dataesr/scientific-paragraphs-categorization huggingface dataset:
Population genetics We compiled whole genome sequencing data from multiple geographic samples collected in Africa, Europe, North America, Asia and Australia to investigate worldwide allele frequency patterns of the eyeless SNP at position Chr 4: 710326 in natural populations. This dataset consisted of single individual sequencing data  (Campo et al. , 2013; Grenier et al. , 2015; Lack et al. , 2015; Lack et al. , 2016; Langley et al. , 2012; Pool et al. , 2012)  and Pool-Seq data from various sources  (Bastide et al. , 2013; Bergland et al. , 2014; Kapun et al. , 2016; Orozco-terWengel et al. , 2012; Reinhardt et al. , 2014)  (Table  S2 ). For single individuals, we obtained genotypes of the focal SNP from the Drosophila Genome Nexus website (DGN; http://www. johnpool. net/genomes. html) and estimated allele frequencies based on the number of chromosomes carrying the A-variant for populations with at least ten sequenced individuals.
For Pool-Seq datasets, we re-mapped quality-filtered raw data as described in  (Kapun et al. , 2016)  and estimated allele frequencies based on read counts of the A-variant relative to the total coverage. To increase sequence coverage in Queensland and Tasmania, we merged libraries of multiple collections at the corresponding locations  (Reinhardt et al. , 2014). We further used a π and Tajima's D for all 48 samples from the DrosEU dataset using Pool-Gen  (Kapun et al. , 2018)  with implemented corrections for Pool-Seq data  (Futschik and Schlotterer, 2010; Kofler et al. , 2011). At last, we tested whether very rare occurrences of the A-variant in Sub-Saharan Africa may be due to admixture with non-African genetic variation. We therefore used admixture probability estimates from  (Lack et al.
collection of Pool-Seq data from 48 population samples collected across Europe by the DrosEU consortium (Kapun et al. , 2018)  (accession number: PRJNA388788) for an in-depth analysis of spatial distribution of the A-variant. Specifically, we tested for clinal distribution along the latitudinal and longitudinal axes by means of generalized linear models (GLMs) in R based on allele counts to account for the biallelic nature of the focal SNP. We further contrasted the clinality of 4: 710,326 to 21,008 putatively neutral genome-wide SNPs located in short introns (<60bp) and in distance to chromosomal inversions (Clemente and Vogl, 2012; Parsch et al. , 2010). To test if the observed p-value from a GLM at the focal SNP deviates from neutral expectation we empirically assessed significance. We therefore generated empirical cumulative density functions (ECDF) based on the -log10 transformed p-values of all neutral SNPs and calculated the area under the ECDF confined by the -log10 p-value of the focal SNP and the upper tail of the distribution by integration.
performed using Graphpad Prism 7 (Graphpad software Inc. ). Details on the tests, sample sizes and p values are indicated in figure legends except where mentioned otherwise. Zhu, J. , Palliyil, S. , Ran, C. , and Kumar, J. P. (2017). Drosophila Pax6 promotes development of the entire eye-antennal disc, thereby ensuring proper adult head formation. Proceedings of the National Academy of Sciences of the United States of America 114, 5846-5853. Zhu, L. J. , Christensen, R. G. , Kazemian, M. , Hull, C. J. , Enuameh, M. S. , Basciotta, M. D. ,Brasefield, J. A. , Zhu, C. , Asriyan, Y. , Lapointe, D. S. , et al. (2011). FlyFactorSurvey: a database of Drosophila transcription factor binding specificities determined using the bacterial one-hybrid system. Nucleic acids research 39, D111-117.
ACKNOWLEDGMENTS This work was funded by  VIB (B. A. H) , the  Belspo WiBrain Interuniversity Attraction Pole network and Fonds Wetenschappelijke Onderzoeks (FWO)  grant  G. 0503. 12  (B. A. H. ). Work in B. A. H lab is currently funded by the  Institut Hospitalier Universitaire (IHU)  and the  Institut du Cerveau et de la Moëlle Epinière (ICM), Paris, France. S. W. was a fellow of the  FLiACT Marie Curie ITN  ( FP7, EU ). M. K. and T. F. were supported by a  Swiss National Science Foundation (SNSF)  grant ( PP00P3_133641 ) to T. F. We thank  D. Pottier ,  S. Villain ,  R. Ejsmont ,  N. Mora ,  A. Soldano ,  N. Gompel ,  N. Posnien  and  D. Nunes  for advices, discussion or/and technical help;  J. Clements  and  P. Callaerts  for sharing Drosophila strains and antibodies. We acknowledge the  Drosophila Species Stock Center (DSSC) , the  Bloomington Stock Center  ( NIH P40OD018537 ), the  Kyoto Stock Center (DGRC) , for sending Drosophila stocks, the  Developmental Studies Hybridoma Bank  ( DHSN, University of Iowa ) for sending antibodies and Addgene for sending plasmids.
