dataesr/scientific-paragraphs-categorization huggingface dataset:
We then generated a phylogenetic tree using the neighbour-joining algorithm included in the neighbour software from Phylip (v3. 698)  [68]  which was used to create the following guide tree for CACTUS  [69] : ((angus:0. 00187,hereford:0. 00115)Anc1:0. 0004,(ankole:0. 00317,((yak:0. 00671,((abuffalo:0. 01228,wbuffalo:0. 0095)Anc6:0. 00438,goat:0. 04443)Anc5:0. 01195)Anc4:0. 00254,brahman:0. 00256)Anc3:0. 00023)Anc2:0. 0004,ndama:0. 00195)Anc0; The HAL archive of multiple whole genome alignments (mWGA) was generated using the software CACTUS  [69] , and then converted to PackedGraph format using the hal2vg software (v. 2. 1)  [70]  with the African buffalo genome as reference. We then used the nf-GraphSeq workflow (https://github. com/evotools/CattleGraphGenomePaper/tree/master/detectSequences/nf-GraphSeq) described in Talenti et al.
We characterized the content of the novel regions by 1) performing a motif analysis using HOMER (v4. 11. 1)  [74] , and 2) by detecting the novel features. To identify these features, we used the annotation generated by Ensembl and available in the rapid release database (http://www. ensembl. info/2020/06/25/ensembl-rapid-release/; accession GCA_902825105. 1). We identified all gene features overlapping a novel sequence using bedtools intersect (v2. 30. 0)  [72] , and identified only these fully overlapping a novel region still with bedtools intersect with the -f 1. 0 option (100% of overlap between the feature and the novel region). Once we identified these fully new gene features, we extracted the GO term and KEGG pathways present in the annotation itself in embl format. To do so, we first converted the file in GenBank format, and then extracted for each gene the transcript IDs, protein IDs and biological terms.
Detection of variants in WGS samples across Africa. For all 196 WGS samples from S. caffer across Africa (raw data is available at ENA via accession numbers PRJEB59220 and ERP144275), reads were mapped with bwa-mem (BWA v0. 7. 17) against the reference genome generated as above. The GATK (v4. 0. 11. 0) pipeline, following the best practices as outlined at https://gatk. broadinstitute. org/hc/enus/articles/360036194592-Getting-started-with-GATK4 was used with HaplotypeCaller to identify variants (SNPs and Indels). The GATK best practice includes a Variant Quality Score Recalibration (VQSR) step that compares all variant calls to those in a high quality set to identify and flag potential false positives. Unlike in well-characterised species no goldstandard set of variants is available for the African buffalo.
PLINK (v1. 90) was used to calculate sample missingness, the proportion of variant sites missing from each sample, and vcftools (v0. 1. 13) to calculate the relatedness of all individuals. For downstream analyses, individual samples with a missingness greater than 0. 15, additionally individuals that were closer than fourth degree relatedness (relatedness value 0. 0625), were also removed, resulting in a variant dataset covering 163 individual animals. We checked for any mapping biases due to use of an East African reference genome, by randomly sampling three animals per country and comparing how read mapping rates differed by longitude (Supplementary Figure  7 ). No obvious mapping bias was observed among the West African samples when mapping to the reference genome obtained from an East African sample.
Genomic diversity analyses The VCF file for the set of unrelated samples was first filtered through bcftools (v1. 9; https://samtools. github. io/bcftools/) to keep only unrelated individuals according to the KING method implemented in vcftools  [77, 78]. A cutoff of 0. 0625 was applied to exclude 3 rd degree relatives or closer. Furthermore only biallelic SNPs in large contigs (>10Kb) were retained. Variants were further filtered using plink (v1. 90b4)  [79]  to restrict to those with a minor allele frequency >0. 05. This dataset was then used to carry out analyses of migration events and effective population size. ADMIXTURE and the identity-by-state phylogenetic tree can benefit from having an even sample size for the different populations/samples deriving from the same location that were tested  [38].
