the best of our knowledge, there are no major investigations aimed at culturally competent verbal interaction for elderly care. From the technical perspective, in spite of the many Cloud-based tools for Automatic Speech Recognition (e. g. , Microsoft Speech Recognition, Google ASR, Nuance Freespeech 2  ) and Natural Language Processing (e. g. , Di-alogFlow -previously api. ai, Microsoft LUIS 3  ), many problems still need to be solved towards the aim of achieving a natural interaction between the robot and the person. In a recent survey about verbal and non-verbal human-robot communication  [9] , a list of desiderata is presented: (D9) Utilization of online resources and services.
The robot periodically asks the person if she wishes to continue chitchatting: if the answer is negative, the robot switches back to the state ACCEPTING REQUESTS. The reader may be confused by the fact that the dialogue for acquiring additional details about the person's request is part of the state ACHIEVING GOALS instead of being part of ACCEPTING REQUESTS. This depends on how the behaviour of the robot is modelled as a sequence of elementary actions for the sake of planning and real-time execution: in our system, the state ACCEPTING REQUESTS is implemented in software as a specific action whose purpose is to acquire the intentions of the person among a set of possible options, whereas the state ACHIEVING GOALS is ultimately implemented by picking out one among a set of actions, each including a contextual dialogue that is purposely aimed at acquiring details about a specific goal.
IV. CASE-STUDY: PEPPER AND KABIR A. Technological solutions The methodological approach described in the previous Sections has been adopted in order to embed with culturalcompetent behaviour the humanoid robot Pepper developed by SoftBank Robotics (Figure  1 ). Verbal interaction is managed by a module referred to as Conversation Module. Depending on the state of the system (i. e. , ACCEPTING REQUESTS, ACHIEVING GOALS, or CHIT-CHATTING) the modules acquires speech or text from the person and parses it using different technological solutions, and ultimately returns outputs which have different meanings: 1) when the system is in the state ACCEPTING RE-QUESTS or ACHIEVING GOALS, the output is interpreted as a User request, i. e. , short-term information describing goals to be achieved by the robot; 2) when the system is in the state CHIT-CHATTING, the output is interpreted as a User statement,i.