23 Genes were assigned to cell types by fold enrichment (i. e. mean expression in one cell type divided by the mean expression in all other cell types) or module membership in the case of co-expression (module membership is a measure of how correlated a gene's expression is with respect to a module's eigengene). Each of these datasets came with advantages and disadvantages, which motivated our decision to use all three. The Barres data was based on the analysis of human tissue; however, it covered only one brain region, was derived from a small number of individuals (n = 14) who all had an underlying neurological disorder (epilepsy, stroke and glioma), and lacked cellular detail. While the cell-type-specific data provided by the Linnarsson group covered both the central and peripheral nervous system, and contained remarkable cellular detail, it was mouse in origin.
5 , Supplementary Table  4 ). All of these modules were enriched for markers of pyramidal S1 neurons, which have previously been associated with SCZ. 15  Furthermore, some modules (brown, turquoise, dark magenta and cyan) were enriched for markers of interneurons and dopaminergic neurons, both of which are implicated in SCZ. 15, 24 g. 1  Overview of approach and datasets used. This study compiled several brain-related genomic annotations reflecting tissue-and celltype-specific activity, using data generated by the GTEx project,  20  the Barres group  21  and the Linnarsson group. 22 These annotations, each of which varied in their cellular resolution, included: tissue-specific eQTLs (reflecting the effect of genetic variation on gene expression); tissuespecific co-expression networks (reflecting the connectivity of a gene to all other expressed genes in the tissue), and tissue-and cell-typespecific gene expression.
Given our inability to implicate a cell type in PD, we wondered whether the latter scenario of pathway-specific risk might be applicable in PD. To address this question, we applied stratified LDSC to gene sets implicated in PD by Mendelian forms of PD, functional assays performed in the context of PD-associated mutations, such as the A53T missense mutation in SNCA, and rare-variant studies of sporadic PD. [29] [30] [31] [32] [33] [34]  In particular, we focused on gene sets associated with autophagy,  30, 31  the lysosomal system 32 and mitochondrial function. 33, 34 Our gene sets were derived either from Gene Ontology terms (autophagy) or curated gene databases (lysosomal, hLGDB; mitochondrial, MitoCarta 2. 0; see Methods), developed using literature curation (with a focus on unbiased proteomic studies) and experimental approaches.
Numerical results are reported in Supplementary Table  2  There are several caveats to this study from the limitations posed by the current PD GWAS, which does not capture the impact of rare variation or some forms of structural variation. These caveats include the quality of our annotations, the strategies employed to generate them and, perhaps most critically, the annotations we cannot account for. First, the quality of our annotations is especially pertinent in the case of the gene sets used to reflect various PD-implicated pathways. While lysosomal and mitochondrial gene sets were derived from rigorously curated gene databases, with a focus on unbiased proteomic and localisation studies, the autophagy list stemmed from Gene Ontology, which has not undergone the same meticulous curation.
The noise introduced by potentially inaccurate annotation could affect our ability to detect heritability enrichments. Furthermore, the quality of eQTL datasets, reflected in their power to detect an eQTL, is dependent upon sample size. It is entirely possible that with growing sample sizes, our understanding of the contribution of tissue-specific and brainregion-specific eQTL annotations to PD and SCZ heritability may change. Likewise, given that both TWAS  27  and coloc  28  are dependent upon the accuracy and integrity of eQTL datasets, it is likely that growing samples sizes will alter the list of PD susceptibility genes, potentially resulting in a different cellular expression profile. It is worth noting that there are several tools which evaluate associations between eQTLs and GWAS risk loci to identify susceptibility genes (including coloc, TWAS and others like eCAVIAR  52  ).
