dataesr/scientific-paragraphs-categorization huggingface dataset:
Challenges in this process include the recognition of biological entities that have many different but equivalent names, as well as normalizing redundant entries that represent the same entity in different ontologies. To address these challenges, his group developed the Gilda Entity Normalization Service (http://grounding. indra. bio) (94)  and Biopragmatics Stack (https://biopragmatics. github. io), a set of resources to manage bio-ontologies and their relationships  (94). He then presented INDRA, a software platform that automatically assembles biochemical mechanisms extracted from the literature and pathway databases into knowledge bases and explanatory models (Figure  2B )  (95). For example, using this platform on a set of protein kinases with few known substrates [often called dark kinases  (96) ] allowed identifying previously missed kinase targets  (97)  and enabled the creation of a self-updating portal for deubiquitinating enzymes (https://labsyspharm.
github. io/dubportal/) (98). He also described the BioFactoid platform aimed at leveraging author input to create machine-readable pathways at the time of publication  (99). One major issue that came out of the Session 4 discussions was that large databases often already follow most FAIR principles even if they are not yet always totally open source with no systematic protocol to copy the whole database by outside users. However, the protein function information reported in papers does not. There are still papers today that publish information about proteins without providing an identifier. As pointed out in Session 1, above, clear standards imposed by publishers and, thusly, added to checklists by reviewers would contribute to solving this issue. The InChIKey representation of molecules, which generates unique alphanumeric string identifiers for chemicals (100), is an example of where FAIR standards and interoperability have already been implemented and revolutionized the ability to query databases with chemical entities once the standard was universally adopted.
Many discussions focused on the value of small expert databases that can capture very valuable curated knowledge but might not have the resources to follow FAIR practices. It was suggested that larger databases should provide guidelines and standards that the more niche databases could use, both, to allow information flow and to make possible data integration in cases where the smaller databases can no longer be maintained, as most databases have quite a short life span  (101). Finally, the constant opposition demonstrated between the machine readability and human readability for functional annotation information was discussed. Experimentalists sometimes struggle to use GO term identification for describing a function of interest as terms are not always named intuitively or have yet to be created.
For example, the iModulon approach has allowed the prediction and subsequent validation of the unknown E. coli gene, ydhC, which was determined to encode a purine transporter  (105). Future developments that will integrate iModulon with flux balance analysis models are expected to make the platform even more powerful. Gloria Sheynkman (University of Virginia) discussed the complexity of the annotation of protein isoforms in eukaryotes that result from alternative splicing. Indeed, it is well established that different isoforms can have different functions, but, to date, less than 1% of human isoforms are annotated  (106, 107). Sheynkman reported an HTP study of 366 different isoforms from 161 genes that showed that these isoforms can have wide-ranging differences in protein interaction profiles  (108).
Finally, Peter Uetz (Virginia Commonwealth University) described the use of yeast two-hybrid methods to detect genome-wide protein interaction networks  (116)  and how they can be used to provide functional clues regarding domains of unknown function (DUFs)  (117). Uetz emphasized that essential DUFs can range from poorly to highly conserved  (118)  and that even well-studied housekeeping enzymes, such as those involved in glycolysis, can have regulatory or moonlighting roles through interaction networks that had not been appreciated until more recently  (119). The major point that emerged from all discussion groups was that most of the HTP data generated today are not easily mineable because they are not integrated or cross-referenced within most databases. It was emphasized that combining evolutionary associations with functional associations is a powerful way to discover new functions, but the latter are difficult to analyze as one often relies on supplemental excel files or deposited data with obsolete or unmappable identifiers.
Ideally, BLAST-like engine(s) that can find homologs within various available HTP (e. g. expression data in model microbes or proteomics data, or ChIP-Seq data), such as the Fitness-Browser developed to analyze Tn-Seq data  (120) , would allow for the capture of all HTP data available for a given protein family. User-friendly tools for analyzing and comparing coexpression data from different organisms exist [see Table  1  of (  121 )] but vary greatly in the number of organisms covered and the usefulness of their respective outputs. Established databases like UniProt are always interested in ways to incorporate HTP data, but the lack of metadata and standards makes this objective difficult. In the rare cases where standards have been created and used by the community, such as the International Molecular Exchange standards for proteinprotein interactions  (122) , then these data do get successfully integrated into databases and are much easier to mine.
