dataesr/scientific-paragraphs-categorization huggingface dataset:
Overlap of variants and CpG sites with activity-by-contact (ABC) enhancer region ABC enhancer-gene connection data for 131 cell types and tissues with ABC-scores ≥0. 015  35  was downloaded (Data Availability). The positions of candidate non-coding SNPs, lead SNPs that have co-localized eQTL, and CpG sites that have co-localized meQTL were queried in the ABC data to determine whether the SNP and/or the CpG site was within an ABC enhancer region.
Gene prioritization for gout inflammation To select the input list of candidate genes, association signal boundaries from all of the loci lists were used to select genes from GENCODE, and co-localized cis-/trans-eQTL genes not already in the list were added. Satisfying each of the following seven criteria scored the gene one point in the primary prioritization scheme: 1) having an eQTL co-localized with GWAS signal in whole blood; 2) having an eQTL in monocytes (ImmuNexUT 62 or OneK1K 152 datasets); 3) within a locus with a co-localized meQTL; 4) within a locus with gout association signal that also genetically associates with one or more of 36 white blood cell traits  78  (Table  S36  and Supplementary Notes); 5) differentially expressed in gout (Supplementary Notes); 6) expressed in GTEx whole blood tissue (Supplementary Notes); and 7) differentially expressed in monocytes stimulated with MSU crystals and/or lipopolysaccharide (LPS) (LPS vs MSU crystal, LPS vs phosphate-buffered saline control, MSU crystal vs PBS control)  43.
Given that some of the categories used results that were derived using European ancestry and/or trans-ancestry data and could not be applied to all ancestral groups, the prioritisation scores were standardized based on the number of categories to which the gene was applied (depending from which ancestral group the gene was implicated) (Supplementary Notes). Following this, within each standardized score category, genes were secondarily ranked according to: 1) one of 47 genes containing a strong candidate missense causal variant (Table  1 ); 2) one of the 385 target genes of the activity-bycontact enhancer; 3) gene with FANTOM5 transcription start site (TSS) closest to the lead SNP. Given that not all genes had TSS information from the FANTOM5 153 dataset, scores for repositionable drugs targeting the gene set.
S25  as either a lead variant (including from conditional COJO analysis) or in very strong linkage disequilibrium (r 2 ≥0. 98) with a lead variant (including from conditional COJO analysis). 1, in European, unless otherwise stated; 2, risk allele in European, unless otherwise stated. First section, genes with candidate causal missense variants with information available on impact of the variant on protein function and phenotype; Second section, the remaining genes. Microtubule-associated serine/threonineprotein kinase gene-3. Implicated in TLR4 signalling and inflammatory            Figure  4 : Functional and pathway enrichment analyses of gout candidate genes. The DAVID database was used to identify GO Biological Function term, KEGG and REACTOME pathways enriched in the gout GWAS dataset.
Acknowledgements We would like to thank the research participants and employees of  23andMe  for making this work possible. The following members of the 23andMe Research Team contributed to this study:  Stella Aslibekyan ,  Adam Auton ,  Elizabeth Babalola ,  Robert K. Bell ,  Jessica Bielenberg ,  Katarzyna Bryc ,  Emily Bullis ,  Daniella Coker ,  Gabriel Cuellar Partida ,  Devika Dhamija ,  Sayantan Das ,  Sarah L. Elson ,  Nicholas Eriksson ,  Teresa Filshtein ,  Alison Fitch ,  Kipper Fletez-Brant ,  Pierre Fontanillas ,  Will Freyman ,  Julie M. Granka ,  Karl Heilbron ,  Alejandro Hernandez ,  Barry Hicks ,  David A. Hinds ,  Ethan M. Jewett ,  Yunxuan Jiang ,  Katelyn Kukar ,  Alan Kwong ,  Keng-Han Lin ,  Bianca A. Llamas ,  Maya Lowe ,  Jey C. McCreight ,  Matthew H. McIntyre ,  Steven J.
Generation Scotland received core support from the  Chief Scientist Office of the Scottish Government Health Directorates  [ CZD/16/6 ] and the  Scottish Funding Council  [ HR03006 ] and is currently supported by the  Wellcome Trust  [ 216767/Z/19/Z ]. Genotyping of the GS:SFHS samples was carried out by the  Genetics Core Laboratory at the Edinburgh Clinical Research Facility, University of Edinburgh, Scotland  and was funded by the  Medical Research Council UK , the  Wellcome Trust  ( Wellcome Trust   Strategic Award  " STratifying Resilience and Depression Longitudinally " ( STRADL ) Reference  104036/Z/14/Z ) and by  MRC University Unit  grant  MC_UU_00007/10  ( QTL in Health and Disease programme ).
