dataesr/scientific-paragraphs-categorization huggingface dataset:
Gene ontology analysis The functional classification of all DAS genes identified in the different comparaisons was carried out using their corresponding v2 annotations and the Gene List Analysis tool of the PANTHER 15. 0 Gene Ontology Resource (www. panth erdb. org/), or failing that, by a BLAST search in the genome data resource of the European Bioinformatics Institute (EMBL-EBI) Ensembl Plants (plants. ensem bl. org/ index. html). The annotation data sets selected in the PANTHER tool were GO Slim-BP, -MF and -CC. GO-term enrichment was statistically determined using the PANTHER overrepresentation test (Fishers' Exact with the FDR correction, at the threshold of 5%).
Acknowledgements We thank  Nathalie Jaegli  and  Vincent Dumas  ( INRAE, Colmar, France ) for their assistance in monitoring grape ripeness in the vineyard and collecting berries at the right developmental stage, and the GenomeEast platform for carrying RNA sequencing. We also warmly thank the ' Comité Interprofessionnel des Vins d' Alsace ' for their financial support to DNA sequencing.
Array CGH Whole-genome array comparative genomic hybridization (CGH) was performed as per the manufacturer's instructions on 24 germline DNA samples from the CMN cohort, using Roche Nimblegen 135K oligonucleotide arrays and sex-matched commercial pooled controls. 1-3 μg of patient DNA and control DNA (Megapool reference DNA, male: EA-100M, female: EA-100F, Kreatech, The Netherlands) was labeled using NimbleGen Dual-Color DNA Labeling Kit and hybridized to the oligonucleotide array using the NimbleGen Hybridization System. Two-color array scanning was performed using a Molecular Devices GenePix 4400A (Molecular Devices, Sunnyvale, CA, USA) at a resolution of 2. 5 microns. Data were extracted using Deva software (NimbleGen), and analyzed using InfoQuant CGHFusion (version 5. 7. 0-6. 1. 0) or later Chromosome Analysis Suite 4.
All samples were normalized compared to these ratios and R studio was used to visualize and calculate gene coverage data across all samples. Generation of stable inducible PPP2R3B cell lines SKMEL2 and SKMEL30 melanoma cell lines carry variants affecting codon 61 of NRAS, and were cultured as per manufacturer's instructions. Normal PPP2R3B copy number in both cell lines was verified by next-generation sequencing (NGS). Human myc-FLAG tagged PPP2R3B ORF clone from Origene (RC222908) was linearized and the insert DNA amplified using modified primers generating an N-terminal Myc tag. The In-Fusion® HD Cloning system (Takara, 638909) was used to allow directional cloning of the PPP2R3B insert into the AgeI-MluI site of the lentiviral vector pTRIPZ (GE Healthcare) resulting in the final PPP2R3B (tet-ON) construct, without the TurboRFP or shRNAmir-related elements of the parental pTRIPZ plasmid.
RNAseq RNA integrity was assessed using a Bioanalyser (Agilent). Library preparation using KAPA messenger RNA (mRNA) HyperPrep Kit (Roche) was automated using the Hamilton robot, and sequenced using a NextSeq 500 (Illumina, San Diego, CA, USA) with a 43-bp paired-end run. Data were trimmed for 3' adapter sequences using Cutadapt 1. 9. 1, after which they were aligned to the Ensembl GRCh38 release 86 human transcriptome using STAR 2. 5. 2a. Individual lane level replicates were merged using Samtools 1. 8, raw gene counts estimated using RSEM 1. 3. 0, and normalization and differential expression called using DESeq2. A corrected p value of <0. 05 was deemed significant. Pathway analyses based on genes reported in the various analyses were performed using Metacore (Clarivate Analytics).
271%. Population data from normal individuals from the Database of Genomic Variants 12 identified similar duplications in only 1/36,000, or 0. 003%,  13  and 0. 5% in nearly 7,000 individuals in the MSSNG autism study,  14, 15  with no difference between cases and controls (personal communication). High-depth targeted NGS was eventually selected as the most robust readout for copy number in this repetitive GC-rich telomeric region. NGS confirmed the CMN array findings and on screening leukocyte DNA from an adult sporadic melanoma cohort, identified the same germline duplications in 4/168 (2. 4%) (all BRAF p. V600E), (Fig. 1b ), demonstrating that this copy-number variant is enriched in populations with melanocytic neoplasia. Custom-designed multiple ligation-dependent probe amplification (MLPA, MRC Holland) validated the array CGH findings and suggested the prevalence of duplications involving PPP2R3B to be 5% of the total cohort of 125 individuals with CMN (Fig.
