dataesr/scientific-paragraphs-categorization huggingface dataset:
Candidate p53-DREAM target genes associated with brain abnormalities We next searched for p53-DREAM target genes whose altered expression might contribute to brain abnormalities. Many genes whose mutations cause microcephaly or cerebellar hypoplasia encode proteins implicated in fundamental processes common to all somatic cells (e. g. chromosome condensation, mitotic spindle activity, tRNA splicing). We thus reasoned that BMC differentiation data might also be exploited to search for genes downregulated upon p53 activation and implicated in these diseases. To test this, we used a candidate approach and searched for genes that might be regulated by the p53-DREAM pathway among the 30 genes mutated in primary microcephaly, 23 genes mutated in pontocerebellar hypoplasia, 39 genes mutated in hypoplasia of the cerebellar vermis (Joubert syndrome), 18 genes mutated in syndromes combining microcephaly and dwarfism (Seckel syndrome, Meier-Gorlin syndrome, microcephalic osteodysplastic primordial dwarfism), 12 genes mutated in lissencephaly (often associated with microcephaly); Nuf2, mutated in a bone marrow failure syndrome with microcephaly and renal hypoplasia; Pafah1b3, truncated in a case of brain atrophy; Pqbp1, mutated in the Renpenning syndrome (a X-linked syndrome of microcephaly); and Shq1, mutated in a syndrome with cerebellar hypoplasia, dystonia and seizures; for a total of 126 candidate genes.
A downregulation of gene expression of at least 1. 5-fold upon BMC differentiation was found for 64 of these candidates, including 57 reported to be downregulated upon p53 activation according to the TGR database (Figure  1d , Table  S20 ). Out of the 57 genes, 55 were bound by E2F4 and 36 by LIN9, within regions overlapping the transcription start site in most cases (Figure  1e , Table  S21 ). Out of the 57 human homologs, all were bound by E2F4 and 49 by LIN9 (Figure  1e , Table  S22 ). We next searched the Human Phenotype Ontology website from Jackson Laboratory for genes associated with microcephaly or cerebellar hypoplasia (ontology terms #HP:0000252 and HP:0007360) and found that, out of a list of 1430 genes, 474 candidates were downregulated at least 1. 5 times upon murine BMC differentiation, including 404 reported to be downregulated upon p53 activation (Figure  S6 , Table  S23 ).
Out of these 404 genes, 354 were bound by E2F4 and 153 by LIN9, in regions overlapping transcription start sites in most cases (Figure  S6 , Table  S24 ). Out of the 404 human homologous genes, 371 were bound by E2F4 and 292 by LIN9 (Figure  S6 , Table  S25 ). In sum, the differentiation of bone marrow cells correlated with the decreased expression of 478 genes implicated in microcephaly or cerebellar hypoplasia, including 408 downregulated upon p53 activation according to the TGR database (Table  S26a -b and Figure  3c ). For 303 of these genes, E2F4 and LIN9 were found to bind at identical regions in at least one species (Table  S26c , Figure  3c ). Furthermore, total ChIP scores ≥ 979 were found for 226 of the 303 genes, which appeared as better candidate p53-DREAM targets (Tables S26d, S27, Figure  3c ).
To estimate the relevance of this list of 226 candidates, we analyzed datasets GSE78711 and GSE80434, containing RNAseq data from human cortical neural progenitors infected by the Zika virus (ZIKV) or mock-infected, because ZIKV was shown to cause p53 activation in cortical neural progenitors and microcephaly  [49, 50]. Accordingly, most genes (12/16) known to be transactivated by p53 were upregulated in ZIKV-infected cells (Table  S28 ). By contrast, only 64/226 candidate p53-DREAM targets genes appeared upregulated in ZIKV-infected cells (Table  S28 ). These 64 genes were considered poor candidate p53-DREAM targets and removed from further analyses, leading to a list of 162 candidate p53-DREAM targets related to brain abnormalities (Tables  S26e, S28  and Figure  3c ). Importantly, out of the 162 microcephaly-related candidate genes identified (Table  26e ), 58 also belonged to the list of 213 genes associated with abnormal hematopoiesis (Table  S17e ), consistent with the notion that a deregulation of the p53-DREAM pathway might be involved in both pathological processes.
