dataesr/scientific-paragraphs-categorization huggingface dataset:
The MTBP technology. The MTBP provides a single unified framework for sharing and harnessing NGS data across CCE centers. Deidentified patient clinical and pathological information is fetched from a centralized electronic case report forms system, whereas sequencing data files are retrieved from different institutional and external laboratories. Data transfer, storage and access are implemented by a set of technical measures in accordance with the European legal framework under compliance with data protection regulation (Methods). After data capture, the system triggers a number of pipelines for data integrity control, format harmonization and variant interpretation as appropriate. This ultimately creates the corresponding patient reports, which are immediately shared with the clinical investigators.
Discussion The MTBP provides a unified platform for implementing precision oncology strategies in a truly collaborative manner. As the complexity of cancer biomarkers continues to grow, automating the interpretation and reporting of sequencing results decreases the need for manual procedures and facilitates rapid, comprehensive and consistent clinical decision-making. In addition, the MTBP creates the infrastructure to systematically gather the molecular and clinical information in a 'biorepository' of data, which supports the discovery of new biomarkers and insights for future trial designs. However, deploying the MTBP across the CCE network raised multiple challenges, such as (i) ensuring the interoperability with the information technology systems of each connected center; (ii) automating the retrieval of clinical, pathological and sequencing data provided by different facilities; (iii) developing user-friendly interfaces for distinct user types, such as medical practitioners, project managers and data analysts; (iv) coordinating the efforts to agree on variant interpretation criteria and actionability prioritization; and (v) creating the associated resources, such as a database with up-to-date information of the clinical trials open for recruitment across the network.
Genomic alterations considered for the use of atezolizumab in the first treatment module opened for the Basket of Baskets (NCT03767075) study opened during the evaluation of the CCE patient cohort described here are the following: BRCA1 These are the genes recommended by the American College of Medical Genetics and Genomics (ACMG SF v2. 0 update), plus some additions (marked with an asterisk) agreed to be clinically actionable by the CCE Genetic Counseling Task Force. Of note, the MTBP issues genetic counseling alerts only if the germline variants observed in these genes are estimated to be loss of function based on well-curated evidence and/or bona fide biological assumptions, regardless.
Acknowledgements We acknowledge the Ensembl team at  EMBL-EBI  and  W. Zhou  for the development and support of the Ensembl Variant Effect Predictor and TransVar tools, respectively; the  IT department at SciLifeLab  for their technical support in deploying the portal; the developers and curators of publicly available knowledge bases for their work in these resources; the  OncoKB team  for facilitating the use of their web API in the public-MTBP website; and  H. Zhang  for technical support. The project has received support from the  Personalized Cancer Medicine program  at Karolinska funded by The  Cancer Research Funds of Radiumhemmet, Swedish Foundation of Strategic Research  (principal investigator, J. L. ),  Stockholm County Council  (ALF funding, principal investigator, J. L. ), from  Cancer Research UK  in the Cambridge Centre, from the  CaixaResearch Advanced Oncology Research Program  supported by  "la Caixa" Foundation , and from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement  965397  (CCE_DART).
We also acknowledge  F. Hoffmann-La Roche  for their support in the Basket of Baskets trial ( NCT03767075 ). Boehringer Ingelheim ,  Celgene ,  Chugai, Clovis, Daiichi Sankyo, Debiopharm, Eisai, Eos, Exelixis, Forma, Gamamabs, Genentech, Gortec, GSK, H3 Biomedicine, Incyte, Innate Pharma, Janssen, Kura Oncology, Kyowa, Lilly, Loxo, Lysarc, Lytix Biopharma, Medimmune, Menarini, Merus, MSD, Nanobiotix, Nektar Therapeutics, Novartis, Octimet, Oncoethix, Oncopeptides AB, Orion ,  Pfizer ,  PharmaMar ,  Pierre Fabre ,  Roche ,  Sanofi ,  Servier ,  Sierra Oncology, Taiho, Takeda, Tesaro and Xencor. R. S. reports research funding from  Pfizer ,  AstraZeneca ,  PharmaMar ,  Roche  and  Daiichi Sankyo ; speakers honoraria from  Pfizer ,  Daiichi Sankyo  and Novartis; and participation in ad boards for  Pfizer ,  Daiichi Sankyo  and  Novartis.
