dataesr/scientific-paragraphs-categorization huggingface dataset:
WGS TruSeq Nano libraries were prepared and loaded onto a HiSeq X Ten sequencer (Illumina; control Software v3. 0. 29. 0) and 2 × 150 bp paired-end sequencing was performed at the Kinghorn Centre for Clinical Genomics (KCCG). More than 75% of bases having Q30 base quality and mean coverage of >30×. Raw sequencing data were converted to FastQ format using Illumina's bcl2fastq converter (v2. 15. 0. 4) and read quality was evaluated using FastQC. Sequences were aligned to the b37d5 human reference genome using Burrows-Wheeler Aligner (BWA, v0. 7. 12-r1039), coordinate-sorted using Novosort (v1. 03. 04, Novocraft Technologies Sdn Bhd, Selangor, Malaysia), and improved using GATK (v3. 4-46-gbc02625)  43  indel realignment and base recalibration to generate BAM files. Variants were called using GATK (v3.
4-46-gbc02625)  43  HaplotypeCaller followed by joint variant calling with GenotypeGVCFs and Varian-tRecalibration. The resulting multi-sample VCF file was annotated using ENSEMBL's Variant Effect Predictor (v74)  44  and converted to an SQLite database using gemini (v0. 17. 2)  45. Gemini databases were imported into Seave  46  which was used to perform variant filtration and prioritization. For P5 genetic studies were done by trio WES with enrichment with SureSelect Clinical Research Exome (Agilent, Technologies, Santa Clara, CA, USA) and sequencing by the NextSeq 500 sequencing system (Illumina, San Diego, CA, USA), according to previously published methods  47  and a customized method for calling, annotation, filtering and prioritization of the variants  [47] [48] [49] [50]. Libraries were prepared using the Kapa HTP kit (Illumina, San Diego, CA, USA) and capture was Article https://doi.
org/10. 1038/s41467-023-36277-7 Nature Communications | (2023) 14:1009 performed using the SeqCap EZ MedExome (Roche NimbleGen Madison, WI, USA). Sequencing was done on an Illumina HiSeq2500 HTv4 (Illumina, San Diego, CA, USA) with paired-end 125-bp reads. Read alignment to hg19 and variant calling were done with a pipeline based on BWA-MEM0. 7 and GATK 3. 3. 0. The median coverage of the captured target region was at least 100x. Variant annotation and prioritizing were done using Cartagenia Bench Lab NGS (Agilent Technologies). We excluded variants located outside the exons and intron/exon boundaries and variants with a minor allele frequency (MAF) of >1% in control databases, including dbSNP137 (http://www. ncbi. nlm. nih. gov/projects/SNP), 1000 Genomes Project (release of February 2012), and Exome Variant Server (EVS), NHLBI Exome Sequencing ProjectNational Heart, Lung, and Blood Institute GO Exome Sequencing Project (ESP5400 release) (http://evs.
In silico modelling The structure of TEFM (Protein Data Bank: 5OLA) was used to model the position and function of residues at which substitutions were observed in this study. Molecular structures were displayed using PyMOL. The overall structure is shown using cartoon representation with individual residues displayed as spheres or sticks and polar contacts as dashed lines. DNA isolation and mtDNA copy number DNA was isolated from cultured fibroblasts using a DNeasy Blood and Tissue Kit (Qiagen) and quantified using a nanodrop 2000 spectrophotometer (ThermoFisher). Muscle samples were homogenized with a TissueRuptor II (Qiagen) prior to isolation and quantification via the same method. All samples were diluted to 2 ng/µL with nuclease free water. MtDNA copy number was quantified using a duplex TaqMan qPCR assay amplifying MT-ND1 (mitochondrial target) and B2M (nuclear target) with a CFX96™ Real-Time PCR Detection System (Bio-Rad).
7. 3a, release 08/10/2019,)  63  , in two-pass mode for read alignment to the Human Reference Genome Build 38 (excluding "ALT" contigs). Duplicate reads were marked with Picard MarkDuplicates and quantification was performed using FeatureCounts from the R Subread package (version 1. 34. 7, release 03/01/2019)  64. Differential expression analysis was performed using the DESeq2 package (version 1. 25. 9, release 31/07/2019) comparing expression of the affected individual to 23 unrelated control samples  56. Sashimi plots were prepared using ggsashimi  65.