Data availability The full GWAS summary statistics for the 23andMe discovery data set will be made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. Datasets  will be made available at no cost for academic use (https://research. 23andme. com/collaborate/#dataset-access/); 1000 Genomes Project Phase 3 data was downloaded from http://ftp. 1000genomes. ebi. ac. uk/vol1/ftp/; information on UK Biobank cohort can be viewed at https://www. ukbiobank. ac. uk/; GWAS Catalog (https://www. ebi. ac. uk/gwas/); GTEx data were downloaded from https://gtexportal. org/home/datasets; variant information from dbSNP was downloaded from https://ftp. ncbi. nih. gov/snp/latest_release/VCF/GCF_000001405. 25. gz; CUPs used for SLALOM were downloaded from https://github.
com/cathaloruaidh/genomeBuildConversion; GoDMC methylation QTL data were downloaded from http://mqtldb. godmc. org. uk/downloads; RELI transcription factor ChIP-seq data https://tf. cchmc. org/external/RELI/RELI_public_data. tar. bz2; baseline LD score v1. 1, celltype specific, and cell-type group annotations were downloaded from https://alkesgroup. broadinstitute. org/LDSCORE/; functional annotations for PAINTOR were downloaded from https://ucla. box. com/s/x47apvgv51au1rlmuat8m4zdjhcniv2d; LD score regression summary statistics of UK Biobank traits were downloaded from https://nealelab. github. io/UKBB_ldsc/downloads. html; ABC enhancer-gene connection data were downloaded from ftp://ftp. broadinstitute. org/outgoing/lincRNA/ABC/AllPredictions. AvgHiC. ABC0. 015. minus 150. ForABCPaperV3.
txt. gz); FATHMM scores for non-coding variants were obtained from http://fathmm. biocompute. org. uk/; ImmuNexUT eQTL data were downloaded from https://humandbs. biosciencedbc. jp/en/hum0214-v6; OneK1K eQTL data were downloaded from https://onek1k. s3. ap-southeast-2. amazonaws. com/onek1k_eqtl_dataset. zip; Henry et al. 155 prostate single cell RNA-seq FASTQ data were downloaded from https://www. gudmap. org/chaise/record/#2/RNASeq:Study/RID=W-RAHW; Crowley et al. 154 prostate single cell RNA-seq count matrices were downloaded from GEO (GSE150692); CHIP summary statistics were downloaded from https://ftp. ebi. ac. uk/pub/databases/gwas/summary_statistics/GCST90102001-GCST90103000/; Susztak Kidney Biobank (https://susztaklab. com/); a list of differentially expressed genes in stimulated monocytes can be obtained from Table S2 in the original paper 43 ; GWAS data for white blood cell traits used in gene prioritization analysis (Table S30) (https://www.
One key cohort was the UK Biobank 124 that was also used for genetic risk prediction, genetic correlation analysis with other phenotypes, Mendelian randomization and fine-mapping. 23andMe cohort participants  125  provided informed consent and participated in the research online, under a protocol approved by the external AAHRPP-accredited IRB, Ethical & Independent Review Services (E&I Review). Participants were included in the analysis on the basis of consent status as checked at the time data analyses were initiated. Ethical & Independent Review Services was recently acquired, and its new name as of July 2022 is Salus IRB (https://www. versiticlinicaltrials. org/salusirb).
A SNP was considered significantly heterogeneous between sexes if the heterogeneity Q-statistic had a P < 1×10 -6. Supplementary Notes. To identify additional associations, the European GWAS loci were analyzed with GCTA-COJO 140 using the UK Biobank cohort as a reference panel (Supplementary Notes). Due to a lack of adequately sized reference panels for the other ancestries, we did not conduct conditional analysis on the remaining GWAS datasets.
, 2019). In contrast, P. rapae uses mostly cultivated Brassicaceae. In Japan, plant communities vary substantially along a north-south cline  (Kubota et al. , 2015). Because this holds true also for the latitudinal distribution of wild Brassicaceae, we expected that the plant community cline should affect host usage patterns of P. melete and P. napi more strongly than of P. rapae feeding on cultivated plants. We sampled local populations of these three Pieris species across Japan and acquired data on host plant communities at the sampling sites. We sequenced NSP and MA and used restriction-site associated DNA sequencing (RAD-seq) data for comparison, to distinguish between the potential impact of selection and neutral genetic processes  (Bernatchez & Landry, 2003; Dionne et al. , 2007). Using those approaches we expected to identify microevolutionary patterns of NSP or MA potentially correlating with local host plant usage among Pieris species strongly associated with wild Brassicaceae plants.
