dataesr/scientific-paragraphs-categorization huggingface dataset:
maritimum type strain average spectra. For each peak, the corresponding ribosomal protein is indicated with the degree of ionization (H1, H2 and H3 corresponding to 1, 2 and 3 H + ) and the presence (M) or absence (m) of the first methionine. Color code: red line for the T. maritimum type strain NCIMB 2154 T and black for the sequenced T. maritimum isolates. Additional file 8. Quality control and T. maritimum species identification. The full dataset is composed of representatives of 24 Tenacibaculum species including 135 isolates belonging to the species T. maritimum. It encompasses 476 independent acquisitions (one acquisition corresponds to an average spectrum of several technical replicates) corresponding to 5102 spectra including technical and biological replicates. This dataset was divided into two groups: the positive control group (T.
True and false positives correspond to isolates correctly and incorrectly identified as T. maritimum (TP and FP), respectively. On the other hand, true and false negatives correspond to correctly and incorrectly rejected isolates (TN and FN, respectively). Positive isolates correspond to those having an identity value above a defined threshold. Using the full dataset, the number of TP and FP and the number of TN and FN were counted. The accuracy of the tool [i. e. , (TP + TN)/(TP + TN + FP + FN)] was then computed by increasing the threshold value from 0% to 100% by a 0. 1% step. One could observe than the accuracy varies from 0% to 100% and is maximal (i. e. , above 97%) between 25% and 60% of threshold value (Figure  B ). It is therefore proposed that a 60% threshold value safely identifies isolates as belonging to the T.
Acknowledgements This study was funded by the  AAP Ressourcement Scientifique   F2E 2017  project " New tools for diagnosis of bacterial fish pathogens ". The authors are very grateful to  Klervi L'Her  ( Bio Chêne vert ) for her participation in the sample preparation and MALDI-TOF acquisitions, to the many individuals listed in the legend to Additional file 1 for kindly providing bacterial strains, to  Pierre-Yves Moalic (Labofarm)  for fruitful discussions and to  Zoé Rouy  ( MicroScope Platform ) for her kind help with genome data management. This work has benefited from the facilities and expertise of the high-throughput sequencing facility of  I2BC for its sequencing and bioinformatics expertise  ( Centre de Recherche de Gif  [54]) and of the CEA/GENOSCOPE [55]. We also wish to thank for providing computational resources: the  INRAE MIGALE bioinformatics platform [56] , the  LABGeM , and the  National Infrastructure "France Génomique " funded as part of the  Investissement d'avenir program  managed by  Agence Nationale pour la Recherche  (contract  ANR-10-INBS-09 ).
Availability of data and materials All genome sequencing data have been deposited in the European Nucleotide Archive (Accession numbers: GCA_902705265, GCA_902705275, GCA_902705285, GCA_902705305, GCA_902705315, GCA_902705345, GCA_902705355, GCA_902705365, GCA_902705375, GCA_902705385, GCA_902705395, GCA_902705415, GCA_902705425, GCA_902705435, GCA_902705445, GCA_902705465, GCA_902705495, GCA_902705515, GCA_902705525, GCA_902705535, GCA_902705555 and GCA_902705565).
HS proteoglycans, synthesized by chondrocytes and secreted to the extracellular matrix of the growth plate, play critical roles in growth plate signaling and remodeling. To date, hundreds of EXT1 and EXT2 variants have been registered in the Human Gene Mutation Database (HGMD) 1 1 http://www. hgmd. cf. ac. uk/ac/search. html (Stenson et al. , 2020)  as well as in the EXT1 2  and EXT2 3  locusspecific mutation databases. Most of the known pathogenic variants cause truncation of the encoded proteins, e. g. , frameshift, non-sense, and splice site mutations; pathogenic missense variants are uncommon  (Santos et al. , 2018; Fusco et al. , 2019). The application, more recently, of structural variantdetection techniques such as multiplex ligation-dependent probe amplification (MLPA) and microarray analysis has served to increase the variant detection rate from 70 to 85%  (Li et al.
Whole-Exome Sequencing Genomic DNA was fragmented randomly and then purified by means of the magnetic particle method. Sequences were captured by Agilent SureSelect version 4 (Agilent Technologies, Santa Clara, CA) according to the manufacturer's protocols. The DNA libraries, after enrichment and purification, were sequenced on the NextSeq500 sequencer according to the manufacturer's instructions (Illumina, San Diego). The sequencing reads were aligned to GRCh37. p10 using Burrows-Wheeler Aligner software (version 0. 59)  (Li and Durbin, 2009). Local realignment and base quality recalibration of the Burrows-Wheeler aligned reads were then performed using the GATK IndelRealigner 5  and GATK BaseRecalibrator 6  , respectively. SNVs and small indels were identified by the GATK UnifiedGenotyper 7.