CSBII analysis Standard alignment to the reference genome could potentially create a bias in favour of shorter repeat sequences. Therefore, the available whole genome or whole exome sequencing datasets were re-analyzed. Starting from the Fastq data, quality trimming was performed with TrimGalore!. Alignment to hg38 with adaptations (extra Ns in the repeat) was performed with Bowtie2 (-very-sensitive-local-np 0-nomixed) to avoid a penalty score for (not) mapping to N.
Primary antibodies used are rabbit anti-TEFM (HPA023788), total Abcam OXPHOS human WB antibody cocktail (ab110411), anti-ND6 (V-16)-R (sc-20510-R), anti-TOMM20 (ab186735), anti-VDAC1 (ab14734), anti-NDUFA9 (ab14713), anti SDHA (ab14715), anti-UQCRC1 (ab110252), anti-MTCO1 (ab14705), anti-ATP5A (ab14748), anti-MRPLS18b (16139-1-AP), anti-MRPL12 (14795-1-AP), anti-MRPL3 (HPA043665), anti-MRPS17 (18881-1-AP), anti-MRPS39 (AP1967b), anti-MRPL45 (ab251748), anti-PDE12 (HPA043171), anti-GRSF1 (NBP1-89488) and anti-vinculin (42H89L44). All antibodies were validated by the manufacturer. The oxidative phosphorylation (OXPHOS) complex I and IV activity in fibroblasts was measured using dipstick activity assays (Abcam), using 30 µg of whole-cell lysates following the manufacturers protocol as described  66.
Preparation, acquisition and analysis of mass spectrometry data A total of 20 µg of proteins from fibroblasts (TEFM patient as technical duplicate and single replicates from four control individuals) were quantified with Pierce Protein Assay Kit (Thermo Fisher Scientific) in lysis buffer prior to reduction, alkylation using the PreOmics iST-NHS kit reagents. Protein digestion was performed with trypsin at 1:50 trypsin to protein ratio and according to manufacturer's instructions. Clean peptides from individual samples were individually labelled with TMT10plex TMTs (Thermo Fisher Scientific) at a ratio of 8 µg TMT label to 1 µg protein prior to mixing at a 1:1 ratio. Labelled peptides were cleaned up and eluted per manufacturer's instructions following drying down in a CentriVap Vacuum concentrator (Labconco).
The "top speed" acquisition method mode (3-s cycle time) on the most intense precursor was used, whereby peptide ions with charge states ≥2-7 were isolated with an isolation window of 0. 7 m/z and fragmented with high energy collision mode with stepped collision energy of 35 ± 5%. Fragment ion spectra were acquired in Orbitrap at 50,000 resolution. Dynamic exclusion was activated for 45 s. Raw files were processed using the MaxQuant platform (version 1. 6. 10. 43)  67 and searched against the reviewed UniProt human database (March 2021) containing canonical and isoform sequences using default settings for a TMT 10plex experiment with the following modifications: deamination (of asparagine and glutamine) was added as variable modification, and a mass shift of +113. 084 on cysteine was included as a fixed modification as PreOmics kit recommendation.
Correction factors for the TMT kit product: A37725 and lot number: UB278774 were entered into the search parameters. The protein-Groups. txt output from the search was analyzed in Perseus (version 1. 6. 14. 0)  68. TMT reporter intensity corrected values for TEFM replicates (126C, 127N) and controls (129C, 130N, 130C, 131N) were log 2 -transformed and entries annotated by MaxQuant as "potential contaminant," "reverse," and "only identified by site" were removed from the dataset (Supplementary Dataset). Categorical annotation of the TMT intensities into groups (TEFM, controls) was performed and valid values were filtered to have a minimum 2 in each group. Two sample ttest was performed between TEFM group against controls group using t-test (p-value 0. 05) for truncation. Scatter plot function was used to plot volcano plot with fold change of 1.
5 (log 2 = 0. 585) and p-value 0. 05. The relative complex abundance of the OXPHOS complexes and mitoribosome subunits were plotted with an in-house R script which calculated the difference between the TEFM and control patients for each subunit identified by more than one peptide as indicated by gene name. The mean and standard deviation were then calculated, along with the 95% confidence interval based on the t-statistic for each complex (calculated from the difference between the control and patient samples), along with a paired t-test which calculated significance between the control and patient for each complex. TEFM protein was detected via a single peptide and the MS/MS spectrum was plotted using the MaxQuant platform (version 1. 6. 10. 43).