The Target gene regulation (TGR) database of p53 and cell-cycle genes was reported to include putative DREAM binding sites for human genes, based on separate genome-wide searches for 7 bp-long E2F or 5 bp-long CHR motifs  [23]. We analyzed the predictions of the TGR database for the 151 genes for which we had found putative bipartite DBS. A total of 342 E2F binding sites were reported at the promoters of these genes, but only 64 CHR motifs. The similarities between the predicted E2F or CHR sites from the TGR database and our predicted bipartite DBS appeared rather limited: only 14/342 E2F sites overlapped at least partially with the GC-rich motif of our bipartite DBS, while 27/64 CHR motifs from the TGR database exhibited a partial overlap with the AT-rich motif. Importantly, most E2F and CHR sites from the TGR database mapped close to E2F4 and LIN9 ChIP peaks, but only 16% of E2Fs (54/342), and 33% of CHRs (21/64) mapped precisely at the level of these peaks (Figure  S11 ), compared to 55% (83/151) of our bipartite DBS (Figure  3a ).
Thus, at least for genes with bipartite DREAM binding sites, our method relying on PFM22 appeared to provide more reliable predictions of DREAM binding than the E2F and CHR sites reported separately in the TGR database. Importantly however, predictions of the TGR database may include genes regulated by a single E2F or a single CHR that would most likely remain undetected with PFM22, suggesting that both approaches provide complementary results. Of note, we previously used Consite (http://consite. genereg. net)  [57]  with positional frequency matrices (PFMs) from 6 or 8 experimentally demonstrated murine DBS  [25, 58]  to search for bipartite DBS, a method suitable for the analysis of small (≤10 kb) DNA sequences. Here, the use of PWMscan with PFMs from 10 or 22 DBS made it possible to perform genome-wide searches for bipartite DBS, while facilitating the comparison of mouse and human DNA sequences.
Transcriptome data comparisons We analyzed the gene expression data from Hoxa9-ER expressing hematopoietic stem and progenitor cells grown in the presence of tamoxifen, or in differentiated cells 5 days after tamoxifen withdrawal, a microarray study relying on 45,101 probes corresponding to 20,627 genes (Gene Expression Omnibus #GSE21299)  [34]. For each probe, we calculated the inverse of Log2 from robust multiaverage values. The obtained average (from triplicates) for cells with tamoxifen was given a value of 1, and the ratios before and after tamoxifen withdrawal were calculated. For each gene, we took the probe leading to the highest repression ratio into account and selected those downregulated at least 1. 5-fold upon tamoxifen withdrawal. Among those genes, we identified targets downregulated by human and/or mouse p53 by consulting p53 regulation scores in the Target gene regulation database (www.
RNAseq data analyses To screen for the most relevant candidate p53-DREAM targets, we analyzed publicly available datasets GSE171697 and GSE204924 for blood ontology-related genes, and datasets GSE78711 and GSE80434 for brain ontology-related genes. In addition, we analyzed the dataset GSE121720, containing RNAseq data from glioblastoma cells isolated from patient specimens. This dataset contains 92 samples, which were ranked according to BRD8 expression levels, and the top and bottom 25% samples were assigned as the BRD8 high and BRD8 low groups, as previously described  [79, 80]. Data for the 77 p53-DREAM targets associated with microcephaly or cerebellar hypoplasia (from Table  3 ) were then retrieved from the dataset and analyzed.
Search for putative DREAM binding sites To search for putative DREAM binding sites, we used PWMScan (https://ccg. epfl. ch/pwmtools/pwmscan. php  [51] ) with a custom positional frequency matrix from 10, 21 or 22 murine functional DBS (for details see Figure  S7 ), on both the mouse (mm10) and human (hg38) genomes, with a P value threshold of 10 -3 for cut-off. The putative DREAM binding sites identified were then analyzed for sequence conservation between mouse and human genomes and classified according to PFM score and number of mismatches between the two species at positions 2-6 (for CDE or E2F) and 11-16 (for CHR or CLE) of the DREAM binding sites. For 151 genes, the identified putative DBS, as well as putative E2F or CHR sites reported in the Transcriptional regulation database, were mapped relative to ChIP peaks (or transcriptional start sites) by using the Integrative Genomics Viewer (igv version 2.