Such standards could allow easier capture of the essentiality data and phenotypes data that have now been gathered for many model organisms over the last 20 years  (123, 124). The recent creation of the Global Biodata Coalition (https://globalbiodata. org/) has been a step in the right direction, providing stable funding for core databases that then could have more resources to work with for communities to create better data standards. Another point of discussion that came up in several sessions in this meeting is the nonavailability of potential substrates for enzymatic or even phenotypic assays that greatly limit the power of HTP screens and require custom synthesis. Nor is it yet clear which HTP datatypes are optimal for understanding gene function, as different approaches (e. g. genetics, metabolomics and proteomics) are likely more or less informative for different protein classes.
Authors must become involved in this process. To make this possible, an infrastructure needs to be established to cement FAIR principles for protein functional annotation. The minimal/desired information about protein function needs to be defined, and the informatics infrastructure to allow annotation and curation of protein functional data at the time of paper submission needs to be implemented. Organizations already in place, such as Force 11 (https://force11. org), show that publishers and librarians are already primed to encourage and enforce FAIR functional annotation practices with publishing authors if the community were to just agree on a framework. Education of future scientists is also required as most biologists are unaware of how the data they generate get imported into the databases that they use.
Several open-access training modules have already been created by EMBL/EBI focusing on biocuration (https://www. ebi. ac. uk/training/online/courses/biocur ation-collection/) or data management; these should be integrated into biology graduate programs. Several successful efforts to involve students in annotation processes have already occurred, such as the HHMI SeaPhage Program, which has sequenced and annotated hundreds of phage genomes (https://seaphages. org), or the CACAO effort that uses undergraduates to check annotations  (48). Students could also be involved in generating functional data like that which have been recently established by the SEA-GENES program funded by the Howard-Hughes Medical Institute where students screen several proteins for certain properties (https://www. hhmi.
org/science-education/programs/science-education-alliance). Building on his experience in creating GenBank and leading the Combrex effort  (125) , Rich Roberts strongly advocated for the creation of a consortium or umbrella organization (or a group within an existing one) that would take on the role of an advocate for articulating the importance of solving the functional annotation problem for all fields of life sciences. This consortium would take on the task of convincing politicians, private donors and/or companies of the need for specific funding. Importantly, the utility of measuring the economic impact of accurate and exhaustive protein functional annotation, an exercise recently performed by EBI  (126) , would go a long way to convince the different stakeholders. A set of grand challenges that could be used to federate efforts and funding were identified.
These include (i) integration and harmonization of all protein functional knowledge that is scattered across the literature and databases into a central resource or as common annotations in all databases. This might be a long-term objective, but the first step, which is improved interoperability between databases, is already in the works; (ii) predicting the context-dependent function of proteins in different organismal groups using an integrative model that takes into account sequence, structure, active sites, phylogenetic relationships, expression profile, subcellular localization, presence of substrates, etc. ; (iii) identifying an incentive system for subject matter experts to provide annotation for databases. For example, a challenge could be correcting the poorly or wrongly annotated proteins in UniProt; (iv) creating high-quality annotations (integrating literature capture, models, expert curation, paralog flagging and confidence scores) for 100, 500 or 1000 representative genomes; (v) finding the genes for the 900 enzymes with EC numbers and no genes and (vi) identifying the function of ALL the genes in a few model organisms such as a yeast or E.