Variants were annotated using the Consensus Coding Sequences Database at the National Centre for Biotechnology Information 8. Multiplex Ligation-Dependent Probe Amplification, Fluorescence in situ Hybridization and Whole Genome Sequencing These methods have been described in detail elsewhere  (Su et al. , 2015; Wang et al. , 2015). Reference Sequences and Variant Nomenclature NM_000127. 2 and NM_207122. 1 were used as EXT1 and EXT2 mRNA reference sequences, respectively. Variant nomenclature followed HGVS recommendations  (den Dunnen et al. , 2016). Variants were checked against the Human Gene Mutation Database (HGMD) 9  as well as the EXT1 or EXT2 locusspecific mutation database to identify previously described lesions (as of 12 Sep 2020). For those that were newly described here, their nomenclature was verified via VarSome 10  (  Kopanos et al.
Pathogenicity Predictions The PP3 rule established by VarSome  (Kopanos et al. , 2019 ) was adopted to predict the pathogenicity of EXT1 missense variants as previous described  (Gueguen et al. , 2020). The PP3 verdict was based upon the combined consideration of predictions from a dozen in silico programs including BayesDel_addAF, DEOGEN2, FATHMM-MKL, M-CAP, MVP, MutationAssessor, MutationTaster, PrimateAI, REVEL and SIFT. In silico splicing prediction was performed by means of Alamut R Visual v. 2. 11 rev. 0 11  (Interactive Biosoftware, Rouen, France) and SpliceAI. Alamut includes five prediction algorithms, namely SpliceSiteFinder-like, MaxEntScan, NNSPLICE, GeneSplicer, and Human Splicing Finder. SpliceAI is a recently developed deep residual neural network for splicing prediction  (Jaganathan et al.
The scanning results were analyzed using BlueFuse Multi V4. 5 software provided by Illumina. The haplotypes in each pedigree was performed in the EXT1 regions as well as 2 Mb upstream and downstream of this gene. At the same time, the B allele and Log R Ratio data were obtained from the scanning results produced by Illumina Genome studio 2. 0 software for aneuploidy screening. Next-generation sequencing (NGS) platforms were used for the two PGT cycle in Family 5-II-3. The EXT1 region was selected as the target region. A total of 31 high density and closely linked SNPs mapping within 2 Mb upstream of the gene and 33 linked SNPs mapping within 2 Mb downstream of the gene, were selected. Following mixing with corresponding PCR amplicons of the SNPs and mutations, the WGA products were used for the library construction of NGS.
DNA purification, cDNA library construction and sequencing using the Illumina MiSeqDX were performed sequentially. Haplotypes were established by selection of several informative SNPs. The mutations in all samples were subjected to Sanger sequencing for verification. For PGT-A (PTG for aneuploidies), the VeriSeq PGS-MiSeq kit (Illumina) was used to prepare the NGS libraries. The MiSeq Reagent Kit v3-PGS (Illumina, San Diego, CA, United States) was used to perform dual-index 36-bp read sequencing according to the VeriSeq PGS recipe. MiSeq Reporter software (Illumina, San Diego, CA, United States) was used to make on-board secondary data analysis  (Fiorentino et al. , 2014). Detection and classification of aneuploidies was determined by copy number variation (CNV) values. The frozen-thawed embryos without EXT1 mutations were transferred as previously described  (Liu et al.
Pathogenic EXT1 and EXT2 Variants Found in the Patients The EXT1 and EXT2 variants detected in 18 of the 22 families (i. e. , families 1-18) are summarized in Table  2  alongside the detection methods employed. Of the 16 unique variants, the three large duplication or deletion variants (namely, duplication of EXT1 exons 2-8, deletion of the entire EXT2 gene, and deletion of exons 2-8 of EXT2) have been previously described in  Su et al. (2015)  and  Wang et al. (2015). Of the remaining 13 variants, 6 were already logged in HGMD and/or EXT1 and EXT2 locus-specific mutation databases whereas 7 were not (as of 12 September 2020). The Sanger sequencing chromatograms for the seven newly described variants (5 in EXT1 and 2 in EXT2) are shown in Supplementary Figure  S1. All 16 variants were either absent or present at extremely low frequency (<0.
