dataesr/scientific-paragraphs-categorization huggingface dataset:
Metabolomic analyses Targeted analyses of non-structural carbohydrates, polyols, organic acids, and amino acids were performed with procedures previously reported by  Jubault et al. (2008b) ,  Gravot et al. (2010), and Colinet et al. (2012). Non-targeted LC-MS analyses were performed as follows. Metabolites were extracted from 50 mg of dried plant material. A 1 ml volume of a methanol/water mixture (50/50, v/v) was added to the sample, acidified with 5% (v/v) trifluoroacetic acid, then filtered through a 0. 45 µm filter. Samples were injected into a HPLC-LCQ Deca ion trap mass spectrometer (Thermo Fisher). Chromatogram files were analysed using the Bioconductor package XCMS  (Smith et al. , 2006). Peaks were detected using the centWave algorithm  (Tautenhahn et al. , 2008) , using the following parameters: 700 ppm, peak width from 20 to 60 s, s/n threshold 5:1.
Retention time correction was performed in three successive iterations of the functions 'group' (mzwd=0. 25 and minfrac=0. 2) and 'retcor' (decreasing the bw parameter from 80 to 40 and then 20). A total of 289 different mass signals (see also Supplementary Fig. S1  at JXB online) were obtained in all biological samples (246 HD lines and 59 parental lines). This raw dataset was then filtered as follows. First, the Bioconductor package CAMERA implemented in R  (Kuhl et al. , 2012)  was used to identify 76 pc-groups, that is, sets of co-eluted mass signals with similar peak shapes. The mean number of mass tags in each pc-group was ~3. 8 (range 1-33). In every single pc-group, the low-signal ion species that were highly correlated (Pearson's correlation coefficient >0. 75 among 301 samples) with another ion species of higher signal in the same pc-group were ignored in further analyses.
Data analysis Mean-centred data from targeted and non-targeted analyses were pooled together in a single dataset, before subsequent statistical analyses. Principal component analysis (PCA) was performed on the data from the parental lines using the R package FactoMineR  (Lê et al. , 2008). Statistical analyses of phenotypic, molecular, and metabolic data from the DH lines were carried out using R software (R Core Team, 2016) and the package lme4  (Bates et al. , 2015). A mixed linear model was defined as follows: P ijk = µ + L i + R j + e ijk , (1) where P ijk is the mean value of the ith DH line sampled in the jth biological repetition, µ is the mean of all the data, L i is the DH effect, R j is the repetition effect, and e ijk is the residual. All effects were declared as random. Heritability (h 2 ) and standard deviation were estimated from model (1), using the formula h 2 = σ g 2 / σ g 2 + σ e 2 ( 2 ) where σ g 2 is the genetic variance and σ e 2 is the environmental variance.
Cytoscape-based representation of QTL/trait networks The full list of QTLs/phenotypic traits was treated as a directed network. In this network, nodes represent loci or traits, and edges represent statistically significant links between loci and phenotypic traits (from the QTL analysis described above). Edge widths were used to represent R 2 , that is, the percentage of variance explained by each locus for a given phenotypic trait. Edge colour was used to represent positive or negative allele effects on phenotypic traits. This network was represented graphically using Cytoscape software  (Shannon et al. , 2003).
Partial resistance and metabolomic responses to clubroot infection in the Darmor-bzh and Yudal parental lines of B. napus A first series of experiments was conducted to monitor pathogen development in the B. napus parental genotypes Darmor-bzh and Yudal. Primary plasmodia were observed in root hairs of both genotypes at 10 dpi (Fig. 1A ). At 14 dpi, small secondary plasmodia were detected in the cortical cells of both Darmor-bzh and Yudal (data not shown). At 35 dpi, infected roots of Yudal displayed extensive hyperplasia and hypertrophied plant cells containing well-developed secondary plasmodia of P. brassicae (Fig. 1A ). At the same sampling point in Darmor-bzh, secondary plasmodia were also observed, but they were much less developed than in Yudal, and the infected cells were of similar size or even smaller than uninfected cells in the non-inoculated plants.