| Selection of sampling sites To determine suitable sampling sites that cover different host plant communities, we estimated the diversity of Brassicaceae across Japan with maxent version 3. 4. 1  (Phillips et al. , 2004 (Phillips et al. , , 2017)). We used 11,325 individual location data of 44 genera of the Brassicaceae in the Global Biodiversity Information Facility (GBIF. org; https://doi. org/10. 15468/ dl. a2nqtv) and S-Net (http://scien ce-net. kahaku. go. jp), and 19 climate variables for Japan at 2. 5 arc-minute resolution from WorldClim  (Fick & Hijmans, 2017)  to infer additional potential locations of genera from the Brassicaceae (Table  S1 ). We calculated the Shannon index for genera-based diversity of Brassicaceae using the r package "vegan"  (Oksanen et al. , 2017). Based on this estimation, we chose 11 sampling sites across Japan to cover a gradient of local host plant diversities for each butterfly species (Table  S2 ).
| RFLP-based identification of Pieris butterfly species To determine the identity of the sampled larvae, we used restriction fragment length polymorphisms (RFLPs) in the mitochondrial ND5 gene (GenBank accession nos. : LC090587-LC090590). P. melete ND5 has a HincII site, and P. napi ND5 has a HinfI and a HindIII site, whereas P. rapae ND5 lacks these restriction sites. We amplified ND5 directly from larval bodies, using mightyamp dna polymerase Version 3 (TaKaRa) and ND5 universal primers (Table  S3 ), followed by restriction and separation on 2% agarose gels. To verify that our species assignments worked correctly, we used the same RFLP technique for adult males previously classified as P. melete (N = 24), P. napi (N = 16) or P. rapae (N = 24) based on differences in the shape of the androconium.
| Population and evolutionary genetic analyses For population structure analyses we used SNPs called across the three species and performed sparse non-negative matrix factorization (SNMF) with the "snmf" function (K = 1-10, repetition = 20, iterations = 200) implemented in the r package "LEA"  (Frichot & François, 2015; Frichot et al. , 2014). We used the cross-entropy criterion to determine the number of ancestral populations (K). We calculated species-wide distributions of Tajima's D  (Tajima, 1989)  of NSP and MA using the poly-div_sfs. pl script (https://github. com/ santi agosn chez/poly-div_sfs), iterating random sampling of one individual per population and calculation of D for 300 replicates. We performed these analyses on the entire coding sequence as well as on individual exons. To calculate genome-wide Tajima's D at the species level, we used vcftools  (Danecek et al.
, 2011). We performed the same random sampling of individuals and selected 30 random SNPs, which corresponded to the average number of SNPs in NSP within populations. We used the poly-div_sfs. pl script to calculate the allele frequency of NSP and MA for each species. At the population level, we used dnasp version 6  (Rozas et al. , 2017)  to compute π and F ST of NSP and MA, and arlequin version 3. 5  (Excoffier & Lischer, 2010)  to compute Tajima's D. We estimated F ST -like amino acid divergence in NSP and MA between populations as follows; F ST _AA = {Dij -mean (D within population)}/Dij, where Dij corresponds to the mean pdistance in amino acid sequences between population i and j. We used the online tool at mkt. uab. es/mkt/MKT. asp (Egea et al. , 2008)  to conduct McDonald-Kreitman tests  (McDonald & Kreitman, 1991).
| Comparison of NSP and MA with host plant diversity We used linear regression to test, for each Pieris species, whether diversity of NSP or MA correlated with host plant diversity across sampling sites, using measured host plant diversity as the explanatory variable, and NSP or MA amino acid diversity, or genome-wide genetic diversity estimated from RAD-seq data as response variables. Furthermore, we performed a partial Mantel test  (Mantel, 1967; Smouse et al. , 1986; Sokal, 1979)  to test whether NSP or MA amino acid sequence divergence between Pieris populations correlated with host plant community dissimilarities, using the "vegan" package in r  (Oksanen et al. , 2017). We used the partial Mantel test to compare F ST -like amino acid sequence distances of NSP or MA with the Bray-Curtis index for host plant dissimilarity, after controlling for genetic differences between populations (F ST ) using RAD-seq data.