Zebrafish experiments Zebrafish morpholino injections. The UCSC database  69  (http:// genome. ucsc. edu/) revealed one orthologue of TEFM in zebrafish; tefm (GRCz11/danRer11 assembly). After confirming expression of tefm throughout the first 5 days post fertilization (dpf), an antisense morpholino oligonucleotide (MO) targeting the splice acceptor site of intron 1/exon 2 tefm (5′-3′ ATGTCCTGCAATCAATCAAACATTT, NM_001080026. 1) was designed and synthesized by Gene Tools LLC (USA) and a control MO with fluorescein tag (targets a human betaglobin mutation, 5′-3′ CCTCTTACCTCAGTTACAATTTATA). MOs were diluted to 2 ng/nl in Danieau buffer (58 mM NaCl, 5 mM HEPES, 0. 7 mM KCl, 0. 6 mM Ca(NO 3 ) 2 , 0. 4 mM MgSO 4 ; pH 7. 6) and supplemented with phenol red. We injected 4 ng of MO into the yolk sac of embryos at the 1-cell stage.
Chorion movement analysis in zebrafish At 1 dpf (24 h post fertilization), zebrafish were recorded in their chorions for 1 min at 30 frames/s using a Leica EZ4 W stereomicroscope. Videos were analyzed using DanioScope software (Noldus Information Technology Inc. , Leesburg, VA) to automatically assess burst activity, duration of bursts and burst count/minute.
The 'analyze particles' function was used to select all clusters in the myotome of interest and this provided a number of clusters and area of each cluster. Number of clusters per 100 µm 2 was derived from number of clusters per myotome and the area of the myotome. Data was collected from at least 4 myotomes per fish and is presented as average value per fish. Co-localization analysis between SV2 and αBTx was performed on maximum intensity projections using the 'JACoP' Fiji plugin  71. Briefly, individual myosepta or myotomes were outlined for analysis and then each fluorophore was subject to manual thresholding to remove background. The Mander's correlation coefficient was calculated to give a value between 0 and 1, reflecting the degree of co-occurrence of signals between both SV2 and α-bungarotoxin, and also αbungarotoxin and SV2.
Spontaneous movement analysis of 5dpf tefm -(F0) knockout zebrafish At 5dpf, 48 uninjected control larvae and 48 tefm -(F0) knockout larvae were placed into individual wells of a 96 well transparent microtiter plate in 200 µL of E3 medium. Using a Zantiks MWP (Zantiks. Ltd) system, the distance moved (mm) of each larva was recorded over the period of 80 min while being exposed to either light or darkness for alternating periods of 10 min. The movement during the first 5 min of light and dark cycle 2-4 was used in the final movement analysis to allow for initial habituation to the protocol and to avoid stimulus desensitization. Zebrafish experiments. Statistical analysis was performed using GraphPad Prism software (v9, BD Biosciences, San Jose, CA, USA). Outliers were removed using the ROUT method (set at 1%), then data tested for normal distribution and, from these results, either a nonparametric Mann-Whitney test or parametric test unpaired t-test were applied.
Acknowledgements L. V. H, P. R-G. , C. A. P and M. M. were supported by  Medical Research Council, UK  ( MC_UU_00015/4 ). H. L. receives support from the  Canadian Institutes of Health Research (Foundation  Grant  FDN-167281 ), the  Canadian Institutes of Health Research and Muscular Dystrophy Canada  ( Network Catalyst Grant for   NMD4C ), the  Canada Foundation for Innovation  ( CFI-JELF 38412 ), and the  Canada Research Chairs programme (Canada Research Chair in Neuromuscular Genomics and Health ,  950-232279 ). E. O. is supported by an  AFM-Téléthon postdoctoral  fellowship. CMG receives support from the  Swedish Research Council  ( 2017-01257 ), the  Swedish Cancer Foundation  ( 2017-631 ), the  Knut and Alice Wallenberg Foundation , and grants from the  Swedish state  under the agreement between the Swedish government and the county councils, the  ALF  agreement ( ALFGBG-728151 ).
Data were analysed using the  RD-Connect Genome-Phenome Analysis Platform  developed under FP7/ 2007-2013 funded project (grant agreement no  305444 ) and funding from  European Joint Programme in Rare Disease (EJP-RD)  and  INB/ ELIXIR-ES. This research was also supported by a  New South Wales Office of Health and Medical Research Council Sydney Genomics Collaborative  grant (J. C. ),  NHMRC  project grants ( 1026891  to J. C. , 1140906 to D. A. S. and  1164479  to D. R. T, J. C. and D. A. S. ), and  NHMRC  research fellowships ( 1102896  to D. R. T. and  1140851  to D. A. S. ). We are grateful to the  Crane and Perkins  families for their generous financial support. The research conducted at the  Murdoch Children's Research Institute  was supported by the  Victorian Government's Operational Infrastructure Support Program.
