dataesr/scientific-paragraphs-categorization huggingface dataset:
The protein-coding genes were predicted by Genoscope using the GAZE combiner (2), whereas we performed the TE prediction with the REPET package (3) (Figure  1 ). GAZE is an integrative gene finding software that combines several evidence such as ab initio predictions (Geneid, SNAP or FGenesH)  (20) (21) (22) , the mapping of different protein sequence sets (Genewise)  (23) , ESTs and full-length cDNAs (Est2genome)  (24) , as well as RNA-Seq reads from Solexa/ Illumina technology (Gmorse)  (25). The outputs of these analyses are formatted in fully compliant Generic Feature Format (GFF3) files most commonly used as the exchange format between components. GAZE analysis, validated by some manual expert curation, predicted a consensus annotation of 36 542 proteinencoding genes. To define canonical protein-coding gene models, annotation was aggregated into gene entries consisting of one or more exons, one or more mRNAs and at least one polypeptide.
Following the structural gene prediction step, we performed similarity searches (BLASTP) against UniProtKB/SwissProt, UniProtKB/TrEMBL and the rice proteome database, plus domain searches with InterProScan  (26)  to infer functions for each proteincoding genes model. We defined three parameters: (i) qcov (the percentage of the query covered in the match), (ii) scov (the percentage of the subject covered in the match) and (iii) percentage identity (the percentage amino acid identity in the match). We kept the best ranking following a decision tree to transfer the polypeptide function [e. g. Sucrose-phosphate synthase (SPS)] with a confidence level (close to the Gene Ontology Evidence Code)  (27)  and to define the completeness of the corresponding gene. All the predicted functions we found at this step contributed to create an in-house controlled vocabulary, namely, product.
Other controlled vocabularies were also applied, such as gene (e. g. SPS), Enzyme Commission (EC)_number (e. g. 2. 4. 1. 14), completeness (e. g. complete), is_obsolete (e. g. no), status (e. g. in_progress), evidence code (e. g. Inferred by Curator (IC)), to disambiguate the   term used in gene annotations and to improve the data quality  (11). The results of InterProScan were used to assign Gene Ontology (GO) terms with InterPro2GO. These resources contributed to enrich the GFF3 files dedicated to the manual annotation, at the polypeptide level, with cross-references on external databases (Dbxref), Gene Ontology terms and controlled vocabulary (Ontology_term). CAS functionality has already demonstrated its usefulness in several annotators' training sessions. Such CAS were applied to the preparation of several feature sets [learning gene set, Long Terminal Repeat (LTR) retrotransposon library (1)], and supported genomic studies  (28) (29) (30).
Gene families For a newly sequenced genome, such as Musa acuminata, comparative genomics is critical for the generation of reliable gene function annotations. The Banana Genome Hub relies on a robust comparative genomics database called GreenPhylDB  (31) , which now includes the Musa proteincoding genes. The current version of GreenPhylDB compiles a stable and curated catalog of protein gene families based on the automatic clustering of 22 whole plant genome sequences. Specific information related to the clustering of the Musa genome is reported in Table  2. This information is calibrated against the 22 plant genomes and provides a suitable framework for evolutionary and functional analyses. The banana genome exhibits an interesting evolutionary pattern with several rounds of ancestral whole-genome duplications (WGD).
Paralogous relationships within the banana genome Any search within the Banana Genome Hub will display the matching genomic features on the Musa chromosomes (Figure  2 ) where paralogous regions are colored according to beta ancestral blocks as defined by D'Hont et al. (1). A local version of Plant Genome Duplication Database (PGDD)  (32)  was implemented with a dynamic dot plot allowing to focus on syntenic regions and provide access to the list of the duplicated gene pairs. Duplicated regions were detected with SynMap program of the CoGe web site (33) using a quota-align ratio of 3:3  (33). The synonymous substitution rate (Ks) and the non-synonymous one (Ka) of each gene pair were calculated using the Yang-Nielson method  (34).