Data availability Sequencing data have been deposited to the European Genome-phenome Archive, which is hosted by the European Bioinformatics Institute and the Centre for Genomic Regulation. Because of patient privacy constraints, the data are under controlled access and available upon reasonable request to the CCE Basket of Baskets Data Access Committee (bob@vhio. net) based on European Genomephenome Archive terms (see accession number EGAS00001005893 for further details). Source data are provided with this paper.
Code availability Aggregated results shown in this article were created using Python (>3. 0) NumPy, Pandas and Matplotlib open-source libraries. The code for the MTBP components reproducing the variant annotation framework discussed in the manuscript has been deposited to Zenodo with access restricted for research use only (see https://doi. org/10. 5281/zenodo. 6376428 for terms of use details). Access to other components available in MTBP production systems can be provided upon agreement with the copyright holders.
Basket of Baskets study. Genomic alterations considered for the use of atezolizumab in the first treatment module opened for the Basket of Baskets (NCT03767075) study opened during the evaluation of the CCE patient cohort described here are the following: BRCA1 These are the genes recommended by the American College of Medical Genetics and Genomics (ACMG SF v2. 0 update), plus some additions (marked with an asterisk) agreed to be clinically actionable by the CCE Genetic Counseling Task Force. Of note, the MTBP issues genetic counseling alerts only if the germline variants observed in these genes are estimated to be loss of function based on well-curated evidence and/or bona fide biological assumptions, regardless or BRCA2 loss-of-function mutations (arm 1A); MLH1, MSH2, MSH6 or PMS2 loss-of-function mutations (arm 1B); POLE or POLD1 switch-of-function mutations (arm 1C); intermediate or high tumor mutation burden without an apparent mechanism for DNA damage repair malfunction (arm 1D); loss of function observed in other DNA damage repair genes (arm 1E); and CD274 (PDL-1) copy-number amplifications (arm 1F).
SOFTWARE ARCHITECTURE The software architecture for the G-Fly-Crane is implemented using GenoM3, an open-source (BSD-like license) tool developed at LAAS-CNRS to design real-time software architectures. GenoM3 allows the design and production of modules that encapsulate algorithms with inter-process communication, datapool capabilities with external access, and automatic telemetry generation. This framework has been tested in a variety of research robotic prototypes, and used in several European Space Agency (ESA) projects  [10]. Figure  4  shows the architecture implemented. It is composed mostly by GenoM modules and divided into three layers, namely: (i) UAV layer, which includes the modules related with the flight controllers, state estimation and motor speed controller for each robot; (ii) shared control layer, which provides the connection between robots, platform and the user, implementing the controllers needed to operate the whole G-Fly-Crane system; and (iii) user layer, the inter-face with the human operator, which provides the visualization of the system status and the access to safety parameters, controller gains, logs, etc.
Background Limits to data reusability in life sciences In life science, there has been a long-standing effort to standardize and integrate reference datasets and databases  [1, 2]. Despite these efforts, many studies data are provided using specific and non-standard formats  [3]. This hampers the capacity to reuse the studies data in other pipelines, the capacity to reuse the pipelines results in other studies, and the capacity to enrich the data with additional information  [4]. The Regulatory Circuits project The Regulatory Circuits project  [5, 6]  is one of the largest efforts for integrating human cell genomics data. It has been cited 217 times (Google Scholar -17 May 2021). Its data originate from several independent programs such as FANTOM5  [7]  and ENCODE  [8] , and are aggregated by a complex in silico pipeline.
