dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This study was supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (Grant No. 692637  to D. S. ), the  Deutsche Forschungsgesellschaft (DFG)  (Grant  UE 200/1-1  to RU), and  ANCESMAG   ANR  project ( ANR-20-CE92-0050 ). We thank  Corinne Cruaud  and the  Genoscope (CEA, Commissariat à l'Energie Atomique et aux Energies Alternatives) at Evry (France)  for their essential contribution to the efficiency of the platform. The Galaxy server that was used for some calculations is in part funded by  Collaborative Research Centre 992 Medical Epigenetics (DFG  grant  SFB 992/1 2012 ) and  German Federal Ministry of Education and Research (BMBF  grants  031 A538A/A538C RBC, 031L0101B/031L0101C  de. NBIepi ,  031L0106 de.
Data availability statement Sequencing reads and the annotated complete genome of strain Rhodovastum atsumiense strain G2-11 (DSM 21279) wildtype was deposited to the European Nucleotide Archive database under the BioProject number PRJEB52102. RNA-seq reads and sequencing reads for the genomes of G2-11 mutants generated in this study were deposited to NCBI GenBank database under the BioProject number PRJNA818516.
Details of capture and sequencing of these cohorts (and those subsequently used in follow-up) are provided in the Methods and Supplementary Information. Calling and quality control (QC) of data from the two exome capture platforms were conducted in parallel (Table  1 , Extended Data Fig. 2  and Methods). Sensitivity to detect low-frequency coding variants was evaluated in each callset post-QC by comparison with passing sites in gnomAD v. 2. 1 that had 0. 0001 < non-Finnish European (NFE) minor allele frequency (MAF) < 0. 1 (Methods). We observed that 84% of all exonic SNPs in this frequency range were detected in both CD datasets with sufficiently high quality to enter meta-analysis. Analysis of each dataset was conducted in SAIGE using a logistic mixed-model  14  and a standard inverse-variance weighted (IVW) meta-analysis conducted across 164,149 nonsynonymous variants with MAF (gnomAD NFE) between 0.
Excluding synonymous variants in LD with these known associated nonsynonymous variants, synonymous variants showed little deviation from the null and none reached study-wide significance (Extended Data Fig. 3 ). Encouraged by this, we then nominated a list of 116 variants (including known variants) with P < 0. 0002 for further evaluation in three follow-up cohorts (Supplementary Table  2 ). Additional exome and genome sequencing was undertaken at the Sanger Institute on an independent cohort of 9,731 CD cases ascertained by the UK IBD Genetics Consortium and IBD BioResource. Genome sequencing with a target depth of 15× was performed on 6,000 patients with CD. Whole-genome sequences from 11,852 individuals from the INTERVAL blood donor cohort were used as population controls. Another 3,731 patients with CD were exome sequenced using the Agilent SureSelect Human All.
In addition to reiterating the central role of innate and adaptive immune cells as well as autophagy in CD pathogenesis, these newly associated genes highlight the emerging role of mesenchymal cells in the development and maintenance of intestinal inflammation. Exon V5 capture. In total, 33,704 individuals without IBD or other related diseases from the UK Biobank were used as controls for the Sanger whole-exome sequencing (WES) cases. These UK Biobank samples were sequenced by Regeneron using the IDT xGen Exome Research Panel v1. 0 (including supplemental probes), and thus QC and subsequent analyses were restricted to the intersection of the Agilent and the IDT capture regions. Exome and genome datasets were processed in parallel with similar QC parameters (Methods). Association analyses were performed using a logistic mixed-effects model implemented in the REGENIE v.
1. 0. 6. 7 and v. 2. 0. 2 software, correcting for the case-control imbalance using the Firth correction. Of 116 variants, 28 were associated (P < 4. 3 × 10 -4 (0. 05/116)) with CD in the meta-analysis of the two Sanger cohorts and 94 replicated the direction of effect seen in the discovery cohort (P = 3 × 10 -12 , binomial test). Summary statistics from a German dataset of 4,071 CD cases and 4,223 controls exome sequenced at Regeneron (Methods) were also ascertained and a meta-analysis was carried out across all five cohorts (Table  1 ). Of the 116 variants, 45 exceeded the study-wide significance threshold, P < 3 × 10 -7 (Supplementary Table  4  and Supplementary Data 1). Of note, all 45 study-wide significant sites in the discovery stage exome-wide scan showed stronger evidence of association in the meta-analysis, and none showed significant evidence of heterogeneity of effect across studies.