S1  and Table  S1 ). This resulted in a full dataset of 231 analytes, including 55 identified primary and secondary metabolites (Supplementary Fig. S1  and Table  S1 ). The predominant primary metabolites in non-inoculated roots (Fig. 2 ) were malic acid, citric acid, and the soluble sugars sucrose and glucose (Fig. 2A, B ), in accordance with previous studies  (Wagner et al. , 2012). The major amino acids were S-methylcysteine sulfoxide (SMCSO) (a typical non-proteinogenous sulfur amino acid from Brassica species), glutamine, and glutamate (Fig. 2C ). The main glucosinolate-associated mass signals were related to indole glucosinolates with the most abundant compounds being 1-methoxy-3-indolyl glucosinolate Yudal Darmor-bzh Fig. 3. Constitutive and clubroot-induced root metabolomic differences between inoculated (control) and non-inoculated (with P.
brassicae) is usually observed during the secondary phase of clubroot infection, finally reaching 10-50 µmol g DW -1 in fully developed clubs (5-6 weeks post-inoculation in rapeseed)  (Keen and Williams, 1969; Broman et al. , 2003; Gravot et al. , 2011; Wagner et al. , 2012). In the present study, the slight increase in trehalose content in Yudal at 21 dpi is indicative of an already active secondary-phase development of P. brassicae. Concatenation of datasets from targeted and untargeted metabolic profiling, resulting in a full dataset of 231 analytes (Supplementary Table  S1 ), broadened the view on possible metabolic contrasts between infected and non-infected roots of the two genotypes. PCA of this dataset (Supplementary Fig. S2 ) indicated that 'genotype' was the major factor influencing metabolic contrasts between samples (axis 1), followed by 'inoculation', which led to a clear metabolic shift in roots of Yudal (axis 2) and, to a lesser extent, in Darmor-bzh (axis 3).
A further analysis of this extended metabolite dataset allowed us to identify four categories of analytes showing a contrast between the two genotypes in inoculated roots (Fig. 3 ): metabolites that are constitutively more accumulated in one of the two genotypes (cluster 1, Darmor-bzh; cluster 2, Yudal), and metabolites whose accumulation is triggered by P. brassicae inoculation in only one of the two genotypes (cluster 3, Darmor-bzh; cluster 4, Yudal). Cluster 4 included a large number of compounds that accumulated specifically in inoculated roots of the more susceptible accession, Yudal. This large set of metabolites included trehalose and S-methylcysteine, two analytes that we have previously reported as clubroot susceptibility biomarkers  (Wagner et al. , 2012) ; several additional compounds, such as a salicylic glucoside; and 27 unknown analytes, including M655T655 (with a mass signal ~10-fold higher in inoculated roots of Yudal compared with Darmor-bzh).
3 , cluster 1). The genetic control network (Fig. 8B ) of M320T2161 and M368T3033, which were associated with remarkably high R 2 values (36. 5% and 23. 7%, respectively), indicated that the constitutively high levels of those two compounds in Darmor-bzh was related to the presence of the Darmor-bzh resistance allele at the metaQTL C09_58. 88. This 'coincidence' consistently suggested a possible role for these two unidentified molecules as candidate key players in the partial resistance driven by C09_58. 88. Additional high-resolution MS/MS approaches allowed more precise m/z measurements of parental ions, and further investigation of their corresponding ion fragments (Supplementary Fig. S7 ). These fingerprints did not, however, match any referenced molecule available in public databases.
We aimed to develop an efficient, flexible and scalable approach to diagnostic genome-wide sequence analysis of genetically heterogeneous clinical presentations. Here we present G2P (www. ebi. ac. uk/gene2phenotype) as an online system to establish, curate and distribute datasets for diagnostic variant filtering via association of allelic requirement and mutational consequence at a defined locus with phenotypic terms, confidence level and evidence links. An extension to Ensembl Variant Effect Predictor (VEP), VEP-G2P was used to filter both disease-associated and control whole exome sequence (WES) with Developmental Disorders G2P (G2P DD ; 2044 entries). VEP-G2P DD shows a sensitivity/precision of 97. 3%/33% for de novo and 81. 6%/22. 7% for inherited pathogenic genotypes respectively. Many of the missing genotypes are likely false-positive pathogenic assignments.