Data availability The authors declare that the data supporting the findings of this study are available within the paper and its supplementary information files. Accession number for TEFM cDNA used in the study is NM_024683. 4. The structure of TEFM (PDB ID: 5OLA) was sourced from PDB (https:// www. rcsb. org). The RNASeq fastq files can be accessed on GEO (GSE185245). Diagnostic next generation sequencing data can be made available by the authors on request as this is case sensitive. The mass.
Statistical Analysis All analyses were done in SPSS 22. 0. Pairwise comparisons between parameters of hemolymphs extracted in PBS and DMSO were made with Student's t-tests. Multiple comparisons between parameters of hemolymphs from bees of the four study sites were made with One-way ANOVAs followed by a Tukey's test. When the p-value was lower than 0. 05, the difference in the means of the samples was considered statistically significant. Means are reported with standard errors (mean ± SE).
Statistical Analysis All analyses were done in SPSS 22. 0. Pairwise comparisons betwee hemolymphs extracted in PBS and DMSO were made with Student's t-tes parisons between parameters of hemolymphs from bees of the four study with One-way ANOVAs followed by a Tukey's test. When the p-value was the difference in the means of the samples was considered statistically si are reported with standard errors (mean ± SE).
There is an explosion in the number of ontologies and semantic artefacts being produced in science. This paper discusses the need for common platforms to receive, host, serve, align, and enable their reuse. Ontology repositories and semantic artefact catalogues are necessary to address this need and to make ontologies FAIR (Findable, Accessible, Interoperable, and Reusable). The Onto-Portal Alliance (https://ontoportal. org) is a consortium of research and infrastructure teams dedicated to promoting the development of such repositories based on the open, collaboratively developed OntoPortal software. We present the OntoPortal technology as a generic resource to build ontology repositories and semantic artefact catalogues that can support resources ranging from SKOS thesauri to OBO, RDF-S, and OWL ontologies.
Ontology repositories are usually developed to address the needs of certain communities. Their functionalities span from simple listings with metadata description (i. e. , libraries) to rich platforms offering various advanced ontology-based services (i. e. , repositories), including browsing, searching, visualizing, computing metrics, annotating, recommending, accessing data, assessing FAIRness, sometimes even editing. More generally, ontology repositories help ontology users to deal with ontologies without asking the users to manage them or to engage in the complex and long process of developing them. Plus, as with any other data, repositories help make ontologies FAIR (Findable, Accessible, Interoperable, and Re-usable)  [5, 6]. The OntoPortal Alliance (https://ontoportal. org) is a consortium of several research and infrastructure teams and a company dedicated to promoting the development of ontology repositories-in science and other disciplines-based on the open, collaboratively developed OntoPortal open-source software.
OLS technology has always been open source but some significant changes (e. g. , the parsing of OWL) facilitating the reuse of the technology for other portals were done with OLS 3. 0 released in December 2015. Until very recently (2022), in the context of the NFDI projects (https://terminology. tib. eu), we had not seen another public repository built with OLS. On the other hand, the NCBO BioPortal was developed from scratch as a domain-independent and open-source software. Although it has been very early reused by ad-hoc projects (e. g. , at OORI, NCI, and MMI), it is only in 2012, with the release of BioPortal 4. 0 that the technology, made of multiple various components was packaged as a virtual appliance, a virtual server machine embedding the complete code and deployment environment, allowing anyone to set up a local ontology repository and customize it.
OntoPortal Technology The OntoPortal virtual appliance is mainly made available as an OVF file (Open Virtualization Format) to deploy on a server. Amazon Machine Instances are also available. Once installed, an OntoPortal instance provides an out-of-the-box semantic artefact catalogue web application with a wide range of features. A demo server can be visited at https://demo. ontoportal. org. Administrators of the platform can then include the desired semantic artefacts directly, reach out to their users to let them upload resources, or both. In the following, we review the OntoPortal architecture and default OntoPortal services-many of these have been presented and published already in the context of referenced publications of BioPortal or subsequent projects. Then, we describe the latest services and functionalities developed by members of the Alliance that are being discussed and step-by-step included in the main code branch when relevant.
