dataesr/scientific-paragraphs-categorization huggingface dataset:
It is one of the diploid robusta varieties, which accounts for about 30% of the world's coffee production. C. canephora is also one of the parents of C. arabica, an allotetraploid derived from hybridization between C. eugenioides and C. canephora. Anticipating that additional Coffea genomes would be sequenced within the next few years, we built a dynamic and scalable crop-specific hub, the Coffee Genome Hub (CGH) which includes the first complete genome of C. canephora. These resources will be critical for re-sequencing intra and interspecific genetic resources in coffee. Indeed, community databases federated around a reference genome were initiated in plant genomics with TAIR (2) and Gramene  (3)  and are proving increasingly important to aggregate, query and retrieve large heterogeneous biological data sets.
Various types of plant genomics systems can be distinguished: those based on ENSEMBL system (4) such as Gramene, and others which use and adapt components of the Generic Model Organism Database project (http://www. gmod. org) such as SGN  (5)  for Solanaceae genomes or more recently AIP  (6)  for Arabidopsis genomes. As previously discussed  (7) , we opted for GMOD components that are open source, modular, portable and benefiting from a large community support in which we have been involved. Our main strategy Nucleic Acids Research, 2015, Vol. 43, Database issue D1029 in implementing this hub was to exploit, whenever possible, interconnected generic software solutions to establish a reliable working environment for scientists interested in coffee biology and related topics. The hub architecture is based on the Content Management System (CMS) Drupal with the Tripal module (8) that interacts with the Chado database  (9).
Genomics data Whole genome sequence data. The sequence assembly and scaffold anchoring to linkage groups of the genetic map give a total of 569. 9 Mb (471. 3 without N) divided into 11 pseudomolecules and a sequence made of non-anchored scaffolds randomly concatenated. Gene models. As described by Denoeud et al. (1) , automatic gene prediction was performed using the Gaze combiner  (23) , an integrative gene finding software that combines several evidences such as ab initio predictions (Geneid, SNAP and FGenesH), mapping of different protein sequence sets (Blat then Genewise), Expressed Sequence Tags (ESTs) and full-length cDNAs (Blat then Est2genome), as well as RNA-Seq reads from Solexa/Illumina technology (Gmorse)  (24). The 25 574 predicted genes obtained from the Gaze combiner have been further annotated to include similarities to proteins of plant model or closely related species, namely tomato or grape, and in silico assignment of InterPro protein domains, GO terms and EC (Enzyme Commission) numbers providing information on probable pathways (Table  1 ).
Transposable elements. Transposable elements (TEs) were predicted and classified using the REPET package  (25)  but also using other tools for LTR retrotransposon (LTR STRUC), MITEs (MITE hunter) and SINEs. We developed a specific expert procedure (manuscript in preparation) and kept 448,845 TE classified in retrotransposons and DNA transposons superfamilies.
Transcriptomic data We collected and inserted the different sources of transcriptomics data that have been provided for protein-coding gene annotation reported by Denoeud et al. (1). This includes unigenes, cDNAs (C. canephora and C. arabica) as well as RNA-Seq reads from different tissues (root, stamen, pistil, leaf, stem and flower) of different C. canephora accessions. For these, clean reads were aligned both on the reference genome using MapSplice  (26)  and on the Coding DNA Sequence (CDS) using Burrows-Wheeler Aligner (BWA)  (27)  for transcript level estimate, normalized expression level (RPKM). In addition, statistical values obtained by other differential expression studies from microarray  (28, 29)  and RNA-Seq  (30)  experiments were also made available through the CGH. A table summarizing the different sources of cDNAs and RNASeq can be accessed at http://coffee-genome.
SNP polymorphisms and genotyping data A total of 386 560 Single Nucleotide Polymorphisms (SNPs) were identified by Illumina transcriptome sequencing of seven C. canephora genotypes, selected to represent the main genetic groups identified in a previous genetic diversity study  (31). RNA-Seq reads were aligned directly to the protein-coding sequences using the BWA aligner  (27)  and SNP discovery was then undertaken with the GATK package (32) using the UnifiedGenotyper module to obtain a list of SNPs and allelic data. Resulting variants were then annotated by SnpEff  (33)  and provided to biologists in the Hub via JBrowse and SNiPlay (see Managing SNP polymorphisms and genotyping data). Overall, for the seven accessions, 292 125 variants were found to be genotyped without missing data and matching 18 762 genes, including 155 103 non-synonymous mutations (53%).