Detailed listings of genes can be found in Tables  S17  and S26. (a) Glioblastoma with high BRD8 levels exhibit an overall increased expression of brain-related p53-DREAM target genes. RNAseq data from glioblastoma cells isolated from patient specimens (GSE121720) were stratified according to BRD8 mRNA levels as previously described  [79] , and for each gene the average expression levels in tumors expressing low BRD8 levels was assigned a value of 1. The relative expression for BRD8, five p53-transactivated (TA) target genes (CDKN1A, MDM2, BAX, GADD45A, PLK3) and the 77 p53-DREAM targets associated with microcephaly or cerebellar hypoplasia (from.
Acknowledgements F. T. received funding form the  Fondation ARC  (projet) and the  Ligue Nationale Contre le Cancer (Comité Ile-de-France) ; J. R. and E. E. are PhD fellows of the  Ministère de la Recherche. We thank L. Chen  for technical assistance,  J. Josephides  for suggestions in data analysis, and  A. Fajac  and B. Bardot  for critical reading of the manuscript. Author contributions: conceptualization and project supervision: F. T. ; investigation: J. R. , V. L. , F. T. , C. D. , E. E. ; formal analysis: H. E. ; funding acquisition: F. T. ; manuscript preparation: F. T. , J. R. , V. L. The authors declare that they have no conflict of interest.
Genes are listed according to decreasing repression fold (H/D). According to the Target gene regulation database, 72/76 genes are downregulated upon mouse and/or human p53 activation (the 4 exceptions are mentioned by an asterisk). (c) For each of the 72 p53-regulated genes in (b), the highest chromatin immunoprecipitation (ChIP) scores of E2F4 or LIN9 binding in mouse (m) or human (h) cells are represented. Values are according to ChIP-Atlas. (d) Genes associated with microcephaly syndromes and downregulated upon BMC differentiation. Expression values and ChIP scores represented as in (b), for 64 genes associated with syndromes of microcephaly or cerebellar hypoplasia, of which 57 are downregulated by p53. (e) ChIP scores of E2F4 or LIN9 binding in mouse (m) or human (h) cells for the 57 microcephalyrelated, p53-downregulated genes, represented as in (c).
Data are from 23 samples per group for BRD8, 115 values per group (23 samples x 5 genes) for p53 transactivated targets and 1,771 values per group (23 samples x 77 genes) for p53-DREAM targets. (b) CENPF, ASPM and CASC5 are among the 8 p53-DREAM target genes whose expression is most affected by BRD8 levels in glioblastoma cells. Data for the indicated genes (with 23 samples per group) were retrieved from dataset GSE121720 and analyzed as in (a). ***P<0. 001, **P<0. 01, *P<0. 05 in Student's t tests. See TableS37for additional details.
(b) CENPF, ASPM and CASC5 are among the 8 p53-DREAM target genes whose expression is most affected by BRD8 levels in glioblastoma cells. Data for the indicated genes (with 23 samples per group) were retrieved from dataset GSE121720 and analyzed as in (a). ***P<0. 001, **P<0. 01, *P<0. 05 in Student's t tests. See Table S37 for additional details.
Main Text Introduction Biomedical knowledge, although increasingly abundant, is fragmented across hundreds of resources. For instance, a clinical researcher may use protein information from UniProtKB 1 , genetic variants from COSMIC  2  , protein interactions from IntAct  3  , and information on clinical trials from ClinicalTrials. gov 4. Combining these complementary datasets is a fundamental requirement for exhaustive biomedical research and thus has motivated a number of integration efforts to form harmonised knowledge graphs (KGs; i. e. , knowledge representations based on a machine-readable graph structure). Decisions made on how to store the knowledge at each primary source poses many real-world problems in their recombination, for instance via the use of different identifier namespaces, levels of granularity, or licences  5, 6.