Therefore, for these analyses we identified a representative subsample for the populations with more than 15 animals. Sample size reduction was carried out using the BITE R package  [80]  to select a representative set of individuals for each population. The reduction process was performed on each population separately. For each group we selected the variants with very high call rate (99%) and highly polymorphic (--maf 0. 3). The reduction step in BITE was performed considering only individuals with 95% call rate and up to 10K markers to compute the kinship matrix (options n. trials = 100000, ibs. marker=10000, n. k=2, ibs. thr = 0. 95, id. cr=0. 95). Principal component analysis (PCA) was performed post reduction in sample numbers using plink v1. 90b4. Admixture analysis was performed using ADMIXBoots (https://github.
com/RenzoTale88/ADMIXBoots), a Nextflow workflow that performs bootstrapped admixture (v1. 3. 0)  [81] , defining a consensus of the different K at different iterations using CLUMPP  [82]  and generating plots in R. The workflow was run pruning for variants in linkage (plink --indep-pairwise 5000 100 0. 3), testing every K between 2 and 15, and with 100 bootstraps of 100,000 markers each. A consensus of the different bootstraps was called using CLUMPP in LargeKGreedy mode. Bar charts for each consensus K, boxplots for the distribution of the CV errors and line plots of the H' scores of each K were generated from the pipeline automatically. Bootstrapped identity-by-state (IBS) phylogenetic tree was calculated using the nf-PhyloTree workflow (https://github. com/RenzoTale88/nf-PhyloTree). This workflow uses plink to generate a matrix of identity-by-state distances across individuals.
Estimated Effective Migration Surfaces (EEMS) The EEMS package developed by  Petkova et  al. [84]  was used (https://github. com/dipetkov/eems) to estimate effective migration surfaces. The runeems_snps program was used to visualise spatial population structure in the African buffalo populations and to identify the geographic barriers to migration preventing gene flow across these populations. The runeems_snps program requires the following data as input files: (1) a matrix of average pairwise genetic dissimilarities, (2) sample coordinates, and (3) a list of habitat coordinates, here covering the natural distribution of African buffalo populations on the African continent, and listed as a sequence of vertices organised as a closed polygon. For the input files for EEMS analysis, a matrix of average pairwise genetic dissimilarities was generated from the pruned set of SNP data, using the bed2diffs_v1 program within the EEMS package.
A value of 1 (blue) indicates a tenfold greater migration rate over the average; -1 (orange) indicates tenfold lower migration than average. The courses of the major river systems (Niger, Congo, Nile and Orange rivers), as well as water bodies with a surface area greater than 5,000 km 2 are included to highlight their potential relationships with migratory routes and barriers. Red diamonds indicate geographical location of samples in the dataset. (B) Estimated effective population sizes of African buffalo (solid lines) and human (dashed lines) populations over time. The countries of sampling for each population are indicated in the legend along with the three letter 1,000 Genomes consortium population code for the human data. Only human populations from the 1,000 Genomes consortium dataset of recent African origin are shown.
Acknowledgements This research was funded in part by the  Bill & Melinda Gates Foundation (USA)  and with  UK  aid from the  UK Foreign, Commonwealth and Development Office  (Grant Agreement  OPP1127286 ) under the auspices of the  Centre for Tropical Livestock Genetics   and Health (CTLGH) , established jointly by the  University of Edinburgh (UK) ,  Scotland's Rural College (SRUC, UK) , and the  International Livestock Research Institute (UK). The findings and conclusions contained within are those of the authors and do not necessarily reflect positions or policies of the  Bill & Melinda Gates Foundation  nor the  UK Government. The work was also supported by grants  BBS/OS/GC/000012C ,  BBS/E/D/20002172  and  BBS/E/D/20002174  from the  Biotechnology and Biological Sciences Research Council (BBSRC, UK).
Data availability statement All raw data generated in this project is available through the following routes: for the buffalo reference genome, PacBio, Illumina and Hi-C data is available via Genbank (GCA_902825105. 1) and ENA (PRJEB3658), and the assembly, annotation and associated flat files can be accessed through https://rapid. ensembl. org/Syncerus_caffer_GCA_902825105. 1; transcriptomic data were deposited to ENA (https://www. ebi. ac. uk/ena/browser/view/GCA_902825105. 1) with accession numbers PRJEB36587 and PRJEB36588 for RNA-seq and Iso-Seq, respectively, and population genome data is available through ENA via accession number PRJEB59220.