Genetic map and molecular markers Molecular markers and genetic maps are stored in the Moc-caDB database  (34)  and can be viewed and compared using the CMap  (19)  embedded in the Hub. Four Coffea genetic maps are currently recorded, including the high-density C. canephora consensus map combining Simple Sequence Repeat (SSR), Restriction Fragment Length Polymorphism (RFLP) and SNP (including RADseq) markers used for the anchoring of scaffolds. For the latter, among the 3230 loci distributed on 11 linkage groups, 2564 markers have been anchored and located on scaffolds and were thus crosslinked between CMap and JBrowse. Additional information (genetic diversity data, primers. ) can be accessed from CMap which redirects to MoccaDB.
It is interesting to note that there are 211 coffee-specific clusters, ranging from 2 to 18 paralogs and 116 phylum-specific families in the Asterids sharing at least one sequence in common between C. canephora, Solanum tuberosum and Solanum lypercosicum. We identified an over-representation of the NB-ARC superfamily (724 sequences bearing the NB-ARC InterPro signature IPR002182). In addition, a high number of gene copies (47 sequences) were also detected for the Sam Dependent Carboxyl Methyltransferase family that is involved in caffeine synthesis (Figure  1 ). Metabolic pathways. Coffee enzymes and metabolic pathways were predicted in the same manner as in the MusaCyc (7) using respectively PRIAM  (36)  and Pathway Tools. The percentage of CoffeeCyc enzymes and transporters predicted was 30.
Whole genome duplications and synteny blocks The coffee, tomato and grape proteomes were compared using BLASTP (e-value 1e-20) and the 10 best hits were retained. Chromosome segments of genomes containing at least 10 orthologous genes were considered as syntenic regions. A local version of the Plant Genome Duplication Database  (37)  was implemented in the CGH with a dynamic dot plot allowing the display of syntenic regions and further access to the list of orthologous gene pairs. The analysis of the paralogous relationships within the reference genome revealed that nearly all chromosome segments were duplicated in three copies. These duplications observed in the coffee genome originate from the ancestral triplication of eudicots  (38). The comparison of coffee and grape genomes showed that globally, one coffee segment corresponds to three grape segments and that one grape segment corresponds to three coffee segments.
Genomic viewers JBrowse. The CGH integrates the interactive Ajax-based genome browser JBrowse v1. 11. 3  (15, 16). We decided to incorporate JBrowse as a complementary browser to the traditional CGI-based genome browser (GBrowse) to speed up the performance of genome browsing and improve interactivity. This next-generation genome browser, built with JavaScript and HTML5, allows users to easily navigate and explore the C. canephora genome sequence and annotation data over the web. Anticipating for new genomes or high volume data in the future, we also opted for JBrowse for easy addition of new tracks of information. Users can select tracks and view various genomic features located on the reference genome, such as gene models, transposable elements and repeats, Blast matches of ESTs, SNPs and putative orthologous genes from other model plant species.
Most of the features displayed (CDSs, TEs, ESTs) are clickable and will link to a window detailing information about the selected feature. Some features are thus directly cross-linked to related specific databases (e. g. Gene report provided by Tripal, markers toward MoccaDB, SNPs toward SNiPlay). In addition, JBrowse shows RNA-Seq raw read alignments to the genome directly from Binary Alignment/Map (BAM) files. This can be of considerable help in assessing validity of predicted transcripts and for improvement of structural annotation. Chromosome viewer. To complement the Genome Browsers, we developed a chromosome viewer based on the Highcharts API (http://api. highcharts. com) that provides a quick overview of feature density along the chromosomes. This viewer includes the visualization of the non-uniform distribution of genes (introns and exons) and transposable elements (Copia, Gypsy, LINEs and DNA transposons), as well as SNP density tracks.
