dataesr/scientific-paragraphs-categorization huggingface dataset:
Incorporating new GO terms into exRNA Atlas as a use case The exRNA Atlas is a central data repository developed and maintained by the DMRR that distributes data provided by the ERCC. The first public release of the exRNA Atlas, in early 2016, contained exRNA-seq profiles of over 500 samples generated by ERCC members analyzed uniformly using standard in-house analysis pipelines and quality-controlled using standards agreed by the Consortium. The exRNA Atlas browser enables efficient searching and sub-selection of exRNA profiles for retrieval and integrative analysis. To facilitate integration of the datasets into the exRNA Atlas, data and metadata standards have been developed by the Metadata Working Group of the ERCC. The metadata standards build on metadata data models we previously developed during the course of the NIH Epigenome Roadmap project, International Human Epigenome Consortium (IHEC) project, and in collaboration with the ENCODE3 project, and NCBI.
The metadata include core objects such as biosamples, donors, experimental protocols, studies and analysis methods. The Genbor-eeKB document modeling system was used to define syntactic and semantic models for ontology-rich multifaceted metadata based on the lightweight JSON syntax. The latest release of the exRNA Metadata Standard associates various biomedical ontologies to both the attribute (key) values as well as with the attributes themselves and supports dynamic validation against ontologies available in NCBO Bioportal. More than twenty data elements within the metadata model are associated with ontologies, such as biofluids, disease types, anatomical locations, cell culture sources, cell lines, tissues, and other properties needed to describe biosamples and donors. A number of these elements reflect the clinical focus of the ERCC, defined by ontologies such as the Systematized Nomenclature of Medicine -Clinical Terms (SNOMEDCT) (https:// www.
While granular/specific annotations would be more informative, ontological inferencing can be applied to a given term (extracellular vesicle) to automatically retrieve its annotations and those associated with its descendant terms (e. g. extracellular exosome). GO query engines like QuickGO (http://www. ebi. ac. uk/QuickGO/) support this type of query. We anticipate that updating the gene products associated with each GO term will be an ongoing process. The use of GO terms to annotate exRNA metadata and implement faceted search within the exRNA Atlas browser We have implemented a search interface that allows users to browse exRNA Atlas biosamples based on different facets of metadata, including the type of biofluid, cell culture source, disease, and exRNA source. The facets (categories of ontology concepts) provide an effective means for navigating/filtering a large set of exRNA profiles data based on different aspects of the samples.
Significance of our results and other possible extensions These substantial changes to GO reflect the current state of the exRNA field, representing the current literature from the perspective of the consensus among three major professional societies in this field (ISEV, ASEMV, and ERCC). We believe that the new exRNA-relevant terms, together with their relationships to each other and with the existing GO terms, as well as the associated gene products, will be useful for annotating the increasing number of manuscripts and datasets being generated by the exRNA community. We also expect that they will prove useful for literature mining, data repository integration, and for computational analysis of exRNA data. Given the rapid developments in this field, we view the current version of the relevant areas of the ontology to be a work in progress.
We anticipate frequent future amendments to incorporate as well as refine additional terms, relationships, and associated gene products as the community develops a more precise and comprehensive understanding of the biogenesis, biophysical properties, molecular composition, and functions of different types of exRNAassociated EVs, LPPs, and RNPs. To this end, the ERCC group aims to continually interrogate the literature and to solicit input from professional societies and the wider exRNA community. The ERCC has launched the ExRNA Portal (exrna. org), a publicly accessible website and community forum for dissemination of information, sharing of tools and data (primarily through the ExRNA Atlas), and aggregation of input. Future proposed amendments will be posted on the ExRNA Portal and will be subject to a public  comment period.