Conclusion To conclude, mechanisms must be put in place to synergize, synthesize and democratize all aspects of the functional annotation ecosystem. Synergies need to be increased between communities (i. e. biocurators, computational biologists and experimentalists) to effectively transform expert experimental knowledge into high-quality, standardized functional annotations. HTP data sets must become easily mineable, so experimentalists who did not generate the data can readily use it to make functional hypotheses. Scientists working on non-model organisms need feedback from the model organism communities with successful stories of capturing all species-specific functional information to envision effective approaches for their biological domains of interest. Strategies already successfully applied for well-studied organisms (e.
g. function curation projects and GO Phylogenetic Annotation) or specific protein superfamilies (e. g. SSNs for Radical SAM enzymes) could be scaled up and applied across the entire spectrum of protein diversity. The increased synergy between databases is critical to creating more highly connected resources of functional data (common IDs, centralized or federated repositories, consistent annotations, etc. ). Finally, synergies between efforts to assess function prediction methods (e. g. Critical Assessment of protein Function Annotation (CAFA), Center for Critical Assessment of Genome Interpretation (CAGI), DREAM and BioCreative) would help leverage different approaches to address the various computational aspects of function prediction. The deficit of biocurators dedicated to recognizing, selecting, standardizing and integrating the vast amounts of experimental knowledge regarding specific proteins that remains untapped within the scientific literature needs to be recognized.
Article https://doi. org/10. 1038/s41591-023-02268-w differences  18  , we ran analyses separately for white matter (WM)-PVS, basal ganglia (BG)-PVS and hippocampal (HIP)-PVS. We followed up identified risk loci in independent samples of young healthy adults and older Japanese community participants and examined shared genetic determinants with other vascular and neurological traits. Leveraging tissue and cell-specific gene expression databases and drug target libraries, we conducted extensive bioinformatics exploration of identified PVS risk loci.
12), causing acute reversible leukoencephalopathy with increased urinary alpha-ketoglutarate; and PNPT1 (chr2p16. 1), causing Aicardi-Goutières syndrome and cystic leukoencephalopathy (Methods, Extended Data Fig. 4  and Supplementary Table  22 ). Although several genes near PVS lead risk variants were described to be involved in glioma, we found no significant enrichment for glioma genes (Methods). To seek evidence for a causal implication of specific genes and variants, we performed transcriptome-wide association studies (TWAS) using TWAS-Fusion (Methods), with European PVS GWAS summary statistics and the GTEx v7 multi-tissue (RNA sequencing) database, focusing on brain, vascular and blood tissues. We found 36 transcriptome-wide significant expression-trait associations for WM-PVS, 25 for BG-PVS and seven for HIP-PVS that were significant in colocalization analyses (TWAS-COLOC), providing evidence of a shared causal variant between the corresponding gene expression and PVS (Supplementary Table  23 ).
We then conducted GWAS meta-analyses across participating cohorts in METAL, using sample size-weighted meta-analysis as PVS were measured on different scales. Meta-analyses were conducted within each ancestry (European (EUR), Asian (ASN), African-American (AA), Hispanic (HISP)) using METAL (https://github. com/statgen/METAL), followed by meta-analyses across ancestries. Ancestry was genetically inferred using principal components of population stratification (Supplementary Tables  1  and 3 ). Genomic control was applied to each study-specific GWAS with a genomic inflation factor greater than 1. 00. Variants with an effective allele count (twice the product of MAF, imputation accuracy and number of participants with extensive PVS) < 10 and significant heterogeneity (P Het < 5. 0 × 10 -8 ) were excluded from the meta-analysis.
Gene-based analyses We performed gene-based analyses on European PVS GWAS meta-analyses. We included variants within 10 kilobase (kb) of the 3′ and 5′ untranslated regions (UTRs) of a gene to capture regulatory variants. We used the MAGMA software implemented in FUMA  66  to perform a gene-based association study, including 19,037 protein-coding genes. This method is based on a multiple linear principal components regression model. Gene-wide significance was defined at P < 2. 63 × 10 -6. We also performed gene-based tests using VEGAS2 (ref. 67), including 18,371 autosomal genes, leading to a gene-wide significance at P < 2. 72 × 10 -6. Genes were considered in the same locus if they were within 200 kb of each other.