00001) in the Genome Aggregation Database (gnomAD) 13 (Table  2 ). Additionally, in families (excepting the mutation-negative Families 19-22) where additional members were available for genetic analysis, the respective variants were invariably found in patients but not in healthy subjects (Figure  1 ). Of the 16 unique variants, 12 appear to be loss of function by virtue of their nature, irrespective of whether or not they had been previously described; these included all frameshift (n = 5) and non-sense (n = 4) variants and the gross duplication and deletion (n = 3) variants. Of the two splice site variants identified, EXT1 c. 1284 + 1G > C was also highly likely to be of pathological significance due to its disruption of the canonical 2bp GT splice donor site  (Lin et al. , 2019) ; the Alamut prediction concurred with this assumption (Figure  3A ).
The pathogenic relevance of the EXT2 c. 1173 + 2dupT variant was not however immediately apparent because it did not alter the canonical 2-bp GT donor splice site. Alamut was therefore employed to predict its impact on splicing. As shown in Figure  3B , all five in silico algorithms provided by the Alamut suite under default conditions predicted that the variant allele would exhibit significantly reduced splicing potential as compared to the wild-type allele. We further employed the recently developed SpliceAI  (Jaganathan et al. , 2019)  to predict the impact of the two splice-site variants on splicing and, as previously described  (Chen et al. , 2020) , we focused our analysis exclusively on the Delta scores of donor loss although other scores may provide clues as to the nature of the resulting aberrantly spliced transcripts of splice-altering variants.
Both variants were predicted to have a high probability of altering splicing, the Delta scores of donor loss being 0. 91 and 0. 99 for EXT1 c. 1284 + 1G > C and EXT2 c. 1173 + 2dupT, respectively (Delta scores range from 0 to 1, with 1 indicating the highest probability of altering splicing). As for the two missense variants (both in EXT1), c. 1019G > A (p. Arg340His) has been reported in > 30 MO patients according to the EXT1 locusspecific mutation database and was predicted by VarSome to be pathogenic (verdict based on 11 pathogenic predictions from BayesDel_addAF, DANN, DEOGEN2, EIGEN, FATHMM-MKL, M-CAP, MVP, MutationAssessor, MutationTaster, PrimateAI and REVEL vs. 1 benign prediction from SIFT). 13 https://gnomad. broadinstitute. org/ The newly described EXT1 c. 1065C > G (p. Cys355Trp) was predicted by VarSome to be "likely pathogenic" (verdict based on 10 pathogenic predictions from BayesDel_addAF, DEOGEN2, FATHMM-MKL, M-CAP, MVP, MutationAssessor, MutationTaster, PrimateAI, REVEL and SIFT vs.
one of the largest carbon fluxes in the Earth system. An increasing number of highfrequency R S measurements (typically, from an automated system with hourly sampling) have been made over the last two decades; an increasing number of methane measurements are being made with such systems as well. Such high frequency data are an invaluable resource for understanding GHG fluxes, but lack a central database or repository. Here we describe the lightweight, open-source COSORE (COntinuous SOil REspiration) database and software, that focuses on automated, continuous and long-term GHG flux datasets, and is intended to serve as a community resource for earth sciences, climate change syntheses and model evaluation. Contributed datasets are mapped to a single, consistent standard, with metadata on contributors, geographic location, measurement conditions and ancillary data.
, 2001; Subke et al. , 2006) , validation of eddy covariance measurements in complex ecosystems  (Miao et al. , 2017) , responses of soil GHG emissions to extreme climate events  (Petrakis et al. , 2017)  and rising atmospheric carbon dioxide concentrations  (Drake et al. , 2018)  and novel inversion techniques  (Latimer & Risk, 2016). The resulting GHG flux datasets, however, remain widely dispersed and frequently unavailable. There is no centralized database for chamber fluxes akin to FLUXNET  (Baldocchi et al. , 2001 ), although annual (Bond-Lamberty & Thomson, 2010)  and some daily to seasonal  (Jian, Steele, Day, et al. , 2018; Jian, Steele, Thomas, et al. , 2018 ) R S flux databases do exist. This is troubling, both because of the lost or unavailable research opportunities for synthetic work with respect to temporally high-resolution GHG fluxes, but also because of the inevitable loss of data  (Wolkovich et al.