As described above, in the metadata associated with datasets submitted to the ExRNA Atlas, we permit and request depositors to not only tag datasets with the appropriate current GO terms, but to also include more precise keywords, so that over time, frequently used keywords can be evaluated as possible new GO terms. In addition, our ExRNA Atlas use case (faceted search) shows the power of exploiting multiple ontologies in a sample annotation. For example, our metadata includes a description of extracellular RNAs isolated from different types of biofluids. Ontologies such as Uberon  [48] , Nano-Particle Ontology  [49] , SNOMEDCT and Experimental Factor Ontology  [50]  contain concepts corresponding to different types of "Biofluid" (or "Bodily Fluid"). These concepts can be used alongside with the "extracellular space" concept in GO to describe extracellular RNAs that are isolated from different types of biofluids.
Conclusions In this manuscript, we have described an inter-community effort to make a substantial update to GO, focusing on terms and relationships pertinent to the new field of exRNA biology. We have demonstrated how these new GO terms can be used to annotate and search metadata associated with high-throughput datasets, such as RNAseq data. In addition, we anticipate that these terms will be useful as keywords for annotation and querying of the literature. We have also initiated a systematic, literaturesupported process for associating gene products with each exRNA-related term, such that they can be used for cellular component enrichment analysis of omics datasets (e. g. RNAseq-and gene expression data). We recognize that fundamental questions regarding the composition, biogenesis, and functions of exRNA-containing EVs, lipoproteins, and ribonucleoprotein complexes remain to be answered.
Acknowledgements This work was supported in part by the  NIH Common Fund , through the  Office of Strategic Coordination  and the Office of the NIH Director (AM:  U54DA036134 , LCL:  UH2TR000906  and  U01HL126494 , TP:  UH3TR000884 , and RR:  U19CA179512 ). Paola Roncaglia  and  Aleksandra Shypitsyna  are funded by the  Gene Ontology Consortium  ( P41  grant from the  National Human Genome Research Institute (NHGRI) , grant  5U41HG002273-14 ). Aleksandra Shypitsyna  is also funded from the  European Molecular Biology Laboratory  (EMBL). We would like to give special thanks to  Dena C. Procaccini  for coordinating meetings and conference calls facilitating discussion and collaboration as part of the manuscript writing. We would like to thank  Tony Sawford  ( Protein Function Development team, EMBL-EBI ) for running the set of ExoCarta and Vesiclepedia annotations through the validation pipeline for inclusion in the GOA database.
Selection of the panel Based on the genotypic data obtained with 498 DArT markers  [26] , we assessed the number of clonal lineages present within an initial sample composed of 224 diploid accessions with pure M. acuminata genetic background, all of which are conserved and available for distribution in the Bioversity International Transit Centre (ITC). For this purpose, the GenoType software  [28]  was used to compute the genetic distances between all pairs of individuals following the Infinite Allele Model (IAM), considering each missing data as one mutation. According to this model, GenoType identified 275 classes of genetic distances within the initial set of diploids. Based on the frequency histogram of the pairwise genetic distances (S1 Fig), we considered Threshold 9 as the end of the first peak of the distribution, which is assumed to correspond to the genetic divergences between pairs of individuals belonging to the same clone.
Phenotype determination Phenotypic data were based on the known status of each accession with regard to cultivation and wildness (information accessible through MGIS http://www. crop-diversity. org/mgis). In banana, this status is determined based on the presence or absence of seed in the pulp of the developed fruit (Fig  1 ). When introduced to the ITC genebank, the status of this non-reversible phenotype was provided by germplasm collectors. The phenotypes used in this study were scored as 1 (presence of seeds) or 0 (absence of seeds). In total, the panel is composed of 27 seeded accessions and 78 unseeded accessions. As stated above, such seedless phenotypes can actually be associated with two traits: parthenocarpy, which is fully genetically determined, and female sterility, which seems only partly genetically driven.
Read mapping Raw fastq files were processed with the standalone TASSEL-GBS (version 3. 0. 160) [33]  in preparation for alignment and SNP discovery. Illumina raw reads were assigned to their unique samples using the sample-specific barcodes present in each read. Barcode length ranged between 5 and 10 bases. Only reads that harbored an exact barcode match immediately followed by the expected remnant cut site were kept. Prior to the alignment steps, the reads were trimmed to 64 bases (including the remnant cut site). Reads containing base calls of the indeterminate nucleotide 'N' in the first 64 bases were discarded. If reads contained the beginning of the common adapter or a second cut site, they were trimmed to the beginning of the adapter start or second restriction site. Processed reads were then collapsed into a set of unique sequence tags.