OntoPortal Standard/Default Technical Architecture OntoPortal is a complex system composed of multiple -coherently connected-stacks depending on the services implemented. Most of the components (listed in Table  1 ) are developed in Ruby (www. ruby-lang. org). Sometimes, they rely or reuse third party technologies, especially in the storage layer. The OntoPortal system architecture is presented in Fig. 2. It is structured in several layers briefly described here: • The storage layer is mainly made of a triple-store which saves each semantic artefact RDF content in a distinct graph, as well as other data (metadata records, mappings, projects, users, etc. ). We have always used 4store (https://github. com/4store), a very efficient and scalable RDF database. The technology being outdated, we are transitioning to other triple-stores.
This layer also uses: (i) Redis-based key-value storage for application caches and the Annotator dictionary datastore; (ii) Solr search engine (https://solr. apache. org) to index and retrieve ontologies content data with the Search service. • The model layer implements all the models (objects) of the business logic and the mechanisms to parse the semantic artefact source files using the OWL-API (https:// github. com/owlcs/owlapi) and persist/retrieve them from the triple-store using our built-in Object-Relational-Mapping-like library, called GOO. • The service layer, with Ruby/Sinatra (https://sinatrarb. com), implements the core OntoPortal services working with the models: Search, Annotator and Recommender. When necessary, these services rely on specific storage components and external tools (e.
g. , Mgrep concept recognizer  [33] ). A command line administration tool was also integrated to do jobs monitoring and managing the integrity of the system. • The Application Programming Interface (API) layer implements a unified application programming interface for all the models (e. g. , Group, Category, Class, Instance, Ontology, Submission, Mapping, Project, Review, Note, User) and services supported by OntoPortal. The API can return XML or custom formats, but the default and most-used output is JSON-LD, which uses JSON to encode RDF. • The user interface is a typical web application built mostly with Ruby On Rails (https://rubyonrails. org), a popular open-source framework written in Ruby. The user interface offers a set of various views to display and use the services and components built in the API layer.
Typically, groups associate ontologies from the same project or organization whereas categories are about the ontology subjects/topics. OntoPortal also offers a "slice" mechanism to allow users to interact (both via API and UI) only with a subset of ontologies in an installation. If browsing the slice, all the portal features will be restricted to the chosen subset, enabling users to focus on their specific use cases. Library, Versioning and Search: The primary mission of an OntoPortal installation is to host and serve ontologies and semantic artefacts. The portals accept resources in multiple knowledge representation languages: OWL, RDF-S, SKOS, OBO and UMLS-RRF. 5  Ontologies are semantically described with rich metadata (partially extracted from the source files), and a browsing user interface allows to quickly identify, with faceted search, the ontologies of interest based on their metadata.
Automated Access: REST API and SPARQL Endpoint: OntoPortal provides two different endpoints for accessing its content: (i) a REST web service API that returns JSON-LD; (ii) a SPARQL endpoint  [39]. These endpoints are consistent across all the OntoPortal deployments, so software written originally to query a specific portal (most commonly BioPortal) can be used equally well to query any other OntoPortal deployment, assuming that deployment makes its endpoints accessible. The REST web service API provides access to all the resources (read/write) and services described above, and queries are highly customizable using various request parameters. To efficiently handle large result sets, pagination is available for the majority of the endpoints. The SPARQL web service provides direct read-only access to the OntoPortal triple store.
Since Onto-Portal is developed to work with semantic web technologies and artefacts, all of its content (ontologies, mappings, metadata, notes, and projects) is stored in an RDF triple store. For security, some OntoPortal installations (like BioPortal itself) choose not to make the primary triple store queryable. In these cases, a copy of the triple store can be made accessible  [39]. Many external applications developed by the biomedical semantics community to use BioPortal can be adapted to work with any other portal; examples include OntoMaton, OntoCAT, Zooma, Galaxy, REDCap, and FAIRsharing. More recently, we have seen tools developed directly considering multiple OntoPortal endpoints e. g. , https:// github. com/cthoyt/ontoportal-client, and we are discussing federating search and other queries/services across all public OntoPortal systems.
Enhanced Semantic Annotation Workflow: The SIFR BioPortal offers natural language-based enhancements of the Annotator making it first available for French text, but also adding three scoring methods to rank semantic annotations by order of importance  [40]. It also introduced significant improvements that support clinical context detection (i. e. , in the context of clinical text, the Annotator can detect negation, experiencer-the person experiencing the symptom or event-and temporality)  [22]  that were eventually made available for English in OntoPortal.