These tools differ in their underlying algorithms and assumptions (e. g. one versus multiple causal variants at a locus), thus motivating the use of multiple methods followed by integration across the results, as performed in the study by Li et al. 26  used here. Second, our strategy for creating cell-type-specific profiles primarily involved gene expression data and assumed disease relevance only if disease heritability enriched for SNPs within genes with high specific expression. This approach together with the use of the GTEx eQTL dataset (which is likely to be enriched only for eQTLs with larger effect sizes due to power limitations) should capture regulatory SNPs in close proximity to genes of interest. However, as demonstrated in a recent study from Hormozdiari et al. how one chooses to construct an eQTL annotation is fraught with challenges  53  and we recognise that our approach may have produced conservative enrichment estimates.
Stratified LD score regression (LDSC) We applied stratified LDSC  47  (see URLs, Supplementary information) to determine if various categories of genomic annotations (marking tissue-or cell-type-specific activity, as summarised in Annotation datasets) were enriched for heritability of various GWASs (see GWAS datasets below). LDSC exploits the expected relationships between true association signals and surrounding local linkage disequilibrium (LD) to correct out confounding biases, such as cryptic relatedness and population stratification, and arrive at unbiased estimates of genetic heritability within a given set of SNPs (here stratified according to whether they were located within genomic annotation regions). Following the procedure employed by Finucane et al. ,  47  we added annotation categories individually to the baseline model (version 1.
Annotation datasets Tissue-specific gene expression. Annotation files were generated by Finucane et al. ,  18  using GTEx V6P gene expression,  20  and obtained from Alkes Price's group data repository (see URLs, Supplementary information). Briefly, for each GTEx tissue, genes were ranked by a computed t-statistic reflecting their specific expression within that tissue versus all other tissues, excluding those that were from a similar tissue category (e. g. expression in cortex samples was compared to expression in all other tissues except other brain regions; see Supplementary Table  2  from Finucane et al. 18  for t-statistic tissue categories). The top 10% of expressed genes from each tissue was selected and a 100-kb window was added around their transcribed regions to obtain a tissue-specific gene expression annotation.
eQTLs that replicated across brain regions were collapsed into one entry and allocated an effect size (i. e. the absolute value of the linear regression slope) equal to that of the maximum effect size observed across the brain regions. Finally, eQTLs were assigned to either blood or brain by their effect size. A similar approach was used for the within-brain analysis, where eQTLs were assigned to one of the 11 brain regions based on effect size. Cell-type-specific gene expression. Cell-type-specific annotations were constructed using gene expression data from the Barres group  21  and the Linnarsson group  22  (see URLs, Supplementary information), which was generated using bulk RNA-sequencing and single-cell RNA-sequencing, respectively. Due to the disparate nature of the RNA-sequencing methods, each dataset was analysed separately.
59 The Linnarsson data was available with gene expression aggregated by sub-cell type/cluster. Genes were filtered on the Fig. 6  PD susceptibility genes do not enrich in brain-related cell types. a PD susceptibility genes were derived from MAGMA analyses and a study attempting to prioritise genes in PD using TWAS and colocalisation analyses. 26 Genes overlapping between the two sets were removed, resulting in a list of 89 genes. Bootstrapping tests performed using the EWCE method revealed no enrichment of PD susceptibility genes in the major cell-type classes (b) or their cell subtypes (c) from the Linnarsson single-cell RNA-sequencing dataset. Gene lists and numerical results are available in Supplementary Table  5  basis of expression >0, enrichment was calculated and a subset of 265 identified clusters were used as annotations.
Genes assigned to modules significantly enriched for brain-related cell-type markers of predominantly one cell type, with a module membership of ≥0. 5, were allocated a celltype "label" of neuron, microglia, astrocyte or oligodendrocyte and considered cell-type specific. Module membership values range between 0 and 1, with 1 indicating that a gene's expression is highly correlated with the module eigengene. An eigengene is defined as the first principal component of a given module and can be considered representative of the   17  and 18. Bootstrapping tests performed using the EWCE method demonstrated enrichment of autophagy, lysosomal and mitochondrial gene sets in specific cell-type classes (b) and their cell subtypes (c) from the Linnarsson single-cell RNA-sequencing dataset. Asterisks denote significance at p < 0.