SNP discovery, genotype calling and filtering SNP discovery was performed with the standalone TASSEL-GBS. Only uniquely aligned reads were considered for SNP calling. Genotype calling was carried out with the TASSEL-GBS plugins 'tbt2vcfPlugin' and 'MergeDuplicateSNP_vcf_Plugin' with a maximum of 4 alleles per SNP locus. VCFtools (v0. 1. 13) and custom Perl scripts were used to filter the 129,658 SNP markers resulting from TASSEL-GBS. Out of the 105 genotypes selected, one exhibited more than 50% of missing data (ITC0788 'Katual Vunalir') and was discarded from the analysis. Given the heterogeneous nature of our species, we decided to set stringent parameters. We thus discarded all markers with a call rate below 100% from the analysis (101,690 SNPs (78. 4%) removed) and also excluded markers exhibiting three and four alleles in the whole dataset (5,512 SNPs (4.
3%) removed) while one SNP-deletion was considered as a regular allele and recoded as a fictive base to allow the concerned sites to be kept for further analysis (1,001 markers (0. 7%) affected). Inbreeding coefficients (F is ) were calculated for each marker following  [37]  with the software Genetix 4. 05. 2  [38]. All markers not following the Gaussian distribution of the F is values (687 SNPs (0. 5%) removed). Genotypes with read depth <10 were set to missing (30,419 genotypes across 7,548 SNPs), and markers exhibiting more than nine accessions with missing genotypes were discarded (674 SNPs (0. 4%) removed). Finally, we only allowed for a Minor Allele Frequency (MAF) 0. 05 and conserved for further analysis a set of 5,544 highly-reliable markers shared by the panel of 104 genotypes. The rate of heterozygosity per individual was then calculated.
Linkage Disequilibrium (LD) calculation and investigation To evaluate the resolution expected during the GWAS analysis, we investigated the LD pattern through the estimation of the squared allele frequency correlations (r²). We used a two marker Expectation-Maximisation (EM) algorithm, ignoring missing data to estimate the maximumlikelihood values of the four gamete frequencies as implemented in Haploview 4. 2  [42] , and from which r² and corresponding LOD values were derived from the set of 104 diploid genotypes. Significant values correspond to r² with associated LOD > 2. To better visualize LD decay, significant r² values calculated on the haplotypes and obtained from linked markers, i. e. on the same chromosome, were then plotted against the physical distance (bp) between markers. A generalized additive model (GAM) was applied to our data using ggplot2 module (version 0.
To determine which of the models and correction parameters tested were best fitting the data, we plotted the negative log10-transformed observed p-values obtained for each SNP association against their expected distribution under the null hypothesis of no genetic association (Quantile-Quantile plot). Manhattan plots were displayed accordingly. We then considered both nominal and adjusted p-values. Adjustment was performed by using Bonferroni-corrected critical p-values (5% and 1%). To moderate the stringency of the Bonferroni adjustment, q-values, corresponding to the False Discovery Rate (FDR), were computed using the SFDR software  [43]. In order to validate the results obtained while analyzing the subset, we ran a second GWAS using a second, unstructured dataset using a simple GLM and a MLM K.
This dataset was composed of a subset of 33 individuals including 27 cultivated and 6 wild accessions that were identified by STRUCTURE as the largest group of unadmixed (Q>0. 8) accessions. It will hereafter be referred to as the subset. Interestingly, all the 33 individuals of the subset but one originates from Papua. This analysis was run with polymorphic SNPs with MAF 5% and 10% (2518 and 2039 SNPs respectively). Due to the small size of the sample, we flagged it as positively associated with the studied phenotype those of the SNPs that passed the Bonferroni correction only.