(b) PPP2R3B duplications in a UK nonsyndromic melanoma cohort (4 duplications in nonselected cohort n = 168), and CMN cohort (3 duplications in known preselected cohort n = 5) shown by targeted next-generation sequencing (NGS) of PPP2R3B, in addition to the two telomeric genes (GTPBP6 and PLCXD1) and the next centromeric gene (SHOX). Data represent the ratio of corrected read depth (see text for details) across the whole of PPP2R3B with respect to the ratio across the whole of SHOX. Each bar represents an individual patient. PPP2R3B duplications called are shown in light blue: validation of the array CGH findings in the three CMN patients are clustered to the right of the figure, and new duplications in the melanoma cohort in the rest of the figure (n = 4, 2. 4%). Validation of PPP2R3B duplications detected by array CGH.
(a, b) Immunohistochemical staining of formalin fixed paraffin embedded (FFPE) CMN tissue demonstrates moderate intensity PR70 staining throughout the cytoplasm of nevus cells in two patients where tissue was available with a confirmed germline PPP2R3B duplication and (c-f). Negative PR70 staining in four patients with confirmed normal copy number of PPP2R3B. Stained sections were assessed by two independent blinded assessors and assigned a score of 1-3, based on the intensity of staining observed and scores averaged. Scores were as follows: a = 3, b = 2. 5, c = 0, d = 0, e = 0, and f = 0. Increased PPP2R3B expression in melanoma tissue is correlated with improved melanoma specific survival. (g) Kaplan-Meier curve generated from transcriptomic data from 703 FFPE melanoma tumors from the Leeds Melanoma Cohort, hazard ratio (HR) = 0.
Common to all was PPP2R3B, which encodes PR70, ubiquitously expressed in the cytoplasm, and one of the ß" family of regulatory units of the critical phosphatase and regulator of the cell cycle PP2A. 27, 28 PP2A is a heterotrimeric holoenzyme consisting of a structural A subunit, a catalytic C subunit, and a regulatory B subunit. 27, 29 The numerous nonhomologous regulatory subunits are classified into B, B', B", and B"' subfamilies, implicated in control of enzyme activity and substrate specificity. 27, 30 PP2A operates via key effector pathways RAS/ MAPK, Wnt, and AKT/mTOR. 27, 28 ] [33] [34]  Analysis of the TCGA database demonstrates that copy-number variants including PPP2R3B are more common across cancers in general than single-nucleotide variants, suggesting that dosage of PPP2R3B is relevant in cancer development.
None of the CMN patients with duplications have so far developed melanoma; however, this is in line with what would be expected statistically, as the incidence of melanoma in CMN at this age is very low,  36  and no conclusions can yet be drawn about potential association between the PPP2R3B duplications and outcome in CMN. Interestingly, however, our data demonstrate clearly that increased PPP2R3B expression correlates with improved disease-specific survival in melanoma, mirroring the significant protective effect of PPP2R3B expression in urothelial cancer and pancreatic cancer data sets from the TCGA database. 37 A significant association between expression and survival is not seen in the smaller melanoma TCGA data set; however, this may be due to lack of statistical correction for known associated factors.
Statistical significance was determined using a Student's t-test (Prism v7. 0, Graphpad). Statistically significant values are indicated by a single asterisk (p < 0. 05), a double asterisk (p < 0. 01), a triple asterisk (p < 0. 001), or a quadruple asterisk (p < 0. 0001). Knockdown of MITF leads to decreased expression of C21orf91. Quantitative real-time polymerase chain reaction (qRT-PCR) demonstrating decrease in both MITF and C21orf91 messenger RNA (mRNA) following transfection by two different siRNAs targeting MITF transcript (siMITF 1 and siMITF 2) in SKMEL2 cell line (e). Relative fold change in gene expression compared to control cells transfected by nontarget siRNA (siSCRA), standardized to GAPDH, mean + SD of 3 independent experiments. Knockdown of C21orf91 in cells rescues MITF-driven increase in proliferation.