We permuted rows and columns of the first dissimilarity matrix 100,000 times to evaluate the reliability of the partial Mantel test results. The maxent presence-probability estimation predicted that diversity in Brassicaceae communities should be higher in central Japan than in northern and southern regions (Figure  1a ). We chose 11 sites along this predicted cline of Brassicaceae diversity: Yubari, Rusutsu and Oshamanbe (all on the island of Hokkaido), Fukushima, Nagano, Chiba, Kanagawa and Nara (all on the island of Honshu), Tokushima (on the island of Shikoku), Miyazaki (on the island of Kyushu), and the island Yonaguni (from north to south; Figure  1a ). In total, we collected data on 4777 individual plants across 14 genera and 25 species of the Brassicaceae (Table  S2 ). We used these data to estimate the Shannon index for the diversity of Brassicaceae communities at each site.
Capture results: multi-virus panels We started by performing capture of all samples with the three multi-virus panels from two different vendors: the Twist Pan-Viral Panel and the Illumina Respiratory Virus Panels. The Twist panel is composed of probes that target 1,160 targets, corresponding to more than 1,000 human pathogenic viruses. The Illumina panel is composed of 177 targets, corresponding to 83 respiratory viruses and 94 human genes. A list of the accession numbers and gene names is provided in the github repository (https://github. com/biomics-pasteur-fr/manuscript_capture_seq/). As we were performing this benchmark, Illumina released version 2 of its respiratory virus panel, which showed better overall results than the previous version (Figure  S1  A-B). The results described in this paper correspond to those generated using version 2 of the Illumina panel.
All data was mapped to the Wuhan SARS-CoV-2 sequence (accession MN908947. 3). These results suggest that although the viral sequence was captured, it was not captured entirely. It is possible that the e ciency of the probes to speci cally capture the SARS-CoV-2 reads was lower due to the size of the panel (i. e. , number of probes covering the entire genome). It is also possible that we lost SARS-CoV-2 information from our samples due to degradation or the library preparation. We performed taxonomic analysis of all samples to understand why we obtained such a small percentage of reads corresponding to our target. The main objective was to classify the reads not mapping to the SARS-CoV-2 reference sequence. With the exception of samples 4544 and 4688, we obtained between 65% and 97% of reads with hits for the human database (Figure  5A ).
Taxonomic analysis showed between 5 and 69% of the reads mapping to the human database (control genes captured by the panel excluded) (Figure  5B ). Again, most of the reads of samples 4544 and 4688 were identi ed as Primate Bocaparvovirus 1 and Human orthopneumovirus, respectively. Both viruses are also present in the Illumina panel, con rming the results of the Twist Pan-Viral capture. In addition to Human Orthopneumovirus, we detected SARS-CoV-2 reads from sample 4688, covering 26% of the entire viral genome. Using this panel, we also found bacteria in a number of the samples, with the same phyla detected as during the sequencing of the Twist Pan-Viral captured samples.
Indeed, 95% of the sample 4544 data matched the human database, and 75% for sample 4688. However, we detected SARS-CoV-2 reads from sample 4688, covering 27% of the entire viral genome, con rming the presence of the virus in this sample. These results suggests high speci city of the Twist SARS-CoV-2 probes to capture the virus. Even though these two samples contained an abundance of other viruses, these probes managed not only to avoid capturing them but also to speci cally capture the SARS-CoV-2 reads present in sample 4688. We also observed the presence of bacteria in all samples, between 0. 01% and 64%. As shown previously, samples with a high percentage of bacteria did not have a predominant phylum or species, but a mixture of dozens of different species. Arbor SARS-CoV-2 Panel Holmes et al.
[ 12 ] demonstrated the advantages of double capture when the target genome within samples is scarce. We performed a double capture of our samples using this panel, as recommended by the manufacturer. Globally, this panel showed the best percentage of on-target reads of all panels tested, reaching up to 94% (Figure  4B ). For almost all samples in which the virus was detected, the percentage of on-target reads was higher using the Arbor panel than the Twist panel. Moreover, the BOC results were very similar to those of the Twist SARS-CoV-2 Panel. Taxonomic analysis showed between 0. 2% and 98% reads matching the human database (Figure  5D ). Although the percentage of reads classi ed as human was equivalent to that for the Twist SARS-CoV-2 capture panel, there was a signi cant drop in the percentage of reads classi ed as bacterial, with a maximum of 7% of reads matching the bacteria database.
ACKNOWLEDGMENTS We thank the  Biomics Platform ,  C2RT ,  Institut Pasteur, Paris, France , supported by  France Génomique  ( ANR-10-INBS-09 ), IBISA, and the  Illumina  COVID-19 Project. This work was supported by the " URGENCE COVID-19 " fundraising campaign of the  Institut Pasteur. A special thanks also to the other members of  Biomics  who allowed the project to continue during lockdown:  L. Ma ,  G. Haustant ,  Z. Allouche ,  V. Briolat ,  I. Najjar ,  L. Motreff , and  A. Etienne.