The 'scan' exome-wide and the combined meta-analyses have similar power to detect the same true associations at their respective significance thresholds (Extended Data Fig. 4 ). Among the 164,149 low-frequency nonsynonymous variants tested for association in this study, 14 were mapped to a credible set with posterior inclusion probability (PIP) > 5% from a previous IBD fine-mapping study  5. Eight of the 14 variants reached exome-wide significance (in NOD2, IL23R, TYK2, PTPN22 and CARD9). The remaining six variants (in GPR65, MST1, NOD2 and SMAD3) have genetic effects consistent with those previously reported, with P values ranging from 0. 025 to 8 × 10 -5 in the WES discovery cohort. Together, these results demonstrate the accuracy of our exome sequencing study in the lowest frequency range covered by previous GWAS approaches.
To identify new loci not yet implicated in CD and independent exonic association signals at known loci, we accounted for the LD between the 45 exome-wide significant variants and previously reported IBD GWAS hits, as well as previous rare-variant discoveries (Methods). We identified five coding variants in genes not previously implicated in IBD, even by their proximity to previous GWAS signals. We also discovered six independently associated novel exonic variants in genes previously known to harbor coding mutations underpinning CD or IBD risk, two of which are in NOD2 ('New locus' and 'New variant in known locus' in Fig. 1 , Supplementary Table  4  and Extended Data Fig. 5 ). Fourteen significant variants recaptured known IBD causal candidates from fine-mapping, including variants in CARD9, IL23R and NOD2, and the remaining 20 variants either tag the known causal variants through LD or have very small PIP from fine-mapping and thus are highly unlikely to be CD causal variants ('Known causal candidate' and 'Unlikely causal' in Fig.
1 , Supplementary Table  4  and Extended Data Fig. 5 ). A harmonized summary with findings from this study and the fine-mapping study  5  for genes implicated by the 45 exome-wide significant variants is available in Supplementary Table  5. Of note, evidence from earlier GWASs and this study cannot be considered independent and trivially combined since there is considerable sample overlap-while 46% of scan samples come from clinical sites or national cohorts not previously involved in the largest GWAS  4  , the remainder are from sites which also contributed earlier recruited samples (generally 10 or more years ago) to previously reported GWASs and have been exome sequenced in this study. Similarly, at least 10% of the Sanger whole-genome sequencing (WGS) samples used for meta-analysis were previously included in past genotyping studies.
None of the variants described in Table  2  has reached genome-wide significance in previously published GWASs (variant in HGFAC has almost reached significance in ref. 4  , P = 5 × 10 -8 for IBD). The nine new CD-associated variants all had an info score of 1 in the UK Biobank (UKBB) GWAS imputation  41  , except for PTAFR and PDLIM5, which had info scores of 0. 72 and 0. 9, respectively. Novel variants described in Table  2  together explain around 0. 12% of the variance on the liability scale (0. 3% on the observed scale). In comparison, the 25 independent coding variants that were included in the meta-analysis together explained 2. 1% of variance (5. 1% on the observed scale). We performed two gene-based rare-variant (MAF < 0. 001) burden tests in the full-exome Nextera and Twist datasets using SAIGE-GENE  42  , one restricted to loss-of-function variants and another using all nonsynonymous variants (Supplementary Table  7 ).
Sequencing was performed at a median depth of 85% targeted bases at >20×. Sequencing reads were mapped by BWA-MEM to the hg38 reference using a 'functional equivalence' pipeline. The mapped reads were then marked for duplicates, and base quality scores were recalibrated. They were then converted to CRAMs using Picard 2. 16. 0-SNAPSHOT and GATK 4. 0. 11. 0. The CRAMs were then further compressed using ref-blocking to generate gVCFs. These CRAMs and gVCFs were then used as inputs for joint calling. To perform joint calling, the single-sample gVCFs were hierarchically merged (separately for samples using Nextera and Twist exome capture). QC. QC analyses were conducted in Hail v. 0. 2. 47 (Extended Data Fig. 2 ). We first split multiallelic sites and coded genotypes with genotype quality (GQ) < 20 as missing.