We explored whether genome-wide significant PVS risk loci (lead variants or in LD with r² > 0. 9, based on the 1000G European reference panel) were associated with these traits. A P value threshold <3. 3 × 10 -5 , correcting for 21 independent phenotypes, three PVS locations and 24 independent loci tested, was used (Supplementary Table  30 ). We performed a colocalization analysis using COLOC to search for evidence for a single causal variant between PVS and the other phenotypes, a posterior probability (PP4) > 75% supporting a single causal variant for both traits  70. Second, we used LD-score regression (ldsc package: https://github. com/bulik/ldsc/) to estimate the genetic correlation of extensive PVS burden with these phenotypes (P < 7. 9 × 10 -4 was used as a significance threshold, correcting for 21 phenotypes and three PVS locations).
Effect estimates (β values) and SE values were derived from the inverse varianceweighted GWAS meta-analyses. With TwoSampleMR, we estimated https://doi. org/10. 1038/s41591-023-02268-w the effect of each exposure on each outcome using weighted median, random-effect inverse variance weighting (IVW) and MR-Egger. In addition, we confirmed the directionality of the observed associations with the Steiger test  74. With RadialMR (https://github. com/WSpiller/ RadialMR), the putative causal effect of each exposure on each outcome was estimated using the fixed-effect IVW method using the modified second-order inverse variance weight  73. Cochran's Q statistic was used to test for the heterogeneity (P < 0. 05) due to horizontal pleiotropy  73. We excluded outlier SNPs, identified by regressing the predicted causal estimate against the inverse variance weights  73  , and re-ran IVW tests, as well as MR-Egger regression, assessing heterogeneity with Rücker's Q′ statistic  73.
Enrichment analyses in OMIM and COSMIC genes Using hypergeometric tests, we performed enrichment analyses of genes within 1 Mb, 100 kb or 10 kb of the lead variants, but also of genes within 10 kb of the lead variants with intragenic variants, and genes within 10 kb of the genetic loci with intragenic lead variants. We used the rest of the protein-coding genome as a reference. We performed the analysis first combining loci of all PVS locations, and second including only WM-PVS loci. We searched for an enrichment in different gene groups from the OMIM database  77  , including PVS ('perivascular space' OR 'virchow-robin space'), WMH ('leukoaraiosis' OR 'white matter lesion' OR 'white matter hyperintensities') and leukodystrophy ('leukodystrophy' OR 'leukoencephalopathy') genes. We also searched for an enrichment of genes involved in glioma and glioblastoma, identified in the Catalog Of Somatic Mutations In Cancer (COSMIC) (https://cancer.
Cell type enrichment analysis We conducted a cell type enrichment analysis using Single cell Type Enrichment Analysis for Phenotypes (https://github. com/erwinerdem/ STEAP/). This is an extension to CELLECT and uses S-LDSC, MAGMA and H-MAGMA for enrichment analysis. PVS GWAS summary statistics were munged. Then, expression specificity profiles were calculated using human and mouse single-cell RNA sequencing databases (PsychEN-CODE DER-22, GSE67835, GSE101601, DroNc Human Hippocampus, Allen Brain Atlas MTG and LNG, Mousebrain, Tabula Muris, Descartes Human Cerebrum and Cerebellum; Supplementary Table  24 ). Cell type enrichment was calculated with MAGMA, H-MAGMA (incorporating chromatin interaction profiles from human brain tissues in MAGMA) and stratified LD-score regression. P values were corrected for the number of independent cell types in each database.
Lifetime brain gene expression profile We studied the lifetime expression of genes identified in the TWAS-COLOC analysis, and the three genes associated with WM-PVS burden in both the old and young populations, to search for developmental processes. We used a public database (https://hbatlas. org/) comprising genome-wide exon-level transcriptome data from 1,340 tissue samples from 16 brain regions (cerebellar cortex, mediodorsal nucleus of the thalamus, striatum, amygdala, hippocampus and 11 neocortex areas) of 57 postmortem human brains, from embryonic development to older adults of different ancestries.