Exploring gene expression level Transcriptomics data can be explored at three levels: (i) The JBrowse feature allows users to view raw read alignments to the genome and display the relative abundance of transcript fragments for the different tissues or conditions (Figure  2A ) (ii) A web form enables users to load a list of genes and to dynamically generate a heatmap image showing the expression values for the different tissues, thus enabling comparisons between conditions (Figure  2B ). (iii) Based on differential expression studies (see Transcriptomic data), the CGH allows searching for differen-D1034 Nucleic Acids Research, 2015, Vol. 43, Database issue tially regulated genes respecting a minimum log 2fold expression ratio or statistical P-values, by comparing two experimental conditions (Figure  2C ).
Resulting genes can be retrieved as a list or be graphically summarized depending on their genomic location or their Gene Ontology (GO) functions. Furthermore, the database offers the possibility to compare between several pairs of conditions either from the same or from different studies. By clicking 'intersect' and selecting an additional pair of conditions, users may combine the filtering parameters and retrieve common genes that have been found differentially expressed (up-or downregulated genes) in several analyses. Numbers of genes are displayed in a Venn diagram.
g. hapmap, fasta) or send them out for a specialized analysis such as GWAS, diversity analysis or SNP-based distance tree analysis (Figure  3C ). In addition, SNP positions and their associated genotyping information can also be accessed from JBrowse viewer, which can display feature data directly from Variant Call Format (VCF) files (Figure  3B ). This allows users to visualize and browse the genomic location of the SNPs. Mutual links between the SNiPlay database and GBrowse/JBrowse are available so that users can easily switch aspects of the investigation to interesting SNP polymorphisms.
FUTURE DIRECTIONS: WHAT ELSE? The coffee community has now a central portal for the management of the coffee genome with a comprehensive set of related data sets. Our main objective is to propose a community-curated information system with high-quality gene annotations that will support further genomic studies. To reach this point, we need to promote synergies with other groups working on coffee and include new data types as soon as they become available. The Coffee Genome Hub is part of a dynamic strategy for data integration and interoperability of bioinformatics applications federated around a community annotation system to ensure the accuracy of genome annotations. In that respect, future directions will rely on JBrowse with the added possibility to manage both private and public data depending on the authenticated user.
ACKNOWLEDGEMENT The Coffee Genome Hub is supported by the South Green Bioinformatics Platform (http://www. southgreen. fr/). We thank all bioinformaticians of the South Green network for participating to the development or addition of original tools in the platform. We also thank Bertrand Pitollat who maintains the computing facilities (web servers and HPC) with so many system requirements. We also acknowledge with thanks the Coffee International Research Consortium to complete the sequencing project of Coffea canephora, and more generally the coffee research community for providing data and for their feedback. We are grateful to all the people that interact regularly with us for the improvements of the various systems described in this publication and help us to maintain and further develop useful resources for the community.
Photos were taken with an Olympus E-3 digital camera. Several digital pictures were reconstructed using Helicon Focus software. X-ray tomography was realised in the AST-RX service (CT scan facility of the MNHN, UMS 2700), using a v|tome|x L240-180 from GE Sensing and Inspection Technologies phoenix|xray, with a X-Ray 180 KV/15 W nanofocus transmission tube, as well as a movable detector formed by a 20242 pixels (200 microns pixel). The voxel size of the reconstructed volume is 11. 2 mm. 3D reconstructions and movies have been made using AVIZO 7. 0 software. Variations in the material density in the amber piece are visible through changes of coloration from black (low density) to white (high density). No permits were required for the described study.
Total Phenolics, Tannins and Anthocyanins Total polyphenol, tannin and anthocyanin contents of grapes and pomace skin and seed extracts were determined. Crude extracts were solubilized in water/ethanol (90:10, v/v; pH 3. 5 with tartaric acid) at appropriate concentrations. TPC was determined by the Folin-Ciocalteu assay  [57]  and the data expressed as mg of gallic acid equivalents (GAE) per g dry weight. Total tannin content was measured by acidic hydrolysis using the method of Ribereau-Gayon and Stonestreet  [58]. Anthocyanin content was determined by the SO 2 bleaching procedure  [59].