ACKNOWLEDGEMENTS We gratefully acknowledge the participation of all patients. V. A. K. , A. C. T. and the work presented in this study were funded by the  Wellcome Trust  (grant  WT104076MA ). S. P. was funded by  Caring Matters Now Charity  and by the  Newlife Foundation  (grant  15-16/10 ). The work was supported by the  Great Ormond Street Hospital Children's Charity (GOSHCC) Livingstone Skin Research Centre , and by the  UK National Institute for Health Research through the Biomedical Research Centre at Great Ormond Street Hospital for Children NHS Foundation Trust , and the  UCL GOS Institute of Child Health.
DATA AVAILABILITY Array CGH data have been submitted to http://www. ncbi. nlm. nih. gov/clinvar/. Melanoma transcriptomic data were deposited into the European Genomephenome Archive (EGA) (accession no. EGAS00001002922 -https://ega-archive. org/ studies/EGAS00001002922), access request to j. a. newton-bishop@leeds. ac. uk. Cell line PPP2R3B overexpression RNAseq data were deposited at the Gene Expression Omnibus (GEO) with accession number GSE145195, https://www. ncbi. nlm. nih. gov/ geo/query/acc. cgi?acc=GSE145195, access request to veronica. kinsler@crick. ac. uk.
As outlined in Salmaso et al. 18  many studies use functional classifications and/or functional groups with the objective to better investigate links between phytoplankton communities and the main environmental factors. Some classifications mentioned by Salmaso et al. 18  are based on one characteristic (size classes) or several (morphologically based classifications) while others use functional properties (functional or morpho-functional groups). Indices based on functional groups were also published for lakes  19  and for rivers  20  to be used for the implementation of Water Framework Directive, WFD  21. Nevertheless, as explained by Padisák et al. 22  functional groups were not built using all encountered taxa. Ecologists have to adjust their groups manually or through an R code for the Reynolds Functional Groups  23.
These additions, may not be adequate and lead to misinterpretations. According to Wentzky et al. 24  , the use of functional-traits at the species level (and not at higher taxonomical level) can limit these issues without influencing any assumption about taxon functional group. Thus, data collections of phytoplankton traits at the species level are required for a large number of taxa. Accordingly, different datasets were created considering the taxa size. Olenina et al. 25  reported size-classes (PEG biovolume, http://ices. dk/data/vocabularies/Documents/Forms/AllItems. aspx) but mainly for marine waters. Kremer et al. 26  established a dataset for freshwater and estuaries for a large number of taxa (>1200 species), which had the advantage that it considered cell size and natural unit size (colonial and filamentous growth forms).
At the French level, Druart and Rimet created a trait dataset including only biovolumes  27  and published, more recently, a phytoplankton trait dataset  28  mainly based on Alpine lake surveys and including around 10 compiled traits. To our knowledge, specific traits such as lorica, protuberance presence (and kind of protuberance), potential toxicity associated with potential toxin production by cyanobacteria, heterocyte and vacuole presence have never been merged together in one trait database. More generally, to our knowledge, no database covers a large number of ecological and morphological traits for phytoplankton communities at the French or European level. To be able to use many morpho-functional traits for water bodies in ecological analyses in France, the construction of such a dataset was initiated.
This was based upon the survey database established for the WFD application on French water bodies since 2005. This dataset will be particularly helpful for studying the relationships between functional traits and environmental variables, as well as to develop new trait-based indicators for water quality assessment. We aim in this paper to (1) compile morpho-functional traits of phytoplankton taxa in French water bodies (metropolitan territory) (2) offer information of the corresponding phylogeny (3) provide a dataset helpful for analyzing functional and morpho-functional phytoplankton assemblages from local to national scale (4) deliver a dataset allowing consistent data comparisons between European countries.
Methods Site description. This work was based on the French national database consisting of phytoplankton communities surveyed in 384 lakes between 2005 and 2016 and used for applying the Water Framework Directive. As each lake has been sampled several times this database contains 2,987 phytoplankton samples. Sampled lakes cover the whole French territory (Fig. 1 ) and encompass all natural and artificial types of lakes encountered in France: from lowlands to alpine location, from small to large lakes (0. 09 to 577. 12 km 2 ), from shallow to deep lakes (mean depth from 0. 30 to 153 meters) and also from oligotrophic to hypereutrophic environmental conditions. Data acquisition and compilation. In order to create our dataset we firstly defined the taxa list recorded in the national database.