The effectiveness of the former has been revolutionized by the availability of data from the Exome Aggregation Consortium (ExAC)  8  and the Genome Aggregation Database (gnomAD; http://gnomad. broadinstitute. org). These resources provide access to technically robust variant calls from diverse populations of known providence. There are many different publicly available tools for defining the consequence of an individual variant call  9. One of the most widely used is the Ensembl Variant Effect Predictor (VEP)  10. VEP predicts the effect of each alternative allele on each overlapping transcript for a variant and assigns Sequence Ontology  11  terms to describe the consequences. It can be run either online or using a locally installed version of the program. VEP exploits the extensive and regularly updated Ensembl datasets to provide the most comprehensive variant annotation possible in coding and non-coding regions.
It also supports extensibility through the 'plugin' system, which allows custom methods to be easily added. Automated variant annotation and filtering of WES data using Ensembl VEP has been successfully applied in a genetically heterogeneous disease cohort by the Deciphering Developmental Disorders Study (DDD)  12, 13. The DDD Study has recruited >13,400 individuals, with previously undiagnosed severe and/or extreme developmental disorders (DD), from the UK and the Republic of Ireland. The principal aim of the project is to define the genetic architecture of DD using triobased WES analyses as the main analytical tool  14. Important secondary aims were to identify novel DD loci and develop diagnostic approaches that could be translated into clinical practice. To facilitate this, we developed a database of all known causative DD loci (DDG2P), which captures the information essential to allow facile, very high throughput filtering of variant calls.
This dataset has been used in each of the DDD flagship papers  12, 13. The continual updating of DDG2P has been one of the main drivers of the improvement in diagnostic rates through iterative reporting of the same data  15. The basic architecture and processes used to populate DDG2P  16  have been adapted to be applicable to any clinical presentation that has a reasonable prior probability of being caused by highlypenetrant genotypes at a defined group of loci. To expand from DD to other clinical presentations and to create a system that could be maintained and updated by multiple curators, we created the genotype-to-phenotype (G2P) web application to hosts the DDG2P database and any similar datasets. Here we describe G2P, tailored to address the problem of robust, efficient and flexible prioritization of genotypes identified from NGS data to aid the diagnosis of genetic disease.
As part of our G2P system, we have developed a suite of tools and resources: (1) The G2P portal/web application, which is freely available at https:// www. ebi. ac. uk/gene2phenotype/ for creation, curation and dissemination of G2P datasets; (2) G2P datasets, which formalize collections of locus-genotype-mechanism-disease-evidence threads (LGMDET), curated from the literature, and found to be implicated in the cause of a specific disease or clinical presentation; (3) The G2P extension to Ensembl VEP, which is freely available at https:// www. ebi. ac. uk/gene2phenotype/g2p_vep_plugin (VEP-G2P). VEP-G2P utilizes the allelic requirement information from G2P datasets/ panels and leverages allele frequency data from public datasets such as Genome Aggregation Database (gnomAD) together with the predicted mutational consequence annotations from VEP to produce list of potentially causative genotype(s) given an individual's VCF file as an input.
Sensitivity and precision for DDD causative variants. Using data from the first 4293 trio WES in the DDD study the overrepresentation of plausibly deleterious de novo variants in 94 different genes achieved genome-wide significance  12. There was a total of 804 likely causative de novo variants in these 94 genes that were reported to referring clinicians. Proband-only analysis  LGMDET thread. Each locus-genotypemechanism-disease-evidence thread (LGMDET) associates an allelic requirement and a mutational consequence at a defined locus with a disease entity and a confidence level and evidence links. The publicly available G2P DD and G2P Cancer data can be searched or downloaded on the website (https://www. ebi. ac. uk/gene2phenotype). b gives examples for LGMDE threads from curated datasets.
In addition to the publicly available G2P DD and G2P Cancer data, G2P Eye is actively curated and will be publicly available soon. Access to the curation system can be requested for the creation of user-defined datasets. c summarizes the workflow for diagnostic filtering. The VCF files derived from the next-generation sequence data are passed to VEP which uses Ensembl annotation data to compute and annotate the consequence of each variant. The VEP-G2P plugin runs as an additional step of the VEP analysis. It uses the results of VEP's computations and annotations together with the knowledge from the LGMDETs to filter the variants from the patients input VCF file. The plugin results, plausible genotypes of likely deleterious variants, are returned together with the VEP output file for clinical review.