Approach To address these problems, we present BioCypher to improve biomedical knowledge representation by means of: 1) Modularity: To facilitate the maintenance of multiple task-specific KGs from overlapping primary resources, we propose a modular approach that allows recombining individual data "adapters" for primary resources (e. g. UniProtKB and COSMIC) in a reusable manner. This allows delegating the maintenance work to one central place for each adapter instead of having to maintain the primary resource inside each individual KG (see case study "Modularity"). 2) Harmonisation: To facilitate harmonisation of datasets from a biological perspective, we propose to use ontology mapping. Primary data sources may represent similar data in different ways. By mapping divergent representations onto the same ontological class (for instance, "protein" or "somatic variant"), harmonisation can be greatly simplified (see case study "Tumour board").
In addition, the ontological information projected onto each KG entity allows for more flexible and informative queries in downstream analyses (see case study "Network expansion"). 3) Reproducibility: By sharing the ontology mapping from (2) in a project-specific manner, a database used for a specific task can be reproduced more effectively. Since sharing the databases themselves is o en prohibited by their large size, BioCypher facilitates the creation of task-specific subsets of databases to be shared alongside analyses. This is enabled by extensive automation, reducing the time required and file sizes (see case studies "Network expansion", "Subgraph extraction", and "Embedding").
Implementation We build on recent technological and conceptual developments in biomedical ontology that greatly facilitate the harmonisation of biomedical knowledge. We integrate a consistent and comprehensive biomedical ontology, the Biolink model 16   , and an extensive catalogue and resolver for biomedical identifier resources, the Bioregistry  17. Both projects, like BioCypher, are open-source and community-driven. Biolink serves as a biological anchor for the harmonisation of different data sources, and Bioregistry provides consistent vocabularies for representing biological concepts as well as mapping and validation of identifiers. We also facilitate exchange, extension, and modification of the ontological scaffold to accommodate database-specific needs. BioCypher is implemented as a Python library that provides a low-code access point to data processing and ontology manipulation (for examples, see case studies "Tumour board" and "Network expansion").
BioCypher facilitates the decision on how to represent knowledge and simplifies the creation of the corresponding KG, bridging the gap between the field of biomedical ontology and the broad application of databases to biological research questions. BioCypher's translation framework simplifies the creation of custom KGs through a combination of "adapters" (where the data is ingested) and a schema configuration (where the structure of the graph and mappings to ontology are recorded). Building a task-specific KG, given existing configuration, takes only minutes, and creating a KG from scratch can be achieved in a few days of work. This allows for rapid prototyping and automated machine learning (ML) pipelines that iterate the KG structure to optimise predictive performance (for instance, building custom task-specific KGs for graph embeddings and ML (see case study "Embeddings").
By providing modular output adapters, we can adjust to the various needs of KG users. A Neo4j adapter provides rapid access to extensive databases for querying from analysis ( Jupyter) notebooks and facilitates maintenance of large knowledge collections for storage. A CSV writer allows exchange with other knowledge curation services, for instance in the KGX format  12. Python-native adapters (for instance to sparse matrix or NetworkX format) yield knowledge representations that can immediately be used programmatically in machine learning frameworks such as PyTorch Geometric for deep learning  23. For high performance, we implement property graph database technologies that provide intuitive query interfaces, such as the Cypher graph query language developed by Neo4j  24. This enables complex and versatile queries that pave the way towards rich and highly interactive interfaces.
For example, web widgets and apps (such as https://crossbar. kansil. org and https://drugst. one) allow non-computational researchers to browse and customise the database, and to plug it into standard pipelines  25. Additionally, a structured knowledge representation facilitates connection to modern natural language processing applications  26. Neo4j is highly scalable and interacts well with other components of large-scale, distributed, high performance computing infrastructure. Thanks to common standards, tools developed can be shared across projects and used community-wide or in cloud-based services that preserve sensitive patient data (see case study "Federated learning").
Methods BioCypher is implemented as a Python package. Its main purpose is to receive arbitrarily structured biomedical information and create or update a knowledge graph (KG) database that unambiguously maps each KG entity to its corresponding biological class. It uses ontology (the curation of biomedical concepts into a hierarchy of classes) to encode semantic information about KG entities ("objectness") in the biomedical space. From a so ware development perspective, BioCypher can be described as an extract-transform-load (ETL) pipeline with a focus on biomedicine. As a baseline, we use the Biolink model  16  , a comprehensive and generic biomedical ontology system; where needed, this ontology can be exchanged with or extended by more specific and task-directed ontologies, for instance from the OBO Foundry  33.