05 after correcting for multiple testing with the Benjamini-Hochberg method over all gene sets and cell types tested. Gene lists and numerical results are reported in Supplementary Table  6  gene expression profiles within the module, as it summarises the largest of variance in expression. As with tissue-specific and cell-typespecific gene expression annotations, we added a 100 kb window around each gene. To ensure we were using consistent and reproducible modules, we evaluated preservation of GTEx modules with modules derived from co-expression networks constructed for frontal cortex, putamen and substantia nigra using gene expression data from the United Kingdom Brain Expression Consortium (UKBEC). 61, 66 Preservation values were calculated using the modulePreservation function from WGCNA.
Preservation is a measure of how well the connectivity and correlation structures within the genes of a network´s module are preserved in another tissue expression dataset. The final preservation score we report is Z-summarypress, which is an aggregation of a number of measures (see Langfelder et al. 67  for a detailed explanation). Values under 2 denote no preservation whereas values greater than 2 suggest preservation, and values over 10 suggest strong preservation of the module in the tissue tested. We included only GTEx modules with a preservation value >2. Preservation values and module descriptions are reported in Supplementary Table  4 ; the latter can also be viewed at https://snca. atica. um. es/coexp/Run/ Catalog/. Gene sets. 0] [31] [32] [33] [34]  The autophagy gene set included all genes associated with the Gene Ontology terms: GO:0006914 ("autophagy") and GO:0005776 ("autophagosome"), as derived from the GO C5 collection of the MSigDB database (v5.
2). Lysosomal genes were downloaded from the Human Lysosome Gene Database (hLGDB, see URLs, Supplementary information). 68 All genes reported lysosomal by any of the listed sources (9 of the 16 were unbiased proteomic studies) were used. Mitochondrial genes were obtained from Human MitoCarta 2. 0, an inventory of human genes with the strong support of mitochondrial localisation based on literature curation, proteomic analyses and epitope tagging/microscopy (see URLs, Supplementary information). 69 In addition, we used a gene set comprising loss-of-function (LoF)-intolerant genes, as defined by the Exome Aggregation Consortium (ExAC)  35  using their gene-level constraint metric (pLI ≥0. 9), and constructed an "all genes" gene set. The "all genes" gene set was constructed by extracting all genes from BioMart (using the homo sapiens GRCh37 library, as with all other annotations).
GWAS datasets Table 1  14, 16, 17, [71] [72] [73] [74]  MAGMA: assessing gene-level enrichment Gene-level p-values were calculated with the Nalls et al. 16  PD GWAS (excluding 23andMe contributions) using MAGMA v1. 06 (see URLs, Supplementary information),  25  which tests the joint association of all SNPs in a gene with the phenotype while accounting for LD between SNPs. SNPs were mapped to genes using NCBI definitions (GRCh37, annotation release 105); only genes in which at least one SNP mapped were included in downstream analyses. Gene boundaries were defined as the region from transcription start site to transcription stop site. In addition, we added a window of 35 kb upstream and 10 kb downstream of each gene. This choice was based on (1) most transcriptional regulatory elements fall within this interval,  75  (2) a fraction of GWAS risk loci lie outside gene boundaries and may regulate gene expression  76  and (3) previous work in the field of pathway analysis.
17, 77, 78 Furthermore, the MHC region on chromosome 6 (chr6: 25500000-33500000, human genome assembly GRCh37) was excluded. The gene p-value was computed based on the mean association statistic of SNPs within a gene, with genome-wide significance set to p < 2. 82 × 10 -6 , and LD was estimated from the European subset of 1000 Genomes Phase 3  57. Evaluating enrichment of PD-associated genes and gene sets Expression-weighted cell-type enrichment (EWCE, see URLs, Supplementary information)  79  was used to determine whether PD-associated genes or gene sets have higher expression within a particular cell type than expected by chance. As our input, we used the same subset of clusters from the Linnarsson single-cell RNA-sequencing dataset used in stratified LDSC, in addition to a target gene list.
For each gene in the Linnarsson dataset, we estimated its cell-type specificity i. e. the proportion of total expression of a gene found in one cell type compared to all cell types, using the 'generate. celltype. data' function of the EWCE package. EWCE with the target list was run with 100,000 bootstrap lists. We controlled for transcript length and GC-content biases by selecting bootstrap lists with comparable properties to the target list. P-values were corrected for multiple testing using the Benjamini-Hochberg method overall cell types and gene lists tested. We performed the analysis with major cell-type classes (e. g. "astrocyte", "microglia", "enteric neurons", etc. ) and subtypes of these classes (e. g. ACNT1 ["Non-telencephalon astrocytes, protoplasmic"], ACNT2 ["Non-telencephalon astrocytes, fibrous"], etc.