HPLC Analysis of Anthocyanins Powder skin extracts were dissolved in water/methanol solution (50:50, v/v) at a concentration of 10 mg/mL prior to UPLC-UV analyses using a Thermo-Accela HPLC system (Thermo-Fisher, San Jose, CA, USA) composed of a PDA detector, an autosampler and a quaternary 600 series pump system controlled by an Xcalibur data system. Separation was performed on a C18 Kinetex column (100 mm × 2. 1 mm, 1. 7 µm). The injected volume was 2 µL. The mobile phase pumped at 200 µL/min comprised a 20 min, 7%-26% gradient of acetonitrile in water with both solvents containing 5% formic acid. Eluting peaks were monitored at 520 nm. Identification of mean peaks was performed by comparison to external standards. The data was expressed as malvidin-3-O-monoglucoside equivalent/g dry weight of skins.
Statistical Analysis All measurements were performed in triplicate. Results are expressed as means ± standard deviation (SD). One-way ANOVA was performed to test the effects of variation factors (different samples) on each variable (TPC, total tannin, anthocyanin, phenol concentrations, mDP, etc. ). If significant effects were found at a 95% confidence interval, ANOVA was followed by a Tukey's HSD and Duncan post hoc test to identify differences among groups. These analyses were performed using Statistica V. 7 Software (Statsoft Inc. , Tulsa, OK, USA). Correlations among data obtained were calculated using the MS Excel software correlation coefficient statistical option.
Acknowledgments The authors gratefully thank  Pierre Perrin  ( Perrin et Fils, Chateau Beaucastel, Châteauneuf-du-Pape ) for providing Mediterranean grapes and pomaces and financial support for  Isabelle Ky 's postgraduate research. This work was also supported by a  French grant  of the  ANRT  ( Association Nationale de la Recherche et de la Technologie ) CIFRE n  2011/0118.
2014). Herein, we have analyzed 198 complete fungal genomes (corresponding to 164 different species) for the presence of NLR related proteins. We report on the NLR domain architecture, variability and repertoire size in these 164 fungal species. We find evidence of extensive variation of NLR copy numbers both within and between species. Several NLR domain architectures appear presently restricted to the fungal phylum, whereas others also exist in animal or plant lineages. NLRs appear restricted to filamentous species and are missing from yeast genomes, suggesting that presence of NLRs is associated with multicellularity. Our data suggest an extensive modularity of domain associations, with recurring inventions of domain architectures. Finally, a proportion of the C-terminal domains of NLRs show strong internal conservation, as described for the rapidly evolving HNWD family of P.
Identification IR and functionally validated (FV) queries were obtained by extraction of NACHT and NB-ARC domains from the fulllength sequences according to PfamA PF05729. 7 and PF00931. 17 profile matches  (Finn et al. 2014). PSI-BLAST searches  (Altschul et al. 1997)  with three iterations and an E value cut-off of 10 À5 were carried independently for each query sequence on the NCBI "nr" database  (June 27, 2013) , and then combined. The candidate set was pruned from sequences with multiple disjoint matches to the queries and from very short sequences (below 100 amino acids). Then it was limited to sequences from complete or draft wholegenome sequencing and resequencing projects, according to Genome OnLine Database (  [Pagani et al. 2012 ], as of September 18, 2013), for which at least 2,000 sequences were available in the nr database.
Intrastrain identical copies of sequences were removed, whereas interstrain identical sequences were kept. Boundaries of the NB domain were determined as the longest stretch of matches from all NACHT and NB-ARC queries in the PSI-BLAST search. Proportional Venn diagrams were generated using BioVenn  (Hulsen et al. 2008). Noncanonical P-loop variants were detected by inspecting single-residue changes at the four conserved positions of the motif in multiple sequence alignments of NB domains generated by Clustal Omega 1. 1. 0 (Sievers et al. 2011) , with two iterations separately for nonredundant sets of 4596 NACHT and 1174 NB-ARC STANDs found in the entire nr database (not limited to whole-genome projects).
Annotations from repeatcontaining clans: Ankyrin (CL0465), Beta propeller (CL0186), and TPR (CL0020, includes HEAT repeats) were merged to three main categories: ANK, WD40, and TPR, respectively. Highly overlapping N-terminal annotations, as well as three prion-forming domain annotations were merged (see Results). Conflicting annotations from PfamA HeLo and inhouse HeLo-like signatures were resolved in the favor of the former. Numerical suffixes of signature names were truncated and sequential occurrences of identical annotations were squeezed. Domain associations were visualized using the graphviz package  (Gansner and North 2000). Distribution of domain architectures was quantified by means of paralog and ortholog hits. Ortholog index counted number of species (distinguished by binomial name) in which a given architecture was found.