Identification of Candidate genes The positions of the markers significantly associated to the seedless phenotype were used as starting points to explore the genomic context of the M. acuminata reference sequence. With regard to the pattern of LD decay, we scanned the genome on a window of about 40 kb upstream and downstream the SNPs of interest to identify the genes located in the regions. In the case where two closely distant SNPs showed association (i. e. on chromosome 4 and on chromosome 7), the entire genome region located between these SNPs was explored. All the gene annotations in the selected regions were checked for their structural annotation and exon-intron boundaries were modified whenever necessary. In some cases, genes missed by the automatic annotation were added. Next, to verify the accuracy of the functional annotation assignment of the Musa genes, we identified homologous genes in other plant species, mainly Oryza sativa, Vitis vinifera and Arabidopsis thaliana using the GreenPhyl comparative genomics database  [44]  (http://www.
Marker distribution The GBS method and the filtering pipeline applied to the dataset yielded 5,544 markers (4,543 SNPs and 1,001 one base-indels) with an average nucleotide diversity (Pi) of 0. 29. Even though markers with a MAF below 5% were discarded, the MAF distribution of the markers was skewed toward low frequencies with 31. 1% of the markers displaying a MAF below 10%. Approximately 69% of these markers were located into genic regions (i. e. 5' UTR, CDS, intron or 3' UTR). The heterozygosity rates for each individual of the panel ranged between 0. 04 and 0. 53 (Table  1 ). We observed an average rate of 0. 19 (from 0. 04 in ITC0806 'Banksii' to 0. 46 in ITC0896 'Musa acuminata banksii') in the wild-types and of 0. 29 (0. 14 for ITC0818 'Enar' to 0. 49 for ITC0294 'Pitu') for the edible types.
Panel structure The genetic structure of the panel was investigated following the model based approach implemented in STRUCTURE. Firstly, we used a dataset of 498 DArT markers already available to determine the most likely true of the numbers of genetic clusters (K) present in the panel. The LnP(D) appeared to be a stable increasing function of K for all the values observed from 1 to 4 and started to exhibit high variance at K = 5 and then stabilize newly at K = 8. We therefore considered K = 4 as the most likely true of the values of K tested. At K = 4, the first cluster is composed of 11 unadmixed (Q>0. 8) accessions including 10 wild types originating in SE-Asia and one improved material ITC1193 Diploid EMBRAPA bearing seeded fruits. The cluster 2 is the widest and is composed of 33 unadmixed accessions including 27 cultivated originating in Papua at the exception of one, ITC0299 Guyod originating in the Philippines, and 6 wild accessions belonging to the subspecies banksii.
Pattern of LD The analysis performed by the Haploview software on the 104 genotypes, showed that 26. 09% of the 1,417,890 marker pairs were in significant LD (LOD>2) In the first distance class corresponding to the 0 to 10 kb interval, the average LDs, expressed as the average r² values, were 0. 25 (with 54. 03% significant values exhibiting an average r² of 0. 47). We noticed that this initial value of r² dropped to half its initial value within 10 to 100 kb and then decreased slowly without reaching background LD, i. e. average r² obtained for markers distant of more than 10 Mb. We noted that 39. 35% of the markers pairs are still significantly in LD after 100 kb. The plots of the r² values against the physical distance are presented in Fig 3. The smoothened LD curves, obtained by applying generalised additive models (GAM) to our data, show a secondary increase in r²).
SNP dataset and Genetic Structure of the Panel In this paper, we presented the selection, genotyping-by-sequencing and subsequent analyses of a panel of accessions deemed to be used in GWAS. The stringency of the parameters used to filter the data associated with the known presence of different genepools within the panel resulted in a reduced number of SNPs. It is known that the different subspecies of M. acuminata are heterogeneous, exhibiting various levels of diversity and heterozygosity  [54]  along with structural rearrangements  [55]. It is therefore expected that the mapping of the reads on one of these subspecies, here M. acuminata malaccensis, would alter SNP calling for the others and for those of the cultivated accessions carrying genetic backgrounds different from malaccensis. Taking into account the expected highly hybrid status of most of the cultivated accessions, and the likely absence in the panel of at least one of the ancestral genepools  [26] , we intentionally allow few missing data in our dataset to avoid erroneous imputations.