Variants not annotated as frameshift, inframe deletion, inframe insertion, stop lost, stop gained, start lost, splice acceptor, splice donor, splice region, missense or synonymous were removed from the following analysis. We also removed variants that have known quality issues (have a nonempty QUAL column) in the gnomAD dataset. Sample QC: poor-quality samples that met the following criteria were identified and removed: (1) samples with an extremely large number of singletons (≥500); (2) samples with mean GQ < 40; and (3) samples with missingness rates > 10%. Variant QC: low-quality variants that met the following criteria were identified and removed: (1) variants with missingness rate > 5%; (2) variants with mean read depth (DP) < 10; (3) variants that failed the Hardy-Weinberg equilibrium test for controls with P < 1 × 10 -4 ; and (4) variants with >10% samples that were heterozygous and with an allelic balance ratio <0.
We kept samples that were classified as European with prediction probability greater than 80% (Extended Data Fig. 7 ). For Nextera samples, we used a second random forest classifier to assign EUR samples to AJ, LIT, FIN or NFE, and a third random forest classifier to clean the AJ/NFE split. Meta-analysis. We used METAL  87  with an IVW fixed-effect model to meta-analyze the SAIGE association statistics from Nextera and Twist samples (Table  1 ). The heterogeneity test was performed using Cochran's Q with one degree of freedom. Sanger Institute sequencing pipeline. Sample processing. Genome sequencing was performed at the Sanger Institute using the Illumina HiSeqX platform with a combination of PCR (n = 4,751, controls only) and PCR-free library preparation protocols. Sequencing was performed at a median depth of 18.
6×. Exome sequencing of cases was performed at the Sanger Institute using the Illumina NovaSeq 6000 and the Agilent SureSelect Human All Exon V5 capture set. Controls from the UK Biobank were sequenced separately as a part of the UKBB WES50K release using Illumina NovaSeq and the IDT xGen Exome Research Panel v1. 0 capture set (including supplemental probes). In total, 33,704 UKBB participants were selected for use as controls, excluding participants with recorded or self-reported CD, ulcerative colitis, unspecified noninfective gastroenteritis or colitis; any other immune-mediated disorders; or a history of being prescribed any drugs used to treat IBD. Exome and genome datasets were analyzed separately but followed a similar analysis protocol. Reads were mapped to hg38 reference using BWA-MEM v.
0. 7. 12 (WGS) and v. 0. 7. 17 (WES). Variant calls were performed using a GATK Best Practices-like pipeline (v. 4. 0. 10. 1 (WGS) and v. 4. 1. 8 (WES)); per-sample intermediate variant calling was followed by joint genotyping across the individual genome and exome cohorts. For the exome cohort, variant calling was limited to Agilent extended target regions. Per-region VCF shards were imported into the Hail software and combined. Multiallelic sites were split. For the exome cohort, we subsetted the calls to the intersection of Agilent and IDT exome captures, further excluding regions recommended for exclusion by the UKBB due to an error in read mapping that results in no variant calls made. Population assignment. We selected a set of ~14,000 well-genotyped common variants to identify the genetic ancestry of individual participants through the projection of 1000 Genomes Project cohort-derived PCs.
Equal amounts of sample were pooled before exome capture with a slightly modified version of IDT's xGen v1 probes; supplemental probes were added to capture regions of the genome well covered by a previous capture reagent (NimbleGen VCRome) but poorly covered by the standard xGen probes, the same as the probe library used in UK Biobank exome sequencing. These supplemental probes were included in QC but excluded in the final analysis as we only looked up variants that were in the standard exome captures and reached the nominal significance for replication (Extended Data Fig. 1 ). Captured fragments were bound to streptavidin-conjugated beads and nonspecific DNA fragments were removed by a series of stringent washes according to the manufacturer's recommended protocol (IDT). The captured DNA was PCR amplified and quantified by quantitative reverse transcription PCR (Kapa Biosystems).
Statistics and reproducibility. Previous studies show that a large sample size is needed for IBD genetic studies. We have thus included all samples available to us. We excluded samples of non-European ancestries due to their very limited sample size when properly matched between cases and controls (Extended Data Fig. 7 ). We also excluded data of poor quality from the analysis (Extended Data Fig. 2 ). These exclusions were necessary to ensure the quality of this study. All criteria were pre-established. We used the logistic mixed-model for the association analysis, followed by meta-analyses to combine multiple cohorts. We have multiple cohorts in the study that serve the purpose of replication. Two large cohorts done at the Broad Institute of different exome capture platforms were used to discover candidate variants.