| ME THODS COSORE is designed to be a relatively lightweight database: as simple as possible, but not simpler. It is targeted at continuous-i. e. measured by automated systems-soil respiration flux data, but the database design accommodates manual point (survey-style) R S fluxes, methane fluxes and chamber measurements of net ecosystem exchange as well, paralleling the recent Soil Incubation Database database  (Schädel et al. , 2020). Its development started in April 2019, and as of this writing (2020-09-04) the COSORE version number is 0. 8. 0.
| Database and dataset structure The database is structured as a collection of independent contributed datasets (Table  1 ), all of which have been standardized to a common structure and units. Each dataset is given a reference name (internal to COSORE) that links its constituent tables, and provides a point of reference in reports. Each constituent dataset normally has a series of separate data tables: • description (Table  2 ) describes site and dataset characteristics; • contributors (Table  3 ) lists individuals who contributed to the measurement, analysis, curation and/or submission of the dataset; • ports ( • data (Table  5 ), the central table of the dataset, records flux observations; • ancillary (Table  S1 ) summarizes site-level ancillary measurements; • columns (Table  S2 ) maps raw data columns to standard COSORE columns, providing a record for reproducibility; and • diagnostics (Table  S3 ) provides automatically generated statistics on the data import process: errors, columns and rows dropped, etc.
| Versioning and archiving COSORE uses semantic versioning (https://semver. org/), meaning that its version numbers generally follow an 'x. y. z' format, where x is the major version number (changing only when there are major changes to the database or package structure and/or function, in a manner that may break existing scripts using the data); y is the minor version number (typically changing with significant data updates); and z the patch number (bug fixes, documentation upgrades or other.
| Data license and citation The database license is CC-BY-4 (https://creat iveco mmons. org/ licen ses/by/4. 0/); see the 'LICENSE' file in the repository. This is identical to that used by e. g. FLUXNET Tier 1 and ICOS RI. In general, this license provides that users may copy and redistribute the database and R package code in any medium or format, adapting and building upon them for any scientific or commercial purpose, as long as appropriate credit is given. We request that users cite this article and strongly encourage them to (a) cite all constituent dataset primary publications, and (b) involve data contributors as co-authors whenever possible, as is commonly done for other global databases such as FLUXNET  (Baldocchi et al. , 2001; Knox et al. , 2019). In addition, users should also reference the specific version of the dataset they used (e.
| DATA ACCE SS AND US E Major Four primary user-facing functions (cf. Figure  1 ) are available: • csr_database() summarizes the entire database in a single convenient data frame, with one row per dataset, and is intended as a F I G U R E 1 Summary of COSORE structure (multiple datasets, each with six tables; Tables  2 3 4 5 6 7 ) and primary accessor R functions, as described in the text (see Section 2. 1 in text). For example, R users can join specific tables across all datasets using the csr_table() function, and can access individual datasets with csr_dataset(). Non-R users access flat-file versions of the same data, with essentially the same structure as the R internal structure shown here high-level overview. It returns a selection of variables summarized in Tables  2-5  and Tables S1-S3 , including dataset name, longitude, latitude, elevation, IGBP code, number of records, dates and variables measured; • csr_dataset() returns a single dataset: an R list structure, each element of which is a table (description, contributors, etc.
, as described above); • csr_table() collects, into a single data frame, one of the tables of the database, for any or all datasets; • csr_metadata() provides metadata information about all fields in all tables. Two additional reporting functions may also be useful to users: • csr_report_database() generates an HTML report on the entire database: number of datasets, locations, number of observations, distribution of flux values, etc. ; • csr_report_dataset() generates an HTML report on a single dataset, including tabular and graphical summaries of location, flux data and diagnostics. Finally, a number of functions are targeted at developers, and include functionality to ingest contributed data, standardize data and prepare a new release. See the package documentation for more details.
| Data quality and testing When contributed data are imported into COSORE, the package code performs a number of quality assurance checks. These include: • Timestamp errors, for example illegal dates and times for the specified time zone; • Bad email addresses or ORCID identifiers; • Records with no flux value; • Records for which the analyzer recorded an error condition. Any errors flagged or records removed during this process are summarized in the diagnostics table that is part of each dataset (Table  S3  below). Across all contributed datasets, a median of 7. 9% of raw observations were removed for one of these reasons. Note however that no checking on the flux values themselves is performed (e. g. for outliers, improbable values); currently this is the responsibility of the user. The cosore R package also has a wide variety of unit tests  (Zhao, 2003)  that test code functionality via assertions about function behaviour and by verifying behaviour of those functions when importing test datasets (of different formats and with a variety of errors, for example).