ACKNOWLEDGEMENTS R. H. R. was supported through the  award of a Leonard Wolfson Doctoral Training Fellowship  in Neurodegeneration. M. A. N. was supported by a consulting contract between Data Tecnica International and the  National Institute on Aging, NIH, Bethesda, MD, USA. J. H. and M. R. were supported by the  UK Medical Research Council (MRC) , with J. H. supported by a grant ( MR/N026004/ ) and M. R. through the award of a  Tenure-track Clinician Scientist Fellowship  ( MR/N008324/1 ). J. H. was also supported by the  UK Dementia Research Institute. Full consortia acknowledgements are available in the supplemental materials (Text  S2 ).
S1 ). Because promoter regions of the FP0506 locus are conserved between elastase-positive isolates, these variations are likely unrelated to a transcriptional modulation effect but may arise from differences in elastase release between isolates. FP0506 encodes a putative cell-surface-exposed lipoprotein that likely belongs to a new MP family of the gluzincin clan. The deduced FP0506 protein is 296 amino acids long and was originally annotated as a probable MP precursor, but that annotation was based on poor BLASTp scores  (18). Strikingly, FP0506 does not display any significant primary homologies with known elastases. Nevertheless, FP0506 possesses a strong lipoprotein signal prediction score, with a cleavage site located between residues 21 and 22. In addition, the amino acid residues located downstream of the invariant first cysteine (ϩ1 C of the mature protein) likely encode the lipoprotein export signal that was recently reported for the phylogenetically related bacterium Capnocytophaga canimorsus  (27).
Because of the remote primary homologies mentioned previously, we applied HHpred, which uses hidden Markov model (HMM)-HMM comparisons (after signal peptide removal, to increase the signal/noise ratio), to predict additional structures that might be relevant to assigning FP0506 to a peptidase clade/family. A HHpred search with the Pfam database (V31. 0), encompassing a large collection of protein families, identified (i) PF04228. 12, corresponding to a putative neutral zinc metallopeptidase, as the first hit (E value of 1. 8eϪ19) and (ii) PF01435. 17, corresponding to peptidase family M48, as the second hit (E value of 1. 7eϪ14), also suggesting that FP0506 belongs to the gluzincin clan. This clan is divided into several families based on additional characteristics. However, it was not possible to determine a unique gluzincin family for FP0506 by relying only on the presence of additional specific motifs.
Structural prediction. In order to build a structural model for FP0506, we used HHpred to search the three-dimensional (3D) protein structures in the Protein Data Bank (PDB) (PDB_mmCIF70_28_Jul) and identified 3C37 as the best protein template candidate (Fig. S6 ). This PDB entry corresponds to a putative Zn-dependent peptidase (UniProtKB accession no. Q74D82) from Geobacter sulfurreducens, which is in good agreement with our previous primary homology predictions. In addition, Q74D82 is a lipoprotein of about the same size (253 amino acids) as FP0506. Even though the pairwise alignment obtained with HHpred revealed a low primary identity percentage (17% over 65% of the length of the mature FP0506 protein), the result was highly significant (probability, 99. 54%; E value, 4eϪ16). Of utmost importance, the previously predicted gluzincin motifs of FP0506 are correctly arranged in the alignment, i.
3 ). Therefore, the predicted global fold of FP0506 was in rather good agreement with the X-ray structure of Q74D82, and the critical residues perfectly fit the 3D atomic repartition (Fig. S7B ). We also identified the structural features of the gluzincin clan, including the active site helix (cyan helix in Fig. 3 ) and the glutamate helix (magenta helix in Fig. 3 ). FP0506-homologous proteins are restricted to members of the family Flavobacteriaceae. In order to identify proteins similar to FP0506, we used BLASTn with the nonredundant protein database. We used rather relaxed parameters (40% sequence identity and 80% query sequence coverage) and retrieved 109 proteins; all were predicted lipoproteins, and all were from bacteria belonging to the family Flavobacteriaceae. Among those, a number of pathogens were identified, such as Flavobacterium columnare (another important fish pathogen), Elizabethkingia meningoseptica and Elizabethkingia anophelis (two deadly human pathogens), Chryseobacterium indologenes (a less severe human-pathogenic bacterium), and a number of alga-degrading bacteria such as Zobellia galactanivorans, Zobellia uliginosa, and Cellulophaga species.