Background: To date, no publicly accessible platform has captured and synthesized all of the layered dimensions of genotypic, phenotypic, and mechanistic information published in the field of inborn errors of immunity (IEIs). Such a platform would represent the extensive and complex landscape of IEIs and could increase the rate of diagnosis in patients with a suspected IEI, which remains unacceptably low. Objective: Our aim was to create an expertly curated, patientcentered, multidimensional IEI database that enables aggregation and sophisticated data interrogation and promotes involvement from diverse stakeholders across the community. Methods: The database structure was designed following a subject-centered model and written in Structured Query Language (SQL). The web application is written in Hypertext Preprocessor (PHP), Hypertext Markup Language (HTML), Cascading Style Sheets (CSS), and JavaScript.
All data stored in the Genetic Immunology Advisor (GenIA) are extracted by manually reviewing published research articles. Results: We completed data collection and curation for 24 pilot genes. Using these data, we have exemplified how GenIA can provide quick access to structured, longitudinal, more thorough, comprehensive, and up-to-date IEI knowledge than do currently existing databases, such as ClinGen, Human Phenotype Ontology (HPO), ClinVar, or Online Mendelian Inheritance in Man (OMIM), with which GenIA intends to dovetail. Conclusions: GenIA strives to accurately capture the extensive genetic, mechanistic, and phenotypic heterogeneity found across IEIs, as well as genetic paradigms and diagnostic pitfalls associated with individual genes and conditions. The IEI community's involvement will help promote GenIA as an enduring resource that supports and improves knowledge sharing, research, diagnosis, and care for patients with genetic immune disease.
This can be attributed to, among other factors, the difficulty in keeping up with the rapidly growing body of IEI knowledge (no current resource comprehensively catalogs its expanding genotype-phenotype relationships), along with important associated diagnostic paradigms and management details. Phenotype-driven databases such as Online Mendelian Inheritance in Man (OMIM [www. omim. org]) or the Human Phenotype Ontology (HPO)  2  sacrifice the granularity of allele-specific clinical and mechanistic data, whereas variant-and genefocused databases such as ClinVar  3  or UniProt (www. uniprot. org) sacrifice many phenotypic dimensions of data. Additionally, the nuances and variety of IEI clinical presentations are such that standard clinical coding systems such as the International Classification of Diseases, 10th revision (https://icd.
who. int/browse10/ 2019/en), or HPO  2  remain insufficient for conveying the full extent of phenotypic complexity, even at an individual patient level, much less at the level of cells or families. The availability of detailed immunophenotypic and functional data, in addition to standard clinical and genetic information, adds to the axes of Abbreviations used GOF: Gain of function HIES1: Hyper-IgE syndrome 1 HPO: Human Phenotype Ontology HSCT: Hematopoietic stem cell transplantation IEI: Inborn error of immunity IMAD1: Infantile-onset multisystem autoimmune disease 1 IUIS: International Union of Immunological Societies LOF: Loss of function OMIM: Online Mendelian Inheritance in Man data that must be coherently organized. Moreover, IEIs are often associated with unique genetic principles and assumptions that distinguish them from other mendelian conditions.
Diseasespecific diagnostic-and treatment-related paradigms and pitfalls are often buried in the literature or transmitted by word of mouth but are not widely available in a single searchable repository. Providers are currently faced with the time-intensive and challenging endeavor of gathering and synthesizing information about clinical, genetic, and mechanistic heterogeneity from across scattered resources. To address these challenges, we designed the Genetic Immunology Advisor (GenIA), a comprehensive, centralized, routinely updated, user-friendly catalog of IEI-focused variant-, gene-, patient-, and disease-specific information. GenIA uses a patient-centered model to link diverse data sets and provide structured, harmonized, and longitudinal information. This model facilitates a more precise, unbiased understanding of the natural history of each IEI without losing sight of each family's or individual's unique form of disease, helping to illuminate observations worthy of further investigation.