The input for each of these tests were WES-derived data from three non-overlapping sets of 100 individuals who were randomly chosen as outlined above. In addition AMELIE and DIVINE use a list of HPO terms for each individual. DIVINE, VVP and GAVIN were installed locally; AMELIE analysis used the program website (see online resources). All of the systems achieved reasonable sensitivity with DIVINE ranking top (Fig. 4a ). The precision measures were, in contrast, much more variable with VEP-G2P and AMELIE scoring much better than the other systems. The high precision seen for VEP-G2P is likely to be explained by the explicit statement of the allelic requirement per each gene (monoallelic vs. biallelic) and restricting analysis to ~2000 LGMET in G2P DD. Although the median ranks of the causative gene for the two tools are very similar, AMELIE exhibits longer tail resulting in mean rank for Set A of 5.
Discussion DDG2P was developed to identify reportable, plausibly causative genotypes in known developmental disorders in DDD Study probands  13. Our primary aim was to evaluate LGMDETs-the key architectural feature of DDG2P data-as a scalable and generalizable approach for diagnostic analysis of clinical presentations in which affected individuals may have one of many different Mendelian disorders using only human genetic data. First, we developed the gene2phenotype curation system (https://www. ebi. ac. uk/ gene2phenotype/) to facilitate the creation and review of LGMDETs in different datasets. To maintain consistency and clarity of purpose in G2P datasets, to date, we have used only two highly-motivated expert clinician curators to develop and maintain each G2P dataset. This approach requires a significant investment of time and effort and is difficult to scale.
However, data mining tools (Pubtator, ClinVar etc) are now being incorporated into the online system to minimize the human resource requirements. Additional curation tools will become increasingly important as the diversity of journals reporting novel gene-disease associations continues to widen. Here we present G2P DD and G2P Cancer as first two publicly accessible LGMDET sets. Our primary aim is also dependent on the ability to implement these LGMDET sets in clinical research diagnostic filtering. For this we chose to develop the VEP-G2P plugin as both G2P and VEP are hosted at EMBL-EBI and VEP is widely used in research and clinical practice. The VEP-G2P plugin identifies genotypes that fulfil the MAF filters and LGMDET requirements (that is, genotypes with the required mutational consequence and allelic requirement at a locus) with the aim of only reporting likely causative genotypes.
Taking the genotypes observed in the proband into account when filtering-rather than reporting the full list of all plausibly pathogenic variants-leaves only a small number of loci (mean < 4), minimizing the time required for review of each case by clinicians and clinical scientists. For specific loci reporting genotypes also masks incidental findings, e. g. only homozygous or two different heterozygous (possible compound heterozygous) likely pathogenic variants in BRCA2 will be reported in DDG2P as a cause of Fanconi anemia. The speed and ease of VEP-G2P plugin use has allowed us to assess the expected background output from each G2P dataset against a population ascertained dataset. This required access to WES data from individuals that have not been selected for any specific disease or clinical problem and we have used these individuals as our control group.
Our analysis suggests that at least half of the variants surviving the filtering process are likely to be the result of background population genome variation rather than specifically relevant to this disease being analyzed. Here we used Generation Scotland, which is relatively small in size but in the near future, much larger, unselected WES and WGS control datasets will be available from UK Biobank  19  and these will enable more accurate definition of the characteristics of the variants that constitute the background noise. We consider this type of analysis to be a very important sanity check in genetic diagnostic analysis. We do not wish to imply that the GS variants represent pathogenic alleles as they include all rare missense variants, regardless of their SIFT/PolyPhen predicted effect.