Those two queries are time efficient to retrieve the TF-genes relations of enhancer sample-specific networks and promoter sample-specific. However, those queries do not combine the scores from enhancer and from promoter nor combine the samples constituting a tissue to obtain its regulatory network. The solution proposed does not store the computed networks and requires to be computed each time a user may need them. Another limitation in the use of this resource is the lack of metadata. These are necessary to identify the subset of the samples that meet a user's specific criteria when reusing Regulatory Circuits. Overall, these limitations require a more complete, more flexible and reusable representation of the Regulatory Circuits dataset.
This encompasses both specializing a network by selecting a subset of the samples it is based on, or generalizing a network by adding other samples. Third, it will allow to combine the data from Regulatory Circuits with user-specific samples, to extend the resource. Approach First, we designed the structure of the samplespecific graphs as well as the SPARQL queries for computing the weights associated to TF-genes relations. Second, we designed the structure of the tissue-specific graphs as well as the SPARQL queries for computing the weights and scores associated to TF-genes relations based on the values computed for the samples. Third, we described the biological data associated to the samples and the tissues, we complemented them with mappings to external public databases, and used them to enrich the original dataset with an experimental context graph.
Finally, we proposed a modular organization of the aggregated datasets into RDF named graphs linked by an additional metadata graph, which allows to identify the relevant portions of the dataset in order to maintain query performances. Linked Extended Regulatory Circuits (LERC), a flexible resource to query tissue-and sample-specific TF-genes interactions. We provide an RDF represen- tation of the Regulatory Circuits, called Linked Extended Regulatory Circuits (LERC). It consists in (i) descriptions of biological and experimental context, linked to the references databases, (ii) annotations about TF-gene interactions at the sample level for 808 samples, (iii) annotations about TF-gene interactions at the tissue level for 394 tissues, (iv) metadata connecting the three above listed named graphs.
Overall, the resource contains 3,910,794,050 triples and is available as a Virtuoso endpoint at https://regulatorycircuits-lod. genouest. org/  [15]. We show how our flexible architecture supports biologically-relevant SPARQL queries that were not possible with our previous representation of Regulatory Circuits's final results in RDF. This integration scheme used to construct LERC is applicable to any similar dataset produced in other projects.
Methods All the results presented in the paper were obtained by relying on Semantic Web technologies. Our strategy was to create an RDF graph for each different dataset handled in the regulatory circuits project (biological dataset, experimental context dataset, 394 tissue-specific TF-gene datasets, 808 sample-specific TF-gene datasets). Then, the capability of the RDF language to identify groups of related triples as named graphs was used to link all the RDF datasets together. This modular design allows (1) to assign metadata describing each group, (2) to improve SPARQL queries performances by only considering some relevant portions of a dataset, (3) to extend the dataset by adding new groups such as new samples data and (4) to reuse some portions of the dataset in other studies. Note that this addresses the limits to data reusability identified at the beginning of the Background section.
Biological data RDF graph The biological data graph contains the minimal set of biological entities required to build the Regulatory Circuits networks together with their attributes (values), and describes the relations between these entities. As detailed in  [14]  and depicted in Fig. 1 , it is based on five main types of biological entities: three related to genes or proteins (gene, transcript, TF) and two related to chromosomal regulation regions (promoter, enhancer), connected by five reified relations (see below). The identifiers of genes (19,125 instances of the class Gene), transcripts (53,549 instances of the class Transcript) and transcription factors (691 instances of the class TF) are constructed based on the names provided by the Regulatory Circuits datasets (HGNC reference identifiers for TF and Gene, Ensembl transcript names for Transcript).
These identifiers are linked to identifiers from external databases such as UniProtKB  [16]  (release 2021_04) and Ensembl  [17]  (release 104) as follows. Genes are associated to the UniProt identifier of their reviewed proteins; in case of several proteins being reviewed for a gene, the longest one is selected. Both genes and transcripts are associated to their Ensembl identifiers as already available in Regulatory Circuits datasets. There are two classes of regulatory regions: Promoter (184,828 entities) and Enhancer (43,011 entities). The dataset comprises five types of reified relations: two between TFs and regulatory regions weighed by the confidence of transcription factor binding site in the region (1,169,797 entities for TF_promoter and 524,816 for TF_enhancer), two between regulatory regions and transcripts weighed by the distance and the Weight_Distance between those entities (123,441 entities for promoter_transcipt and 950,514 for enhancer_transcipt), and a last one between transcripts and genes (53,449 entities).