The extracted list was firstly examined for taxonomic consistency. Taxa only determined at a high taxonomic level (order, class, phylum) were rejected from the list. Thus, genus and below this level (species, variety, form, etc. ) were conserved to obtain an accurate taxonomical list for morpho-functional traits (e. g. biovolume, cell form). A taxa name in the national database can be different over time because of taxonomic changes and determination by different operators. To harmonize taxa denomination and to obtain one up-to-date taxa list, the original names used in the lake database were corrected following the most recent research reported on the AlgaeBase website (https://www. algaebase. org/ -last consultation on 31 st July 2020) and in scientific papers. This will allow the traits table to be used for future studies.
Thus, a list of 1,318 taxa, associated with abundance and biovolume data, was obtained from the database. In order to remove idiosyncratic species and any potential determination errors, we only kept taxa which occurred in more than 15 samples. A final taxa list of 636 taxa was obtained. Secondly, we selected a list of 53 morpho-functional traits that we considered to be the most relevant for the description/analyses of functional phytoplankton communities (see Online-only Table  1  for detail of these traits). We compiled available trait data from published sources: we used general books (John et al. 29  ; Reynolds 3 ; Wehr et al. 30  ) for all taxa and specific references listed in Table  1  for more precise information of some traits or taxa. The traits not available in the literature (biovolume, size class, carbon contents, etc.
) were determined/calculated. All available synonyms of the taxa recorded in the literature were taken into account in order to consider morpho-functional traits taken from as a many taxonomic references as possible. Taxonomic information was essentially obtained from the AlgaeBase website. From these publications we mainly obtain morphological characteristics such as cell form, presence of ornamentation on cell and number of chloroplasts. If the information was divergent between two references, the most recent is accepted. To facilitate the use of the dataset in statistical analyses, taxa code (6 letters in upper case), used in the free computer software Phytobs  31  (commonly used in France for freshwater phytoplankton counting), was added for each taxon. These codes were constructed with the first 3 letters for genus and the last 3 for species and infra specific denomination.
In order to have a global clustering of phytoplankton taxa (sometimes useful for result presentations), more or less independent of phylogeny updates and thus stable in time, we included a phytoplankton group name. We classified phytoplankton taxa in 9 groups based on common clustering of algae and cyanobacteria such as chlorophytes (green-algae), diatoms, xanthophytes (brown-algae), chrysophytes (gold-algae). The trait life form was added to the dataset and is defined as the common living form of the taxon in its natural environment (cell, colony or filament). Because some taxa could exhibit a second life form in nature, this information was also indicated in the dataset. We determined the cell and individual form traits for each taxon with the use of 14 simple geometric forms: sphere, cylinder, rotational ellipsoid, flattened ellipsoid, cymbelloid, staurastrum and ceratium form, oval cylinder, double cone, parallelepiped, prism on parallelogram base and on triangulare base, cone with half sphere and tetrahedron as defined in the computer tool Phytobs  31  and illustrated in Fig.
2. Choices were made with the help of book taxa illustrations and according to Hillebrand et al. 32  , Olenina et al. 25  , Druart and Rimet  27  , Hutorowicz  33  and Padisak and Adrian  34. The minimum and maximum cell dimensions were taken from the literature and were used for the definition of the size class (see below). We determined taxa biovolume following the European standard NF EN 16695  35  with the use of the software Phytobs  31  for the calculation (this counting software provides also a biovolume calculation tool). Thus, from real samples observed during the period of the study, 30 individuals of each taxon were measured, as recommended by the European standard, to calculate a mean cell biovolume. For taxa not observed in enough quantity, common minimum and maximum dimensions were taken from the literature in order to determine mean dimensions and calculate a corresponding mean cell biovolume.