Such variants will be more commonly encountered when analysing individuals from populations that are under-represented in the gnomAD database. We have made it very simple for any panel of any size to be converted to be compatible with VEP-G2P plugin. For example PanelApp (https://panelapp. genomicsengland. co. uk) is a gene panel development system created by Genomes England for the 100,000 Genomes initiative (https://www. genomicsengland. co. uk). PanelApp currently hosts 231 gene panels focused on specific clinical diseases (e. g. Charcot Marie Tooth syndrome) or on groups of phenotypically-related diseases (e. g. Hereditary Ataxias). These gene panels were mostly initiated using panels in current clinical use with subsequent crowdsourced curation. We have extended VEP-G2P to optionally support the PanelApp export format.
Sensitivity = TP/(TP + FN). Precision = TP / (TP + FP). a Evaluation of G2P accuracy for likely causative variants in 94 genes achieving genome-wide significance (GWS) for de novo mutations in the DDD study. b G2P accuracy against the set of variants previously identified by DDD in the first 1133 samples, excluding de novo mutations. c ROC curves for VEP-G2P DD performance on 1700 DDD probands with de novo mutations (DNM) identified in the 484 monoallelic genes. The points on the curves represent varying MAF cut-offs: not seen in any control databases (bottom left), MAF < 1:100000, MAF < 1:50000, MAF < 1:25000, MAF < 1:10000 (top right). The region in the top left corner of the ROC space graph has been expanded to scale using the regions bounded by the dashed line rectangles. d The effect of consequence type and MAF on precision and recall (PR curves) of VEP-G2P DD using the same data analysed for the ROC space in c.
Methods G2P dataset structure and availability. The structure of G2P datasets is based on that of the DDG2P diagnostic tool, which has been previously described  16  (Fig. 1a ; Supplementary methods). Each dataset is focused on a disease grouping or defined category of clinical presentation that is of relevance to the clinical diagnosis of Mendelian disease. Each entry in each dataset links a gene or locus, via a disease mechanism, to a disease. A disease mechanism is defined as both an allelic requirement (mode of inheritance, for example biallelic or monoallelic) and a mutation consequence (mode of pathogenicity, for example activating or loss-of function). A confidence attribute-confirmed, probable or possible-is also assigned to indicate how likely it is that the gene is implicated in the cause of disease.
These data are all accessible via the G2P web application, which is searchable by gene symbol, disease name or disease ontology term. The full datasets are also available for download as CSV files (https://www. ebi. ac. uk/gene2phenotype/ downloads). For a dataset to be publicly released it must be comprehensive, up-todate and have a plan for future active curation. To improve consistency in dataset curation and provide clarity to potential users, the rules used to assign confidence, allelic requirement and mutation consequence to entries are defined and available via the web application in the form of tables (Supplementary Tables  1 2 3 ). Locus-genotype-mechanism-disease-evidence threads (LGMDET). Two G2P datasets: G2P DD and G2P Cancer (Supplementary Data File 1 and 2) are currently available.
G2P DD includes LGMDETs associated with clinically significant developmental disorders, i. e. severe and/or extreme disorders that plausibly have their genesis during embryogenesis or early fetal brain developments. This dataset was populated by a combination of clinical knowledge, systematic literature review and genes from existing in-house gene panels by two consultant clinical geneticists (DRF and HVF). We chose to exclude two major groups of developmental disease -isolated hearing loss and isolated dental anomalies-which are planned to have their own G2P panels and (excepting composite phenotypes) are unlikely to present as undiagnosed developmental disorders. G2P Cancer aims to identify Mendelian cancer susceptibility in individuals affected with cancer or with a strong family history.
The characteristics of the 2044 G2P DD and 123 G2P Cancer reportable (i. e. confirmed, probable, RD&IF) LGMDET entries are summarized in Table  1. Variant calling and quality filtering. To evaluate the performance of the VEP-G2P plugin we analyzed three independent sets of WES variant data, each of which had undergone extensive prior analysis. It should be noted that due to differences in the upstream variant calling pipelines (Table  2 ; data also processed at different times at different centres), there is a slight excess in the number of filtered and unfiltered variants per sample in the DDD cohort compared to the colorectal cancer (CRC) and Generation Scotland (GS)  21, 22  cohorts (Supplementary Table  4 ). Although it would be possible to realign and recall these datasets to ensure consistency, we chose to proceed without trying to resolve these differences as this is representative of realistic data available to most research groups involved in clinical diagnostic research.