Database design Our database is structured around patients as the central node connecting all other data modules (Fig  1 ) and written using Structured Query Language. It is hosted in an Apache web server with MariaDB, version 10. 5. 18, installed. Different data sets and data types are currently distributed across 68 interconnected tables, with each of them representing an object or the relationship between objects. Constraints and keys were installed to ensure the compliance and uniqueness of records. We attempted to minimize free-text fields: most columns and attributes in the database tables are designed to hold predefined terms or numbers (integers or decimals) to maximize the query capabilities of the database for future analyses. Whenever possible, we use terms compatible with preexisting ontologies such as those of the Human Genome Organization (HUGO) Gene Nomenclature Committee (HGNC [www.
Web application GenIA can be publicly accessed via a user-friendly web interface (www. geniadb. net) that is programmed using the PHP (Hypertext Preprocessor), HTML (Hypertext Markup Language), CSS (Cascading Style Sheets), and some JQuery libraries from JavaScript for dynamic filtering of records in tables and forms. Authorized curators log into a separate password-protected portal and navigate a series of structured forms and pull-down menus guiding data entry. Unlike the rest of the web application, this interface was programmed using Bootstrap, version 5. 0. 2, which is a CSS framework.
Therefore, patients are generally registered chronologically according to the article in which they were first described. The inspection process (Fig 2 ) involves the following: (1) registering all index cases and family members to generate family pedigrees; (2) noting ages at study or genetic diagnosis and first manifestation if available; (3) assigning reported genotypes to proband and family members; (4) incorporating all phenotypic (clinical and laboratory) and functional data; and (5) storing the names of corresponding, first, and last authors of the inspected references. Disease entities are cross-checked with the Clinical Genomic Database (CGD [research. nhgri. nih. gov/CGD/]),  7  OMIM, PubMed, and International Union of Immunological Societies (IUIS) nosology. 8  Articles are classified as ''inspected'' if curation is considered complete or as ''inspection pending'' if additional information/ clarification is needed.
Variant annotation Before article inspection, all variants are aggregated from OMIM, ClinVar,  3  and original publications found via the aforementioned search algorithms. These variants are imported into the database and annotated using Ensembl's Variant Effect Predictor (VEP) software,  9  version 110. For each variant, curators craft a word-limited statement summarizing the variant's predicted pathogenicity classification with associated criteria used, including a detailed description of any functional assay(s) used, based on modified American College of Medical Genetics guidelines. Summaries are updated on a rolling basis whenever new relevant information is published.
Gene and disease descriptions Using the aforementioned algorithms, curators craft a wordlimited statement summarizing what is currently known about the function of individual genes from the published literature, UniProt, and the Human Protein Atlas. Gene dysfunction in human disease is summarized from the same sources, noting the broad classes of mechanisms and clinical phenotypes linked to each gene. Unique or difficult-to-detect genetic lesions, other known diagnostic challenges, and relevant available immune studies associated with a locus are also searched for and recorded. Each of the clinical phenotypes associated with an IEI gene is also individually assigned a descriptor and acronym, which are currently in the process of being harmonized with those in other databases. Each condition contains a more detailed summary of clinical and laboratory findings for that specific condition, data on all reported patients and families, and any available information on management.
Database and graphical user interface development GenIA was designed to focus on the subject as the central node connecting various data sets (Fig 1 ), in contrast to the variant-, gene-, or disease-centered model seen in other databases. 3, 10  GenIA's graphical user interface is organized into 8 main data modules (Table  I   3, 11, 12  ), 6 of which can be accessed directly from the home page (Fig  2 ) and serve as portals to an interconnected system through which one can reach the others. This modular structure facilitates scalability by allowing the addition of thousands of records per data set (eg, longitudinal instances), the incorporation of data items into existing objects (eg, new laboratory parameter), and the creation of additional modules, such as those on specific diagnosis and management options (currently under development).
