dataesr/scientific-paragraphs-categorization huggingface dataset:
Genotypes were called using BeadStudio/Genomestudio software (Illumina®). Quality control (QC) measures included removing duplicate samples, samples with evidence of contamination (heterozygosity range Genomic features of human kidney. Human kidney-specific chromatin immunoprecipitation followed by sequencing (ChIP-seq) data can be found at GEO: GSM621634, GSM670025, GSM621648, GSM772811, GSM621651, GSM1112806, GSM621638. Different histone markers were combined into chromatin states using ChromHMM  45. Glomerular basement membrane measurement in RASS cohort. RASS study participants were followed for 5 years with percutaneous kidney biopsy completed prior to randomization and at 5 years. Structural parameters measured by electron microscopy on biopsy included GBM width, measured by the electron microscopic orthogonal intercept method  17.
8. 2. Illumina sequence quality was surveyed with FastQC. Adaptor and lower-quality bases were trimmed with Trim-galore. Trimmed reads were aligned to the Gencode human genome(GRCh37) with STAR-2. 4. 1d. The readcount of each sample was obtained using HTSeq-0. 6. 1 (htseq-count) and then normalized fragments per kilobase million values were used to perform association analysis with fibrosis and sclerosis using linear regression. Mouse kidney single cell RNA-sequencing. Kidneys were harvested from 4 to 8-week-old male mice with C57BL/6 background and dissociated into single cell suspension as described in our previous study  47. The single cell sequencing libraries were sequenced on an Illumina HiSeq with 2x150 paired-end kit. The sequencing reads were demultiplexed, aligned to the mouse genome (mm10) and processed to generate gene-cell data matrix using Cell Ranger 1.
3 (http://10xgenomics. com)  47. RNAseq and microarray profiling of human kidney samples from the Pima cohort. Kidney biopsy samples from the Pima Indian cohort were manually micro-dissected into 119 glomerular and 100 tubule-interstitial tissues to generate gene expression profiles  86. Expression profiling in the Pima Indian cohort kidney biopsies was carried out using Affymetrix GeneChip Human Genome U133 Array and U133Plus2 Array, as reported previously, and Affymetrix Human Gene ST Genechip 2. 1  87, 88  , and on RNA-seq (Illumina). The libraries were prepared using the ClonTech SMARTSeq v4 Ultra Low Input polyA selection kit. Samples were sequenced on a HiSeq 4000, single end, 75bp. Mapping to human reference genome GRCh38. 7 was performed with STAR 2. 5. 2b (https://github. com/alexdobin/STAR).
ACKNOWLEDGEMENTS This study was supported by a grant from the  JDRF  ( 17-2013-7 ) and  National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)  grants  R01 DK081923  and  R01 DK105154. R. M. S was supported in part by  JDRF  #  3-APF-2014-111-A-N , and  National Heart, Lung and Blood Institute (NHLBI)   R00 HL122515. J. N. T. was supported by  NIDDK   K12 -.
S2B is a valuable tool for DG prediction, bringing new insights into pathological mechanisms. More generally, S2B can be applied to infer the overlap between other types of network modules, such as functional modules or context-specific subnetworks. An R package implementing S2B is publicly available at https://github. com/frpinto/S2B. Disruption of a gene sequence may cause the dysfunction of the encoded protein, which can trigger the onset of a disease. Such genes are defined as disease causal genes. Nevertheless, a disease is a pathologic phenotype resulting from synergic disruptions of varied cellular functions caused by both genetic and environmental factors 1. Consequently, disease associated genes (hereinafter called Disease Genes (DGs)) are not necessarily causal. They can be modifiers, that modulate disease severity, or phenotypical, unable to influence the disease course but responsible for disease phenotypes.
To expand the list of known DGs, information systems, like DisGeNet 9 , Open Targets 10 or DISEASES 11 , integrate and weight heterogeneous evidence sources linking genes with diseases, including text-mining approaches. Network-based DG prioritization methods aim to recover complete disease modules, using network interactions of known DGs to predict new DG candidates. One such method, DIAMOnD 4 , starts from the set of known.
We considered applying one of these methods to the seeds of two diseases separately as an S2B alternative. Proteins in the intersection of the two prediction sets would be candidates for simultaneous association with both diseases. We tested this hypothesis with the DIAMOnD algorithm  4. For each module, 250 iterations were computed and the intersection between the two sets of 250 proteins was compared with the known overlap, estimating DIAMOnD precision (Table  1 ). DIAMOnD predicts many candidates for connectivity modules with moderate precision, while for shell and rwr modules the number of candidates is generally small and precision low. A better performance of DIAMOnD with connectivity modules was expected, as these are generated with the same algorithm used by DIAMOnD to make predictions.
For each pair of artificial modules tested, we selected from the top S2B candidates the same number of candidates predicted by DIAMOnD. The matched S2B precisions are higher than DIAMOnD's for shell and rwr modules, but lower for connectivity modules (Table  1 ). For this type, the number of DIAMOnD candidates is large and, as shown in Fig. 1A , S2B precision for connectivity modules decays quickly with candidate rank. S2B predictions would have a median precision of 0. 60 (similar to DIAMOnD) if the top 20 candidates were considered. In conclusion, although DIAMOnD is a good approach for connectivity type modules, S2B provides a good performance for every type of module tested.