| CURRENT DATA AND COMMUNIT Y CONTRIBUTI ON S The database currently has 89 contributed datasets with a total of 8. 14 million flux observations across 20 years and five continents (Table  1 ; Figure  2 ), widely distributed in climate and biome space, from Arctic to tropical ecosystems (Figure  3 ). In terms of data volume, the current database is dominated by CO 2 fluxes in evergreen and deciduous forests (Table  1 ; Figure  4 ) from the mid-northern latitudes (Figures  2  and 5 ). These data are unequally distributed around the year, with many more data available during the Northern Hemisphere growing season (Figure  6 ). There is an order of magnitude more data in COSORE from the Northern than Southern Hemisphere, and currently no CH 4 data at all from the Southern Hemisphere. The interval between measurements ranges from 3 to 1,440 min, with 25%-50%-75% quantile values of 30, 60 and 60 min respectively.
A one hour interval between measurements is thus by F I G U R E 8 Distribution of CO 2 fluxes in COSORE datasets, by IGBP classification (cf. Table  1 ). For visual clarity this figure excludes fluxes <-1 and >10 µmol m -2 s -1 (210,752 observations, 2. 6% of the data). Number of datasets (sites) making up data is given in parentheses after IGBP abbreviations in each panel SAV  (1)  WET (  4 ) WSA (  4 ) ENP (  1 ) GRA (  7 ) MFO (3) OSH (  5 ) DNF (  2 ) DWO (  1 ) EBF (  11 ) ENF (  18 ) CRO (  3 ) CSH (  1 ) DBF (  21 ) DBP (  1  far the most common choice (Figure  7 ). Currently 92% of the datasets, and 99. 999% of the data, provide sub-daily temporal resolution. Such resolution allows novel analyses of the 'hot moments' of CO 2 and other GHG fluxes (e. g. Diefenderfer et al. , 2018). Dataset CO 2 fluxes (mostly soil respiration, but as noted above also some heterotrophic respiration and net ecosystem exchange) are generally log-normally distributed in most IGBP classifications (Figure  8 ).
The distribution of CH 4 is more complex, with most data clustered around 0 nmol m -2 s -1 but featuring long distribution tails to many orders of magnitude larger fluxes for both net uptake and release (Figure  9 ), due to the complexity and variety of biochemical processes involved in methane production and oxidation  (Riley et al. , 2011). The COSORE team welcomes data contributions of soil-atmosphere GHG flux data. We prioritize continuously measured (i. e. from automated systems including non-chamber approaches) soil respiration datasets, but the database structure also accommodates (discontinuous, i. e. manual) data, as well as measurements of methane, net ecosystem exchange and heterotrophic respiration fluxes. Contributors receive a QA/QC report for all submissions, including details on invalid data, removed data, etc.
, 2017; Mitra et al. , 2019; Subke & Bahn, 2010). Finally, open data and open-source harmonization tools (with which to compile disparate datasets) support scientific reproducibility, serve as an educational resource  (Mouromtsev & d'Aquin, 2016)  and reduce loss of data over time  (Powers & Hampton, 2018). A crucial attribute of COSORE is its relationship to preexisting databases and efforts. The older Global Soil Respiration Database (SRDB, Bond-Lamberty & Thomson, 2010) focuses on seasonal to annual fluxes, with monthly-and daily-resolution offshoots of the SRDB  (Jian, Steele, Day, et al. , 2018; Jian, Steele, Thomas, et al. , 2018)  following similar designs. Others, such as ForC  (Anderson-Teixeira et al. , 2018) , take a broader scope and also focus on annual fluxes. We hope that the large volume of standardized, highfrequency GHG flux data in COSORE will enable novel global scale syntheses, modelling activities, new insights driven by machine learning  (Albert et al.
, 2017; Vargas et al. , 2018)  and conceptual advances (e. g. 2 ). There are no datasets from Africa (cf. Epule, 2015)  and little South American data. The IGBP representation is skewed as well (Figure  4 ), although the database's climate space coverage is reasonable (Figures  3  and 6 ). This spatial patchiness-a function of many factors including economic development, infrastructure, scientific investment-imposes significant restrictions on our ability to draw global inferences and analyses from extant observational data. F I G U R A second category of limitations arises from COSORE's particular design. The database is oriented towards lightweight and minimal requirements, aiming for breadth over depth. This has benefits and costs. Having low barriers to entry shifts the burden of contributing data away from data providers, and keeping the design lightweight (with limited controlled vocabularies, ancillary data, etc.