Metabolic pathways We set up a pathway tools database  (35)  for metabolic pathways that we called MusaCyc. Banana genes coding for enzymes were classified following the EC number using PRIAM (PRofils pour l'Identification Automatique du Metabolisme)  (36). Then, metabolic pathways were predicted from these EC numbers using Pathway Tools  (35). We also used the Pathway Hole filler program to identify missing enzymes in the MusaCyc database. The percentage of MusaCyc enzymes and transporters predicted from the proteome was 17. 1% (6128 enzymes and 112 transporters for a total of 36 528 polypeptides), against 13. 9% for RiceCyc v3. 3 (6040 enzymes and 603 transporters for a total of 47 894) and 22. 5% for AraCyc v8. 0 (6023 enzymes and 143 transporters for a total of 27 416).
Transcriptomics Transcriptomics data and ESTs data are useful resources to characterize transcription patterns and to validate gene structures  (37). The ESTtik system contains a semi-automatic cDNA annotation pipeline and a database, providing public and private access to cDNA libraries, including Next Generation Sequencing data. We used the system to annotate 14 banana cDNA libraries, comprising five genotypes, and various tissues and conditions. The transcriptome database records 91 041 public banana cDNA sequences available through the Global Musa Genomics Consortium, assembly data and various annotation data (e. g. Blast against public databases, Gene Ontology data, domain search, SSR and SNP data mining). We designed specialized web interfaces to query and visualize all the information.
These sequences were linked and mapped onto the assembled genome in a two-step procedure (1): Blast and then a refinement with Est2Genome software  (24). The results are available within the Musa GBrowse 2 on a track called 'MusaGenomics consortium ESTs'. Similarly, Genoscope aligned 6 888 879 EMBL monocotyledon mRNAs with Blat and Est2Genome (GBrowse Public monocotyledon EST track). cDNA reads (829 587 from DH-Pahang) produced by 454 technology were aligned to the banana sequence assembly with the same procedure and posted to GBrowse Pahang 454 EST read track. Approximately 30 million Illumina sequence reads were obtained for each of the four RNA-Seq libraries corresponding to cDNA libraries from DH-Pahang and Pahang genotypes inoculated with fungal pathogens and the corresponding controls.
Links with genome resources in Rice, the model plant for monocotyledons Rice is one of the most studied crop genomes and is a model plant for monocotyledons. As banana belongs to the Zingiberales, a sister order of the Poales, a strong link with rice genome resources is extremely useful. We connected the Banana Genome Hub to OryGenesDB  (43) , an interactive tool for rice reverse genetics. OryGenesDB contains >245 000 flanking sequence tags of various mutagens and functional genomics data collected from both international insertion collections and the literature. OryGenesDB provides a set of tools around GMOD Genome Browser to visualize or to search for insertions in candidate genes. In all, 11 665 putative pairs of orthologs between rice and banana have been identified using reciprocal blast hit strategy, and it is possible to take advantage of the system for gene annotation or functional genomics purposes.
Data Aggregation and System Interoperability Data storage We use the modular relational database schema Chado to store only the gene models (e. g. GAZE predictions) and functional analysis results (Blastp and InterPro) required for manual annotation activities, in a PostgreSQL database. All other GFF3 features comprising structural annotations (i. e. FGenesH, Geneid, Genewise outputs) are stored in a separate GBrowse MySQL database. Manual annotation activities are performed using the Artemis annotation tool directly connected to the Chado database  (12). The Artemis gene builder has four tabs (Properties, Core, Controlled Vocabulary and Match) that provide curators with a comprehensive overview of each protein-coding gene model. The 'Controlled Vocabulary' section provides a way to add (or remove) CV terms assisted by an auto-completion field that ensure the compliance of the annotations with agreed standards.
The 'Match' section displays the similarity found at the functional annotation step. Moreover, to ensure a consistent and accurate standard of annotation using a CAS, we developed the Chado Controller  (11) , which contains three modules: (i) a module for managing user access rights to the features stored in the database (i. e. private and public project can be stored in a single database), (ii) a module for checking the manual annotation before edition in the database (annotation inspector) and (iii) a module for recording the history of the modifications (annotation history with the Chado 'audit module').
Interoperability As genomic data sets are distributed over multiple data sources (Table  3 ), a central function of the Banana Genome Hub is to link heterogeneous data and information systems (Figure  2 ). Providing different entry points to users and facilitating the data retrieval process in as minimum of steps were the driving principles of the work presented here. Conceptually, data consistency and quality is ensured by the CAS, whereas the interoperability with other systems is promoted by cross-links to their sites from Tripal and GBrowse (Figure  3 ) forged using unique identifiers and ontology. The Hub is organized around a protein-details page for each banana protein-coding gene. The page contains genomic data extracted from Chado and various links for associated data (e. g. gene families, metabolic pathways, etc.