Genome-Wide Association Study for seedless phenotype Given the LD pattern of the panel, the genome coverage of the selected SNPs is not optimal and we may have missed markers truly associated to our phenotype. In addition, and given the likely occurrence of long-distance LD, the possibility of detecting regions distantly associated with causal SNPs cannot be ignored even though we did not identify such cases. We aimed to present the panel and show its potential to successfully perform GWAS using easy-to-handle analysis tools, notably TASSEL, one of the most used software to perform association studies in crop plants. Given that the panel i) was suspected to exhibit complex and unbalanced levels of genomic structure  [26] , ii) is composed of a relatively small set of accessions, therefore decreasing imputation accuracy  [59]  and that iii) missing data imputation may introduce substantial biases in downstream genetics analyses  [60] , we intentionally filtered out missing data to avoid imputation and to get highly reliable markers.
This panel of accessions is freely available for distribution at the ITC and documented on MGIS (http://www. crop-diversity. org/mgis/content/gwas-panel-gbs) with links to all SNP datasets that are publically available and visualizable. Even though GWAS for complex traits underlined by genes with moderate or small effects has not been tested yet with this panel, it is likely that these elements will allow a broader use of GWAS in banana and will speed-up the process of discovering genes coding for traits of interest at the level of the banana community  [69]. Finally, Brachi et al  [70]  proposed different schemes to further improve the accuracy and resolution of the results of GWAS in plants. However, these recommendations mostly affected the elaboration of the plant samples to be used. It was proposed to develop multiple bi-parental crosses or multi-parent intercrosses for the study of broadly adaptive traits and then to identify and sample regional subsamples to study locally adaptive traits.
Acknowledgments This work would not have been possible without the support and authorization of RTB, the  CGIAR Research Program on Roots, Tubers and Bananas. We acknowledge  Sharon Mitchell  and the  Cornell University Biotechnology Resource Center  for the generation of the GBS data. We thank the  South Green Bioinformatics Platform  (http://www. southgreen. fr/)for providing us with computational resources to analyze and store the data. We also thank  Dr. B. Irish  ( USDA-ARS ) for providing the pictures of banana fruits and  Rachel Chase  ( Bioversity International ) for her careful reading of the manuscript. Finally we want to thank the Academic Editor  K. Olsen  and the two anonymous reviewers for their helpful comments and suggestions.
Data from Genotyping by sequencing (FASTQ files) were deposited in NCBI's Sequence Read Archive (SRA) database with accession numbers from SRX1583991 to SRX1584160 (BioProject PRJNA305234 -Study SRP070070). Associated SNP dataset were also deposited into dbSNP (being processed -BAN_GWAS code) and was deposited in 7910/DVN/ NZQBAH. Additionally, passport data and an interactive diversity tree for all accessions //www. crop-diversity. org/mgis/) at http://www. crop-diversity. org/mgis/content/gwas-panel-gbs.
Physics (CNRS/IN2P3 ) is involved in educational actions promoting the use of cosmic ray detectors in high schools. These projects are ran in close collaboration with teachers to ensure that they match the letter and spirit of the curriculum. These instruments, lent to teachers for periods ranging from a few weeks to a few years depending on the measurements possible with them, complement nicely the lectures. They allow the students to sense the elementary particles and to understand the problems raised by their detection. The well-recognized cosmodétecteur and the new COSMIX detector are described in the following, together with the COSMAX software framework, which gives a direct access to the Fermi satellite data.
In France, the National Institute for Nuclear Physics and Particle Physics of the CNRS (in short CNRS/IN2P3) is using its high expertise in particle physics detectors to design, build and maintain cosmic ray detectors for high schools. The two main such projects, the "cosmodétecteur"  [1]  and the "COSMIX" detector  [2] , are described in the following. Devices more suited to outreach exhibits, like the "Cosmophone" or the "Cosmic Arch", exist as well. As the number of cosmic ray detectors available to teachers is limited, complementary approaches are pursued in parallel. Following the successful example of the LHC-based International Masterclasses [3], the COSMAX software framework  [4]  is being developed to give an easy access to the data of the Fermi satellite and to the associated analysis tools.