For author attribution, the preferred way of entering data into BioCypher also includes the exact provenance of each entry, for instance, the publication it was derived from or the consortium responsible for the curation of said content. In the same way, all licences of the contents are propagated forward, enabling the users of the framework to easily determine the allowed uses for any given KG. Particularly for the creation of databases available to the public we recommend using the "strict mode" of BioCypher, which does not allow creation of entities without associated source, licence, and version parameters. In this scenario, BioCypher can effectively prevent the re-distribution of data whose original licence does not allow it, and guarantees that data originators are acknowledged. BioCypher is a free so ware under MIT licence, openly developed and available at https://biocypher.
Usage A key advantage of the modular structure of BioCypher is the ability to reuse existing adapters for primary or secondary knowledge sources. In case no adapter exists for a given resource, it can be created following the pattern of one of our existing adapters and shared with the community for further use. To create a custom KG with BioCypher, two main components are necessary: 1) a YAML file (https://yaml. org) detailing the configuration of graph constituents, including their mode of representation (node or edge) and their preferred identifier (default is included); and 2) one or multiple adapter modules responsible for handing off the data to BioCypher. These two components are described below. BioCypher provides a number of utilities for manipulating the input data as well as the ontological foundation of the graph, for instance, filtering properties of input types or arbitrarily extending the ontology.
Schema configuration Configuration of graph constituents is made available through a graph schema YAML file, whose main purpose is to mediate between the structure of the input data and the resulting BioCypher graph structure. It details, for each constituent species of the graph, the mode of representation (node or edge), the unique identifier system used (e. g. , UniProt or HGNC Symbol), the label to be expected in the input, and -in the case of relationships -the types of source and target nodes. It can also be used to unify the properties attached to nodes and edges in the resulting KG, which is useful when combining sources or dealing with heterogeneous datasets.
Data retrieval (the adapter) A Python adapter module is responsible for the actual database creation process. Briefly, the primary data are ingested and passed into BioCypher through the driver instance created in the build pipeline. BioCypher accepts lists and generators; the latter enable streaming of very large datasets that may not fit into the working memory of smaller machines. We provide information about the structure of the input data through the package and its documentation. BioCypher enables automatic extended labelling of each node with the entire hierarchy of that node derived from the ontology tree, which allows more flexibility in querying the resulting KG and simplifies the queries. For instance, a narrow query could yield interactions of proteins, while a query for "polypeptides" (the ontological parent of "protein") yields proteins, peptides, and precursors; a query for "gene or gene product" additionally returns genes and transcripts without the need for concatenating all individual classes of entities or modifying the underlying graph.
Interacting with the graph All interactions with BioCypher take place through its main module (`driver. py`). It connects to a running Neo4j instance with multiple options for authorisation, and also handles calls to the other modules, such as the batch writer used for the rapid `admin import` feature of Neo4j, which allows building and maintaining very extensive databases in very little time. The main modes of graph manipulation are showcased in our tutorial. The connected graph database can be interactively manipulated through the Python driver supplied by Neo4j through our `neo4j-utils` library (https://github. com/saezlab/neo4j-utils), creating, updating, and deleting nodes and edges ad libitum. BioCypher provides a high-level interface that introduces consistency through the use of standardised structure of the biomedical contents.
For internal consistency and integration purposes, each BioCypher graph contains a meta-graph that details its structure and exact settings (e. g. , identifiers used), which is created upon instantiation of a graph instance and updated a er each manipulation. It includes time-stamps for versioning purposes, which can also be customised with manually created version numbers, facilitating sharing and reproducibility of analyses. Time can be a limiting factor in the interactive creation of graph content. If the dataset is of large size -say, in the realm of millions of nodes and edges -the creation of a graph can take hours to days. For this reason, we also support the creation of consistent CSV files to be used for theneo4j-admin import` shell command. This mode significantly speeds up the database creation through the deactivation of safety features that guarantee the consistency of the graph in interactive mode.