). The types of unique identifiers allowing the links are reported in the Figure  3. GBrowse 2, through its pop-up, proposes similar content and links based on unique identifiers that support persistent cross-links. GBrowse 2 was designed to facilitate data aggregation and can connect several data sources. We use this feature to aggregate Chado database, GBrowse MySQL database and BAM files (for RNA-Seq) using specific Perl database adaptor modules, respectively, Bio::DB::Das:: Chado, Bio::DB::SeqFeature::Store and Bio::DB::Sam (Figure  1 ). This is particularly useful, as performance considerations dictated that we needed to apply different storage strategies depending on the type and volume of data. Although Chado remains the central database (for Artemis and Tripal), Chado sometimes suffers from poorer performance for data extraction.
First, such modifications may change the result of protein domain search like InterProScan that are handled in different places (e. g. Chado, GBrowse MySQL, GreenPhylDB). Significant changes in the sequence, owing for instance to erroneous frame shifts or gene fusion, could result in an alternative protein clustering in GreenPhylDB clusters. In any case, the phylogenetic analyses may be affected as well as the ortholog predictions. Hence, introducing flexibility in gene annotation should be accompanied by the ability to update subsequent analyses. As a step towards overcoming these challenges, we coupled our gene curation pipelines with an in-house version of the Galaxy workbench  (44) , to handle data resulting from our automatic pipelines. We implemented several analytical workflows to reproduce the analyses usually done at large scale via Perl scripts executed in command line.
The exons 11 and 12 of this gene have also been merged, as are the exons 11 and 12 of GSMUA_Achr4G05060_001 (Supplementary File 1 Figure  S2 ), exons 1 and 2 and exons 13 and 14 of GSMUA_ Achr6G17480_001, and exons 11 and 12 on GSMUA_ Achr9G22510_001. These modifications are supported by additional evidence of gene structure like the ESTs. The ESTtik tools (Supplementary File 1 Figure  S8 ) facilitated the retrieval of information about these ESTs. Through direct access to Artemis from GBrowse (Supplementary File 1 Figure  S2 ), the genes were quickly corrected. Moreover, Gbrowse also links with a physical map and a genetic map of CMap for identifying genetic markers near such genes of interest (Supplementary File 1 Figure  S4 ). All the SPS family members are characterized by the same InterPro signature (IPR006380-SPS domain) (Supplementary File 1 Figures  S5  and S6 ).
To identify Musa CESA and CSL sequences, we searched the Musa proteome using the protein combination tool of GreenPhylDB with InterPro domain IPR005150 corresponding to the CesA family (CESA, CSLB, CSLD-J) and IPR001173 corresponding to the Glycosyl transferase family 2 (CSLA, CSLC) (Supplementary File 2 Figure  S1 ). The search using IPR005150 identified 34 sequences that clustered within the 'Cellulose synthase' family in GreenPhylDB. Predicted enzymes with the associated E. C. number E. C. 2. 4. 1. 12 for CesA function that were retrieved from MusaCyc, corresponded to the same set of 34 sequences (Supplementary File 2 Figure  S2 ). The search using IPR001173 in GreenPhylDB identified 32 sequences of which 26 clustered within the 'Putative glycosyl transferase' family that comprised CSLA and CSLC homologs from other species.
Homologous sequences from the rice, sorghum, Arabidopsis and grapevine genomes were retrieved from GreenPhylDB and were assigned to the different subfamilies following published data  (57, 58, 61)  (Supplementary File 2 Figure  S3 ). We manually curated the compiled 60 Musa genes using information from different de novo prediction programs, expression data and BLASTP data available on the banana GBrowse (Supplementary File 2 Figure  S4 ). This resulted in a final set of 59 genes. All sequences were aligned using the program MAFFT  (62)  with default iterative refinement method. Maximum-likelihood phylogenetic analysis was performed using PhyML V. 3. 0  (63)  with the Le-Gascuel (LG) evolution model, a gamma law with four categories and an estimated gamma distribution parameter, a Nearest Neighbor Interchange starting tree, and approximate likelihood ratio test for branches based on a Shimodaira-Hasegawa-like procedure.