Experimental context graph The experimental context graph describes the experimental information about the 808 samples (cell types, organs, patient clinical data [age, gender. ], diseases. ), about the 394 tissues (linked to the samples they are composed of ) as well as the mappings to reference databases. Note that the experimental data (expressions and ranks) belong to the biological data graph. All the information contained in the experimental context graph are extracted from the nmeth_. 3799-S2. xlsx file present in the Regulatory Circuits supplementary data, and formatted to respect the identifiers of the generated samples-specific or tissue-specific RDF graphs. When applicable, we also include links to other knowledge bases from the Linked Open Data such as gene identifiers from Ensembl, protein identifiers from UniPro-tKB, cell types and anatomical structures from the Uberon and the Foundational Model of Anatomy ontologies.
Third, an additional metadata graph associates each of these named graph with the corresponding sample or tissue. Fourth, the samples and tissues' descriptions (i. e. the organs, cell types as well as patient's characteristics) as well as the composition relations of tissues into samples are represented in the experimental context graph. Thus, a user can query the experimental context graph to identify the samples and tissues that meet some constraints, and retrieve the associated networks. Likewise, new samples or tissues can be combined with Regulatory Circuits by adding the corresponding graphs and generating the associated metadata and experimental context graphs. In both cases, the weights and scores for the regulation relations of new dataset can be recomputed with the queries from Figs.
2  and 3 , addressing the reusability and reproducibility requirements. original project  [5]. All data related to Linked Extended Regulatory Circuits (LERC) resource are available on the website of the project: https://regulatorycircuits-lod. genouest. org. The RDF version of the dataset is under the Attribution 4. 0 International (CC BY 4. 0) license. The SPARQL queries used to generate the sample and tissue-specific TFgene graphs are available on GitHub https://github. com/ mlouarn/RCsparql/. The generated turtle files are available at https://zenodo. org/record/4889146.
c Execution times of the queries insert_tissue X. rq where X depends on the number of samples in the tissue (cf. Fig. 3  and github repository) for contructing the graphs in (b), calculated for the first 55 tissues networks. Structure of the 394 tissue-specific graphs representing the parameters of the TF-gene regulation relations obtained according to the Max score (between 180,534 and 1,369,313) and we have no explanation for the TFgenes interactions that have a positive score according to Regulatory Circuits, even though the corresponding scores in all the associated samples are zero. All the TF-genes interactions, with their weights and scores, computed or from the original networks are stored in their respective tissue-specific named graphs and are available for querying following the SPARQL patterns available on GitHub.
Modular organization of all the resources associated with regulatory circuits: the LERC dataset As seen previously, the initial RC dataset  [14]  can be associated simultaneously to (i) an RDF experimental context graph (see Methods section), (ii) 808 sample-specific RDF graphs, and (iii) 394 tissue-specific RDF graphs. In order to allow for a transversal exploration and to avoid performance issues when integrating and querying large datasets, we integrated all these resources in a single RDF dataset. This dataset is organized according to a modular architecture based on named graphs and shown in Fig. 6. The different layers of the modular organization are linked by an RDF metadata graph, which contains all the information about the other graphs including their VoID descriptions and characteristics about the graphs (number of triples, entities, etc.
), as well as the associations of the samples and tissues with their respective graph. The RDF model is supported by the regulatorycircuits. owl ontology provided on the GitHub repository (cf. Availability section). Overall, the LERC dataset encompasses a total of 1,205 graphs of five types: 1 source biological data graph (in blue), 1 experimental context graph (in purple), 808 sample-specific graphs (in green), 394 tissue-specific graphs (in orange) and 1 metadata graph (in grey). Their main characteristics are as follows: • The source biological data graph representing the biological data of the Regulatory Circuits and FANTOM5 projects was already published in  [14]  (see Fig. 1 ). • The experimental context graph contains all the information about samples and tissues. As shown in Fig. 7 , it describes the experimental information about the 808 samples (cell types, organs, patient, diseases.