Two independent cohorts done at Sanger and one Kiel/Regeneron cohort were used to replicate the findings (Extended Data Fig. 1 ). All reported findings have been replicated. No randomization was conducted. No blinding was carried out. Code and pipelines to reproduce our analysis are available on Zenodo  90. Cross-cohort meta-analysis. We used the Cochran-Mantel-Haenszel test to combine association summary statistics between the Broad Institute, Sanger Institute and Kiel/Regeneron cohorts.
We therefore classify S47L as 'tagging'. (3) The NOD2 A755V variant is in LD with rs184788345, the best PIP variant from fine-mapping (r 2 = 0. 85). The marginal P value for A755V is one order of magnitude less significant than rs184788345. Considering A755V is a missense variant while none of the variants in the credible set defined by rs184788345 is coding, we assign A755V as a likely 'Known causal candidate'. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article. Extended Data Fig. 4  | Power to detect single variant associations. We performed a series of power calculations using the methodology described by Johnson and Abecasis (2017). Our initial 'exome-wide scan' (two cohorts) had fewer samples and a more lenient significance threshold than subsequent meta-analysis (five cohorts).
acknowledgements We thank all of the principal investigators, local staff from individual cohorts and all of the patients who kindly donated samples used in the study for making possible this global collaboration and resource to advance IBD genetics research. This research was funded in whole, or in part, by the  US National Institutes of Health  grants no. U54HG003067  and no. 5UM1HG008895 , the  Wellcome Trust  grants no. 206194  and no. 108413/A/15/D , and  The Leona M. & Harry B. Helmsley Charitable Trust  grant no. 2015PG-IBD001. We thank the  Broad Institute Genomics Platform  for genomic data generation efforts and the  Stanley Center for Psychiatric Research at the Broad Institute  for supporting control sample aggregation. M. A. R. is in part supported by the  NHGRI of the NIH  under award no.
Data availability We describe all datasets in the manuscript or its Supplementary Information. Genome Reference Consortium Human Build 38 can be accessed at https:// www. ncbi. nlm. nih. gov/assembly/GCF_000001405. 40/. Sequence data used in this study have been made publicly available in dbGaP Study Accession: phs001642. v1. p1, Center for Common Disease Genomics (CCDG), Autoimmune: Inflammatory Bowel Disease (IBD) Exomes and Genomes (https://www. ncbi. nlm. nih. gov/projects/gap/cgi-bin/study. cgi?study_id=phs001642. v1. p1). The summary statistics of Nextera and Twist meta-analysis have been deposited on GitHub (https://github. com/iibdgc/Crohn-s-Disease-WES-meta) (https://doi. org/10. 5281/zenodo. 6564928). This research has been conducted using the UK Biobank Resource and controls made publicly available by dbGaP (phs001000.
7 × 10 -7 and < 10 -16 , respectively). Only one other gene in either analysis exceeded the threshold expected once in the study by chance (ATG4C, NonSyn P = 3. 3 × 10 -6 ). This potentially novel signal in ATG4C was driven by three distinct missense variants with individual P < 0. 01 (N75S, R80H and C367Y) (Supplementary Table8) along with two others with P < 0. 05 (K371R, R389X). The ATG4C gene burden signal was examined in the Sanger datasets and replicated, with the meta-analysis reaching exome-wide significance (P = 1. 5 × 10 -7 ) driven by several of the same variants. Further examination of results from the single-variant tests Box 1 |.
the Brain Imaging Data Structure (BIDS) is a standard for organizing and describing neuroimaging datasets, serving not only to facilitate the process of data sharing and aggregation, but also to simplify the application and development of new methods and software for working with neuroimaging data. Here, we present an extension of BIDS to include positron emission tomography (PEt) data, also known as PEt-BIDS, and share several open-access datasets curated following PEt-BIDS along with tools for conversion, validation and analysis of PEt-BIDS datasets.