Enrichment in drug target genes We used the GREP (Genome for Repositioning)  79  software tool, which quantifies an enrichment of gene sets from GWAS summary statistics in drugs of certain Anatomical Therapeutic Chemical Classification (ATC) classes, or indicated for some ICD10 (10th revision of the International Statistical Classification of Diseases and Related Health Problems) disease categories, and captures potentially repositionable drugs targeting the gene set. Genes with false discovery rate FDR q < 0. 1 in MAGMA were used for enrichment analyses (in GREP) of target genes for approved or investigated drugs curated in DrugBank and the Therapeutic Target Database. We used the Trans-Phar (integration of TWAS and Pharmacological database) software to identify drug target candidates in a specific tissue or cell type  80  , using first FOCUS to identify up-and downregulated genes in participants with extensive PVS burden, followed by a negative Spearman's rank correlation analysis between the gene expression (Z-score) of the top 10% genes with the highest expression variation and the LINCS CMap L1000 library database (Extended Data Fig.
Reporting summary Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. leveraging TWAS on all GTEx v7 tissues and a database of compoundregulated gene expression (C-map) (Methods). A TWAS using FOCUS, which demonstrates fine-mapping of causal gene sets from TWAS results, and 27 tissues in the GTEx v7 database (corresponding to defined 13 tissue-cell-type categories assigned by the 27 tissues in GTEx v7 database and 77 LINCS CMap L1000 library cell types) was performed to identify up-and down-regulated genes in participants with extensive PVS burden, and select the top 10% genes with the highest expression variation. We observed significant enrichment of HIP-PVS in drugs for vascular diseases, including simvastatin (lipid-lowering drug, p = 1.
Acknowledgements Austrian Stroke Prevention Study (ASPS)/Austrian Stroke Prevention Family Study (ASPS -Fam) (E. H. , P. G. G. , H. S. and R. S. ): We thank the staff and the participants for their valuable contributions. We thank  B. Reinhart  for her long-term administrative commitment,  E. Hofer  for the technical assistance in creating the DNA bank,  J. Semmler  and  A. Harb  for DNA sequencing and DNA analyses by TaqMan assays, and  I. Poelzl  for supervising the quality management processes after ISO9001 in the biobanking and DNA analyses. The  Medical University of Graz  and the  Steiermärkische Krankenanstaltengesellschaft  support the databank of the  ASPS/ASPS -Fam. The research reported in this article was funded by the  Austrian Science Fund (FWF)  (grant nos. PI904 ,  P20545-P05  and  P13180 ) and supported by the  Austrian National Bank Anniversary Fund  (grant no.
ANR-10-COHO-05  and  ANR-18-RHUS-0002 ) and from the  University of Bordeaux Initiative of Exellence (IdEX). This project has also received funding from the  European Research Council  under the European Union's  Horizon 2020 research and innovation program  under grant agreement no. 640643  and from the  Fondation pour la Recherche Médicale  (grant no. DIC202161236446 ). Q. L. G. was supported by the  Digital Public Health Graduate Program (DPH) , a  PhD program  supported by the  French Investment for the Future Program  (grant no. 17-EURE-0019 ). Investigating Silent Strokes in Hypertensives: a Magnetic Resonance Imaging Study (ISSYS ) (P. D. , C. C. and I. F. -C. ): This research was funded by the Instituto de Salud Carlos III (grant nos. PI10/0705, PI14/01535, PI17/02222), cofinanced by the European Regional Development Fund.
Lothian Birth Cohort 1936 (LBC1936) (M. L. , M. E. B. , I. J. D. , Z. M. , S. M. M. , M. C. V. H. and J. M. W. ): We thank the LBC1936 cohort members and research staff involved in data collection, processing and preparation. The LBC1936 is supported by Age UK (Disconnected Mind program grant). The work was undertaken by The University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, part of the cross-council Lifelong Health and Wellbeing Initiative (grant no. MR/K026992/1). The brain imaging was performed in the Brain Research Imaging Centre (www. bric. ed. ac. uk), a center in the SINAPSE Collaboration (www. sinapse. ac. uk) supported by the Scottish Funding Council and Chief Scientist Office. Funding from the UK Biotechnology and Biological Sciences Research Council (BBSRC), the UK Medical Research Council (MRC), the Row Fogo Charitable Trust (M.