Amplicon sequencing The microbiota community was analyzed in all fecal samples at 6, 12, 18, and 24 months. Illumina MiSeq sequencing analysis targeting V3-V4 hypervariable 16S rRNA subunit was carried out at Genotoul (Toulouse, France) using specific forward F340 (5 ′ -CCTACGGRAGGCAGCAG-3 ′ ) and reverse primer R805 (5 ′ -GGACTACHVGGGTWTCTAAT-3 ′ ). Thermocycling was performed with an initial step at 94 • C for 60 s, followed by 30 cycles of denaturation at 94 • C for 60 s, annealing at 65 • C for 60 s, and elongation at 72 • C for 60 s, with a final elongation of 10 min at 72 • C. The raw dataset containing pairended reads with corresponding quality scores was merged and trimmed using settings as previously mentioned  (28). Quantitative Insight Into Microbial Ecology (QIIME) opensource software package (1.
Sequencing data analysis Unweighted, generalized, and weighted UniFrac distance metrics were calculated from subsampled OTU tables (10,000 reads/sample) and visualized with PCA plots. Significant differences were tested using Permutational Multivariate Analysis of Variance using Distance Matrices, "adonis" function from R package "vegan. " Alpha diversity measures expressed with an observed species (sequence similarity 97% OTUs) value were computed for rarefied OTU tables (10,000 reads/sample) using the alpha rarefaction workflow. Differences in alpha diversity were determined using a t-test-based approach employing the non-parametric (Monte Carlo) method (999 permutations) implemented in the compare alpha diversity workflow. ANCOM (Analysis of Composition of Microbes) analysis was performed to detect significantly different taxa over time  (30).
Statistical analysis For cultural, qPCR, and SCFA data, statistical analysis was done using IBM SPSS Statistics 20. 0 (IBM SPSS Inc, Chicago, IL, USA). The normality of each dataset was assessed using Shapiro-Wilk tests. Cultural and qPCR data above the detection limit were log 10 transformed. For qPCR data used for correlation and comparison analysis, default values of half the detection limit (Supplementary Table  2 ) were assigned for values below the threshold of the qPCR (see above). The tests revealed non-normal distribution, cultural, qPCR, and SCFA data were expressed as a median. The differences in median composition and metabolic changes across time points were assessed using non-parametric multiple comparisons with the Kruskal Wallis test. Changes in microbial levels enumerated by cultural and qPCR, and metabolite concentration over time were assessed using linear regression.
A common data set from both instruments was created for this campaign. Details of these measurements can be found in  Brownwood et al. (2021). These instruments also measured NO 2 in the sampled air in a separate mode or second measurement channel. In addition, NO 2 concentrations were measured by another custom-built cavity ring-down instrument  (Liebmann et al. , 2018)  and a commercial chemiluminescence instrument combined with a blue-light converter (Eco-Physics). NO 2 concentration measurements from all instruments were combined to one common, quality-checked data set  (Brownwood et al. , 2021). Ozone concentrations were measured by a commercial instrument using UV-absorption (Ansyco). NO 3 and N 2 O 5 concentrations were measured with 2 custom-built instruments applying cavity-ring-down spectroscopy  (Wagner et al.
, 2011; Sobanski et al. , 2016). NO 3 was detected at 662 nm and the sum of NO 3 and N 2 O 5 in a second channel, in which the inlet and cavity is heated to thermally decompose N 2 O 5. Measurements were also combined to one data set taking also into account that NO 3 and N 2 O 5 can be expected to be in a thermal equilibrium for conditions of the experiments in this work. https://doi. org/10. 5194/egusphere-2022-587 Preprint. Discussion started: 13 July 2022 c Author(s) 2022. CC BY 4. 0 License. HO 2 , OH and RO 2 radical concentrations were determined by a laser-induced fluorescence instrument  (Fuchs et al. , 2011 (Fuchs et al. , , 2012;; Cho et al. , 2021). OH radicals are excited at 308 nm in a low-pressure cell and their fluorescence is measured by gated single-photon counting. The fluorescence cell for the detection of only OH radicals was equipped with a chemical modulation reactor (CMR), which allows to account for potential interferences in the measurements  (Cho et al.
Data availability. Data from the experiments in the SAPHIR chamber used in this work are available on the EUROCHAMP database webpage (https://data. eurochamp. org). Experiment on 09 August 2018: Fuchs et al. (2018a), experiment on 10 August 2018: Fuchs et al. (2018b), experiment on 12 August 2018: Fuchs et al. (2018c), experiment on 13 August 2018: Fuchs et al. (2018d).