GenIA currently contains 68 interconnected tables with more than 500,000 records. Any published subject-proband or affected/unaffected family member-is associated with multiple data types, including genetic data with familial variant segregation, clinical findings, laboratory measurements, and/or functional studies (Fig  1 ). Additional relevant parameters further strengthen these connections (eg, a genotype is enhanced by the assignment of zygosity and clinical relevance for that specific individual [ Fig 1]). In terms of clinical data, we record, whenever available, the age at first clinical manifestation, age at  presentation for each sign or symptom, age at genetic diagnosis or age at time of study, age at time of major therapeutic intervention such as hematopoietic stem cell transplantation (HSCT), and age at death.
Variant curation for probands and families All variants identified as disease-causing or disease-associated variants in a gene are first registered in the database, then annotated and classified as described in Methods, and ultimately associated with patients and publications. This enables us to accurately calculate the total number of reported individuals carrying each variant (Fig  4, A, B,  and D ). To avoid overestimation, we investigate and note any patients who are reported in the literature more than once. Of the 866 patients with molecular diagnoses recorded during the pilot gene curation process, we identified at least 189 (22%) who have been reported at least twice, with some found up to 4 or 5 times in different articles (Fig 4  , C ). Approximately 93% of all reported variants in the 24 pilot genes (288 of 309) are found in 5 or fewer patients each, but recurrent variants are seen more frequently in some genes (eg, STAT3, DIAPH1, FOXN1) (Fig  4, A, B , and D and see Table  E2  in the Online Repository at www.
DISCUSSION Among the challenges that we face in IEI research and clinical care is the extensive and expanding genotype-phenotype landscape with its wealth of associated information. Thus far, this has not been systematically cataloged in a single, easily curated and searchable database; therefore, we developed GenIA as a resource to tackle this challenge. The flexibly of GenIA's unique subject-centered model accommodates various data types and classes (Fig 1 ). For example, comprehensive genotype-phenotype data are not typically found in phenotype-driven catalogs, whereas in-depth information about zygosity, clinical relevance, phenotypes, mechanisms, and management is typically unavailable from large variant databases. GenIA integrates these and other forms of data to help providers draw valuable conclusions about a condition's clinical and genetic landscape, diagnostic and management challenges, transmission, penetrance, and expressivity.
The intention of creating GenIA is not to replace but rather to dovetail and harmonize with existing resources and endeavors, such as OMIM, Clin-Var, HPO, and ClinGen. 22  Some preexisting resources curate more multifaceted and detailed data but focus only on 1 or a few conditions (ie, CFTR2 [https://cftr2. org/], Infevers [https:// infevers. umai-montpellier. fr]). Although we acknowledge the challenges of extending this level of detail and completeness to a much larger group of disorders, we strongly believe that this can be accomplished-particularly with involvement from the IEI community. We are also investigating the possibility of increasing efficiency by adapting emerging machine learning and artificial intelligence strategies for use in some modules, but without losing the nuances of human biology or responsible scrutiny.
By achieving complete curation of 24 pilot IEI genes in less than 1 year, with each curator working on a volunteer basis, we have demonstrated how our current curation interface and protocol renders our goals for GenIA realistic and achievable. Indeed, while preparing this article, we completed curation of 4 of the 24 genes (ARPC5, DOCK11, LYN, and STAT6). We have used these pilot genes to demonstrate how the data stored in GenIA can be mined and synthesized to ask important clinical and biologic questions or identify specific concerns to be addressed. Incomplete and unequal data availability across articles is unavoidable, even for our pilot cohort (Fig 3  , E ). Not only are publications limited by content regulations but they may also lack patient-specific comprehensive examination or longitudinal follow-up data.
Moreover, some currently used assays may not yet have been developed at the time of study. Fortunately, we were able to extract detailed clinical data for the vast majority of genetically confirmed patients registered thus far, enabling us to generate fairly accurate calculations for the prevalence of clinical features associated with specific genes (Fig  6, A  and B ) and conditions (Fig 6  , C  and D ). The richness and quality of data in GenIA are useful to diverse stakeholders in the IEI community. Its integrated design facilitates detailed natural history studies of disease progression or investigations into novel therapeutics. GenIA can also serve as a powerful tool for those interested in elucidating genotype-phenotype correlations, conducting mechanistic studies, curating gene-disease relationships, developing variant classification guidelines, or performing diagnostic studies (Fig  2, A ).