) and mappings to reference databases such as Uberon) and the 394 tissues (links to the samples they are composed of). • Each sample-specific graph provides the weights of the TF-gene interactions associated with the considered sample (See Fig. 4 ). • Each tissue-specific graph provides the weights and scores of the TF-gene interactions associated with the considered tissue-specific regulatory networks, which is an aggregation of biologically related individual samples (See Fig. 5 ). • The metadata graph contains all the information about the other graphs including their VoID descriptions, number of triples and entities. The LERC resource is composed of 3,910,794,050 triples and the distribution of the triples by graphs can be seen in Fig. 6. In total it required 28. 6 days CPU on a 32GB RAM virtual machine with 4 VCPU to generate the RDF dataset from the initial integration of the biological data graph to the computation of the tissue-specific graphs.
We notice that such a tailored result is obtained with a very short SPARQL query (Fig. 8 ) which can be easily adapted to the user's needs. If the user wants to add to LERC the result of this new network computed with specific conditions, it can be done by using one of the injection queries (see Methods section and Fig. 2 ) replacing the samples' names by the ones respecting the conditions and naming the resulting graph with a new specific name. Naturally, this should be performed on the user's local copy of the dataset. For the sake of further studies and to follow the original Regulatory Circuits networks, these two weights (weightP & weightE) may need to be combined in a single score. Fig. 8  SPARQL query for retrieving the set of samples that meet some conditions expressed by the user (here, identify the subset of the "CD14+ Monocytes" samples taken in the blood of patients over 55 years old).
Discussion In this article, we address the issue of making the biological datasets from the Regulatory Circuits project  [6]  reusable. We exposed the intermediate results such as the sample-specific regulation networks so that biologists can now access the information they need. This adds both the capacity to reuse portions of the pipeline's results in other studies, and the capacity to enrich the data with additional information. According to the Findability, Accessibility, Interoperability, and Reuse (FAIR) guidelines  [18] , the construction of the LERC resource to represent and extend the Regulatory Circuits project as an RDF dataset follows the best practices, using reification for weighted relations, and using named graphs to link the RDF graphs created for each different dataset handled in the regulatory circuits project.
It also provides mappings to reference databases such as UniProtKB, Ensembl, Uberon, the Foundational Model of Anatomy ontology (FMA) and the Human Disease Ontology and follows the faldo chromosomal localization format. Federated SPARQL queries can then be used to combine information for Regulatory Circuits with information from these resources (e. g. associations with diseases, cell types or anatomical location. Examples illustrating federated queries and ontology-based reasoning are provided in the Github respository). The LERC resource is available on a persistent domain and all queries are publicly available on GitHub. By both converting and enriching the Regulatory Circuits dataset into RDF with our modular principles, and by allowing flexible SPARQL queries on the LERC resource, our contribution to reusability is threefold.
First, it facilitates the reuse of Regulatory Circuits results in other studies by providing access to the tissue-specific regulatory networks and the associated information. Second, it facilitates the reuse of the studies' data in other pipelines by providing access to the samples' experimental context and to the intermediary results such as the sample-specific regulatory networks, which can be reused to compute other indicators than Regulatory Circuits published weights and scores. Third, it provides the capacity to enrich the Regulatory Circuits dataset with additional information as the data model and Semantic Web technologies support adding new samples or defining new tissues, and the SPARQL queries we provide can generate the corresponding weights and scores. Overall, the Regulatory Circuits case study confirms that Semantic Web technologies are a relevant solution for reusing knowledge bases  [12, 19] , and demonstrates that they are also applicable to address the challenge of integrating them to project-specific datasets  [20].