9-Cis-epoxycarotenoid Dioxygenase Family For the final use case, we highlighted the 9-Cis-Epoxycarotenoid Dioxygenase Family (NCED), a small intron-less gene family belonging to a larger family of carotenoid cleavage dioxygenase (CCD) genes that can have introns, are implicated in abscisic acid biosynthesis, and are targeted to chloroplasts. NCED proteins have been shown to be involved in biotic and abiotic stress responses  (65) , fruit ripening  (66, 67)  and seed maturation  (68)  and therefore might present some interest for banana breeding. A total of 13 DH-Pahang CCD gene members were identified from GreenPhylDB (search for IPR004294, carotenoid dioxygenase) and MusaCyc (search for EC 1. 13. 11. 51) (Supplementary Table  S3 , Supplementary File 3 Figures S3.
ß The Author(s) 2013. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/ licenses/by/3. 0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. Page 1 of 14 (page number not for citation purposes) Database, Vol. 2013, Article ID bat035, doi:10. 1093/database/bat035.
All other bioinformatic systems are integrated using HTML iframes (those with the Banana Genome Hub green banner) such as GreenPhylDB (E), MusaCyc (F), PGDD Dot Plot (G), Macrosynteny Karyotype (H) CMAP and TropGeneDB (I), SNiPlay (J) and Galaxy (M). Banana Genome Browser links also ESTtik (K) and OryGenesDB (L). The in-house Advanced Search is linked to the GBrowse 2. Biomart query builder allows exporting personalized qualifiers of genomic features in various formats. The Macrosynteny Karyotype is linked to GBrowse 2 using the Bio::DB::SeqFeature::Store MySQL database. CMAP allows the Comparison of various maps (sequence, genetic, etc. ). (G) The system of the PGDD is used to show the Beta ancestral blocks reconstruct from the DH-Pahang paralogous regions. (H) The Macrosynteny Karyotype is the result of an Advanced Search.
Figure3. Architecture of the Banana Genome Hub and Interoperability between Biological Information Systems. Gene report can be displayed using Tripal, GBrowse or BioMart and edited with Artemis. Polypeptides can then be further analyzed with GreenPhylDB using for instance keywords and InterPro domains, with the Galaxy workflow manager by running personalized phylogenetic workflows and with Pathway Tools to study metabolic pathways through keywords or EC numbers. Finally, SNP stored in GBrowse can be investigated with SNiPlay. Genetic markers can be positioned on genetic maps using CMAP and investigated into the TropGeneDB, linked with the MGIS through ITC accession numbers. Germplasm material can then be requested to the ITC. Most of the systems were embedded using the Drupal CMS using HTML iframe.
Acknowledgements The authors thank all those who applied their expertise for the gene and transposable annotation and contributed to the improvement of the quality of the banana reference sequence:  Eva Hribova  ( LTR retrotransposon Copia ),  Pat Heslop-Harrison (LINE) ,  Olivier Panaud (Retrotransposon) ,  Thomas Wicker  (DNA transposon),  Julie Leclercq  (miRNA). They also thank  Bertand Pittolat  who maintains the computing facilities (web servers, High computing facilities).
Funding ANR  Genoplante  (grant  ANR-07-GPLA-004   GNPAnnot );  ANR Plant Genomics  (grant  ANR-08-GENM-V019  Musatract);  CGIAR Research Project Root Tuber and Bananas (CRP-RTB ). This work was supported by the high performance cluster of the  South Green Bioinformatics Platform. Funding for open access charge: xxx.
through GNPAnnot (as of 29 November 2012). ChrUn_random corresponds to un-anchored scaffolds Database, Vol. 2013, Article ID bat035, doi:10. 1093/database/bat035 Original article. Chromosome Length (bp) Gene count TE count Predicted Curated Predicted Curated chr1 27 573 629 2834 29 0 chr2 22 054 697 2327 23 0 chr3 30 470 407 3253 51 0 chr4 30 051 516 3367 43 11 chr5 29 377 369 2974 44 0 chr6 34 899 179 3700 61 11 5 chr7 28 617 404 2766 38 0 chr8 35 439 739 3454 48 11 0 chr9 34 148 863 3105 65 11 1 chr10 33 665 772 3157 54 11 0 chr11 25 514 024 2678 32 0 chrUn_random 141 147 818 2927 0 64 0 Total 472 960 417 36 542 488 172 17. Page 3 of 14.