2006; Dereeper et al. 2008). Genes with no clear ortholog in all ("orphans") or in some other strains ("semiorphans") from the same species were identified according to co-phenetic distances between leaves in multistrain phylogenetic trees (using the R package ape). To detect highly homologous pairs of NB domain sequences associated with different N-terminal domains, BLASTP scores  (Altschul et al. 1997)  were calculated in the all-against-all manner for the entire data set of NB domains. A target was counted as highly similar to the query if the match scored at least 99% of the maximum score obtained by the query. To avoid false positives, only matches with at least 80% identity over 100 or more amino acids were counted; sequences with unknown N-terminal annotation were also excluded.
Repeat Domain Analysis Highly internally conserved repeats were detected using T-reks  (Jorda and Kajava 2009)  with customized parameters (PSIM = 0. 85, kmeans = 10, overlapfilter on, external MSA: ClustalW 2. 1, and Muscle 3. 6); repeat regions shorter than 100 amino acids were filtered out. Sequences from dikarya, metazoan, and viridiplantae belonging to ANK, WD40, and TPR clans were extracted according to designation in the Pfam repository (27. 0) and availability in the nr database  (June 27, 2013). The content of highly internally conserved repeats was calculated as above. "Skipredundancy" from the EMBOSS package  (Rice et al. 2000)  was used to obtain the nonredundant count of the highly conserved repeats. For analysis of P. anserina ANK and TPR motifs, genesencoding STAND proteins with individual repeats displaying over 85% internal conservation were analyzed, excluding hnwd family members, resulting in a set of ten genes.
They code for TPR, ankyrin, or HEAT repeats. For each gene, the repeat-encoding DNA was polymerase chain reaction (PCR)-amplified from five wild isolates from the Wageningen collection (Wa94, Wa 96, Wa97, Wa99, and Wa100) (van der  Gaag et al. 1998) , gel-purified, cloned in the XL-PCR-TOPO plasmid (Invitrogen, Life Technologies) and sequenced. Sequences were manually assembled before further analysis. In addition, sequences from the S strain were extracted from the P. anserina genome sequence and were added to the data set. Protein repeats were identified using RADAR  (Heger and Holm 2000). Individual repeats were then aligned using ClustalW and a neighbour-joining tree constructed using MEGA5  (Tamura et al. 2011). Sequences clustering together with a high bootstrap support were analysed further, and the other were discarded from the data set.
We first conducted PSI-BLAST searches in the nr ("nonredundant") database with the HET-e1 HET domain by excluding fungal sequences and found that homologs of this domain are also found outside of the fungal kingdom in Stramenopiles, Haptophyceae, Choanoflagellates, green algae, and bryophytes. Next, we used Hidden Markov model searches to identify remote homologs of the HET-e1 HET domain. Both algorithms that we used (HHpred and JackHHmer) identified similarity to TIR domains. In particular, the two best hits in HHpred were to structure-based profiles constructed from the TIR domain of PdTIR from Paracoccus denitrificans  (Chan et al. 2009 ) and of the TcpB protein from Brucella melitensis  (Kaplan-Turkoz et al. 2013; Alaidarous et al. 2014; Snyder et al. 2014). Figure  6  presents an alignment of fungal HET domain proteins with bacterial and human TIR domains of known structure and related HET domains from phylogenetically diverse origins.
To locate the positive selection and polymorphic sites on the repeat domain structure, the repeats were homologymodeled to ANK and TPR domains of known structure. The TPR motif domain of Pa_2_10340 was modeled using the human kinesin light chain 2 structure (Protein Data Bank [PDB] ID 3EDT) as template. In the TPR motifs, all positive selection sites as well as the other polymorphic position mapped to the concave side of the TPR structure in the a-helical regions. The ANK repeat domain of Pa_3_8560 was modeled using the structure of artificial ANK repeat domain of engineered protein OR264 (PDB ID 4GPM) as template. In the ANK repeats, with one exception, the positive selection and polymorphic site also mapped on the concave surface of the ANK repeat domain in the inner helices and the b-hairpin/loop region, which correspond to the binding interface of ANK repeats based on cocrystal structures  (Javadi and Itzhaki 2013).