Recently in 2020, similar to the Organization for Human Brain Mapping recommendations from the Committee on Best Practices in Data Analysis and Sharing (COBIDAS) for MRI and MEEG, consensus guidelines for describing the content and format of PET brain data in publications and archives has emerged  8. The Brain Imaging Data Structure (BIDS) standard was originally developed for MRI as a community standard for organizing and sharing brain imaging study data within and between laboratories 9. While the focus was originally targeting MRI, and especially structural and functional MRI (fMRI), BIDS has since rapidly expanded to include many different imaging modalities, including magnetoencephalography (MEG)  10  , electroencephalography (EEG)  11  , intracranial electroencephalography (iEEG)  12  , and linking brain imaging to genetics  13.
BIDS mainly addresses the heterogeneity of data organization by following the FAIR principles (findability, accessibility, interoperability, and reusability)  14. Findability and reusability are addressed in BIDS by providing rich metadata in dedicated sidecar files. Interoperability is addressed by using the existing Neuroimaging Informatics Technology Initiative (NIfTI) format for storing brain imaging data, text files arranged according to the JavaScript Object Notation (JSON) format for complementary metadata, and Tab Separated Value (TSV) format for tabular data. While accessibility is not directly addressed within the BIDS standard itself, the existence of such a standard facilitates the development of public data repositories. The largest of these repositories, OpenNeuro (https://openneuro.
org), is already built around the BIDS standard, and a new repository Fig. 1  Overview of a common PET experiment. This example includes blood measurements and is defined on a common time scale. Note, "time zero" can either be defined as time of injection or scan start, and all the PET and blood data should be decay-corrected to this time point. fully dedicated to PET scans (OpenNeuroPET) is currently under development (https://openneuro. org/pet). BIDS also fosters interoperability and reuse of already acquired data by defining how to structure and store data using naming conventions and dedicated metadata files. Because BIDS data follow a common data structure and description, the proliferation of BIDS datasets incentivizes the creation of analysis pipelines that target this structure, the adoption of which promotes verifiable and reproducible research practices.
In this work, we present the main features of the extension of BIDS to include PET data (PET-BIDS). This extension largely builds upon the original BIDS specification  9  , and the guidelines for the content and format of brain PET data in publications and archives  8. The full documentation of the PET-BIDS extension can be found in the general BIDS specification (https://bids-specification. readthedocs. io/en/stable/).
PEt-BIDS Summary The extension of BIDS to include PET data largely aligns with the BIDS specification for other imaging modalities, describing a way for organizing PET data and specifying metadata for PET experiments. A detailed overview of the directory structure is given in Fig. 2. Each subject's data corresponds to a directory which contains subdirectories for each acquisition session (e. g. , baseline, rescan or intervention) and imaging modality (e. g. , pet). The subdirectories are also accompanied by a dataset_description. json file containing generic information about the dataset, providing full credit to the authors sharing the data. Furthermore, a free form text file (README) describing the dataset in more detail such as issues with the data (e. g. excessive head movement or a lesion in the brain for a given subject or scan) should be provided (Fig.
2 ). Within each subject directory, the /pet subdirectory contains the PET imaging data and the corresponding metadata. PET imaging data are stored in 4D (or 3D if only one volume was acquired) NIfTI files with a _pet suffix. When acquiring several volumes (frames in PET terminology) these should be stored in 4D in chronological order (the order they were acquired in). The imaging data are accompanied by a _pet. json sidecar file which details the metadata of the PET acquisition. All the metadata are in accordance with the guidelines for the content and format of brain PET data  8. The file naming structure for PET data closely follows the general BIDS guidelines  9  , using specified key-value pairs joined by hyphens and separated by underscores. Multiple sessions (typically visits) are encoded by adding an extra layer of directories in the form of ses-<label>.
2 ). Blood can be sampled by an autosampler, for continuous monitoring of whole blood radioactivity, and/or manually drawn for discrete blood samples. Therefore, the recording key recording-<label> for blood data has two reserved values: (1) autosampler, and (2) manual. The blood metadata should be stored in a JSON sidecar file with a recording-<label> and a _blood suffix, containing information about what blood data are available (e. g. radioactivity in plasma and/ or whole blood and parent compound). The blood JSON sidecar file should be accompanied by a tabular TSV file with similar naming convention, containing all the values of the available blood data. All blood data should be reported according to a unique reference time-scale in relation to a predefined time zero defined by the PET data (Fig.