Patients and study design The present study was part of a large prospective, randomized clinical trial carried out at the University Hospital of the RWTH Aachen between 2008 and 2011. After obtainment of written informed consent, patients enrolled in the trial were initially randomly assigned to one of the two study arms (xenon vs. sevoflurane) and blinded to receiving either sevoflurane or xenon. Data on xenon consumption and administration were recorded automatically in 50 patients. We retrospectively analysed the recorded data from those patients who were consecutively enrolled and randomized to receive xenon anaesthesia. The study was registered at the EMA (EudraCT number: 2008-004132-20) and at ClinicalTrials. gov (NCT number: 00793663). The local institutional ethics committee and the German federal drug administration (BfArM) approved the study.
Statistical analysis All data were statistically analysed using a commercially available software package (SPSS 19. 0 (SPSS inc. , Chicago, IL, USA). All enrolled patients that were initially randomized to receive balanced xenon anaesthesia were retrospectively analysed according to the xenon delivery mode during maintenance (ECO, AUTO and MANUAL). As primary endpoint we investigated the xenon consumption using the different automated AUTO and/or ECO mode of Felix Dual. Secondary endpoints were the differences between AUTO, ECO and MANUAL groups with respect to xenon flow and gas consumption within wash-in and maintenance. Furthermore we calculated the learning period, which demonstrates the tendency of xenon consumption over a period of 2 years. The linear trend of the learning curve was calculated using a regression analysis and determination of correlation coefficient.
Patients Data on gas consumption and xenon delivery were recorded in a total of 50 patients. Follow up and final analyses of data were performed in 48 patients. The reasons for the two drops out are due to failure of continuous recording of respiratory data (AUTO group) and intraoperative switch/extend scheduled operative procedure (ECO group) (Figure  2 ). Patients treated with ECO, AUTO or MANUAL mode did not differ regarding most relevant baseline characteristics including such as age, gender and body mass index (Table  1 ). Likewise no difference could be detected with respect to neuraxial anaesthesia, type of surgery, remifentanil, propofol, rocuronium consumption, postoperative analgesia and awareness (Table  2 ). For the duration of anaesthesia, the BIS values remained within the recommended reference range in all patients.
Given the presented learning curve, showing a constant decrease of the overall and hourly xenon consumption, these findings might as well indicate a promising approach for effective cost reduction. Since this development based alone on the experience of involved anaesthesiologists, it remains speculative, if an additional training or periodic refreshing courses would have further improved the performance of the anaesthesiologists team. Of note, while conducting this observational study, further improvements have been integrated in Felix Dual and a new software upgrade is available since 2011, which aims to further reduce xenon consumption in ECO mode and to improve the precision of AUTO mode. Therefore present results could even be reinforced by the new software version of Felix Dual. However, further studies would be needed to evaluate current progress in xenon reduction.
The Shannon-Weaver diversity index was found to be the best criterion to be maximized in the first step using the CORE HUNTER program. A primary core collection of 50 entries (CC 50 ) was defined that captured more than 80% of the diversity. This latter was subsequently used as a kernel with the MSTRAT program to capture the remaining diversity. 200 core collections of 94 entries (CC 94 ) were thus built for flexibility in the choice of varieties to be studied. Most entries of both core collections (CC 50 and CC 94 ) were revealed to be unrelated due to the low kinship coefficient, whereas a genetic structure spanning the eastern and western/central Mediterranean regions was noted. Linkage disequilibrium was observed in CC 94 which was mainly explained by a genetic structure effect as noted for OWGB Marrakech.
Dataset A total of 561 accessions from 14 countries, maintained in the ex situ OWGB Marrakech collection, were used in this study (Table  S1 ). A set of 17 SSR loci was used for accession genotyping (Text S1). Plastid DNA (or cpDNA) was characterized using 37 polymorphic loci and two cleaved amplified polymorphism sites (CAPS-XapI and CAPS-EcorRI), as described by Besnard et al. [67]  (Text S1). The phenotypic data was from olive databases and national catalogues based on passport data and variety name as identification key  [68] [69] [70] [71] [72]. Data on 72 agro-morphological traits classified into 213 trait classes according to standards described by the International Olive Oil Council (IOOC) was compiled for 425 varieties (Table  S2 ).