We also specified the mean carbon content per cell in our dataset. We used the formulas described in Menden-Deuer and Lessard  36  and reproduced in European standard NF EN 16695  35  because they consider the decrease in specific carbon content with cell size. Thus, the general formula used for phytoplankton is Carbon [pg C] = 0. 216 × Biovolume 0. 939 and the specific formula for diatom taxa is Carbon [pg C] = 0. 288 × Biovolume 0. 811. Three size class systems were also subsequently defined in the dataset. The first two use the Sieburth et al. 37  class limits and are based on the cell dimensions. The third one uses the biovolume thresholds defined by   1. Specific books and references used for some traits and taxa for the different phyla. Ignatiades  38. For Sieburth et al. class limits, two variables were defined: one using the minimal individual length and the second using the maximal individual length.
These characteristics were mainly extracted from the literature. The precise number of plastids and flagella were also added. Some distinctions were defined for the protuberance type, their size and their number onto the individuals. The protuberances were split into seven different types including granule, needle or bristle and the size was divided in two size classes (large and small). The main nutrition mode was indicated for each taxon following the common definitions: autotroph, mixotroph and heterotroph  44, 45. The reproduction mode was specified more precisely when mentioned in the literature or just indicated as sexual or asexual when not. Some taxa are known only from freshwater and others also from the marine environment. Thus, this information was indicated as a Water_Type in the trait dataset.
The trophic status of the water where the taxon lives was defined under "Water_Trophy" as from oligotrophy to hypereutrophy. In the case of a taxon with a large trophic range, the main trophic status was indicated. In some literature and mainly in Reynolds  3  , the tolerance to abiotic parameters are defined. We also indicated this trait as a tolerance trait in the dataset (literal form). We finally reported in the dataset the Reynolds functional groups, based on the original description of Reynolds  3  and developed further (with more taxa) by in Borics et al. 20  and Padisak et al. 19, 22.
Data Records The phytoplankton morpho-functional traits dataset  46  is in the form of a large table with taxa in rows and their corresponding morpho-functional traits in columns. This trait table is contained in a formatted file with semicolon-separated values (*. csv) and stored in a public repository available on https://data. inrae. fr/dataverse/eabx. with the following DOI: https://doi. org/10. 15454/GJGIAH. The name of the file was defined as FRENCH_ PHYTOPLANKTON_TRAITS. csv. This trait table is designed to be updated with new data especially from French lakes (new surveys and new taxa occurrences) and/or new morpho-functional traits relevant for the study of phytoplankton communities. In the table some variables are numerical and continuous such as biovolumes or cell dimensions, other are categorical such as life forms and other boolean as toxin production.
Consequently, the table is a mixed format with numeric and text. The different variables are listed with their attributes in Online-only Table  1  to facilitate understanding and reuse of the data. Missing information was labelled '#NA'. In the dataset, the chlorophytes group is the most represented with 36. 5% of the total (Fig. 3 ) and cryptophytes is the least (2. 2%). Other groups like cyanobacteria, diatoms and chrysophytes represent between 10 to 20% of the dataset taxa. The individual life form of each taxon listed is mainly represented by the cell form (56. 8%), followed by the colonial form (32. 9%) and, finally, the filamentous form of around 10% (Fig. 4 ).
technical Validation The technical validation of the data stored in the dataset was undertaken following the scheme outlined in Fig. 5. All the information collected from the literature was double-checked and all sources are provided in the methods section above. All measures (biovolume, carbon content, size classes attribution, etc. ) were made by the same individual in order to reduce error. After that, all data included in the traits table were controlled by the same person (C. L. T. ), an expert on French phytoplankton. The dataset thus created was explored through preliminary analyses with box-plots and  histograms in order to check distribution and to highlight erroneous data (outliers, aberrant text, missing data, etc. ). Many cross-checks were undertaken in order to highlight missing and inconsistent data (C.
Usage Notes The phytoplankton trait dataset is freely available and stored in https://data. inrae. fr/dataverse/eabx public repository and should be appropriately referenced by citing the present paper. A survey taxa list should be first checked for synonymy before being analyzed with the morpho-functional dataset. This will avoid the mismatch of taxa names. We strongly encourage users to use the synonymy database present in the Phytobs software. Note that many traits were obtained from literature indicating general characteristics of species or genera and do not represent individual responses to environmental conditions. Some of them can be affected by different environmental conditions. For example, traits based on dimensions (min, max length and width), biovolume, carbon content and in some cases size-classes can be directly influenced by environmental conditions  36, [47] [48] [49]  as well as the presence of heterocyte and akinete for some cyanobacteria taxa  50.