Acknowledgements This work is part of an  EU   Joint Programme -Neurodegenerative Disease Research (JPND)  project with the acronym ' Fly-SMALS '. The project is supported through the following funding organisations under the aegis of JPND -www. jpnd. eu: France ,  Agence Nationale de la Recherche ;  Germany ,  Bundesministerium für Bildung und Forschung (BMBF, FKZ) Portugal, Fundação para a Ciência e a Tecnologia (FCT)  and  Spain, Instituto de Salud Carlos III (ISCIII). In particular this work was funded by the  ISCIII  and  FEDER  through projects  AC14/00024  and  PI15/00328 , by  FCT  through grant  JPND-CD/0002/2013  and  FCT/MCTES/PIDDAC research center grant  to  BioISI   UID/MULTI/04046/2013. MLG is a recipient of a fellowship from  BioSys-PhD  programme (ref  PD/ BD/128109/2016 ) from (FCT), Portugal.
Data availability. The datasets analysed during the current study are available in OMIM [www. omim. org], DisGENET [www. disgenet. org], APID [apid. dep. usal. es] and Huri [interactome. baderlab. org] repositories. All datasets generated during the current study are are included in this published article (and its Supplementary files). Code availability. An R package implementing S2B is publicly available at https://github. com/frpinto/S2B.
, 2009). Microinjections were made (0. 5 ml at a rate of 0. 5 ml/min) via a 10 ml Hamilton syringe mounted on an infusion pump, connected to the injector by a length of plastic tubing. Extracellular voltage excursions were amplified, band-pass filtered (300 Hz-10 kHz), digitized at 10 kHz and recorded directly onto computer disc using a Micro 1401 data acquisition system (Cambridge Electronic Design [CED] Systems, Cambridge, UK) running CED data capture software (Spike 2).
Peri-stimulus time interval histograms (PSTHs) were constructed based on SC/PBN multi-unit (bin width 1 ms) and STN single-unit data (bin width 10 ms). PSTHs were imported into an Excel program (Peter Furness, University of Sheffield;  Coizet et al. , 2006 Coizet et al. , , 2009 Coizet et al. , , 2010;; Dommett et al. , 2005) , which determined the following response characteristics. (i) Baseline activity: the mean number of single multi-unit events during the 500 ms bins prior to the footshock. (ii) Response latency: the latency of a visually evoked response was marked as the point when the value of post-stimulation events exceeded 1. 96 S. D. of the baseline mean. (iii) Response duration: response offset was recorded when post-stimulation activity returned to a value 1. 96 S. D. of the baseline mean.
Statistics The statistical reliability of differences between response latencies for the SC/PBN and STN, and comparisons of response duration, amplitude and magnitude before and after SC/PBN injections of muscimol was made using parametric (ANOVA, t-test) or non-parametric (Wilcoxon, Mann-Whitney) statistical tests according to the normality of the data. STN baseline firing rate change before and after the noxious stimulation was assessed during the 500 ms before the sham and noxious stimulations. The data were imported in MATLAB, bined and compared using a Wilcoxon test. STN firing pattern was also assessed using MATLAB according to the methodology developed by  Piallat et al. (2011). Neurons were classified as irregular, regular or bursting according to the interspike distributions and autocorrelograms.
8 caudal to bregma). A series of digital images (magnification 10 X) were taken and imported into a graphics program (Macromedia Freehand) where they were montaged. The borders and layers of the PBN were drawn over the montage. The location of anterogradely labelled axons and terminals was plotted on sagittal sections from 1. 9 to 2. 9 lateral from bregma. Fibres and terminals associated with the injections were traced with the aid of a pen tablet (intuos) with a microscope equipped with a camera lucida and a graphics program (Microsoft PowerPoint). By focusing on different depths in the brain sections, it was possible to produce a drawing that contained all labelled elements in the section. For the 3D reconstruction, the most representative example was selected and series of sagittal sections containing PBN and STN were digitized using a light microscope (Nikon, Eclipse 80i, TRIBVN, 2.
9. 2 version, Chatillon, France) coupled to the ICS Framework computerized image analysis system (TRIBVN, 2. 9. 2 version, Chatillon, France) and a Pike F-421C camera (ALLIED Vision Technologies Stradtroda, Germany). Digitized images were converted into a. tiff format and individually exported into Adobe Photoshop to create individual. tiff files with the same dimensions. A stack of 2D sagittal sections was then created using Cygwin (Cygwin TM sources) and IMOD package software (Boulder laboratory for 3D Electron Microscopy of Cells, University of Colorado, Boulder, CO)  (Kremer et al. , 1996). As previously described  (Coizet et al. , 2017; Mailly et al. , 2010) , sections were aligned with the Midas program from IMOD using manual rigid body transformations. The stack was opened in IMOD, where the structures of interest were delineated including the PBN, SNr/SNc, STN, and SC.
FDG-PET/CT interpretation FDG-PET/CT findings were interpreted by two experienced nuclear medicine physician blinded to clinical information. Usual clinical reporting visualisation protocol was used; orthogonal CT, PET and fused PET/CT images were displayed simultaneously, alongside a rotating maximum-intensity projection using Beth-Israel PET-CT viewer plug-in (http:// petctviewer. org) for ImageJ software from FIJI (http://www. fiji. sc). This free and open source software being the tool of choice as it can gather various imaging information at the same time and allows reliable measures for statistical analysis  [13, 14]. Beth-Israel PET-CT viewer enable to draw outlines to define adapted regions of interest (ROI) in order to measure metabolic parameters for every single metastatic site.
The next step could be to reassess the history of human populations that was drawn using genomic data. 2% of the human genome, near recombination hotspots  (Glemin et al. , 2015) , but due to the transient nature of these hotspots, a larger fraction of the genome could have been affected in the long term. Here, we use two whole-genome human datasets to determine how and to what extent recombination and selective forces affect genome-wide diversity in humans. We examine the relationship between recombination rate and the average derived allele frequency per individual, as well as the SFS. After determining the parts of the genome that are least affected by BGS and gBGC, we examine the impact of these two processes on the SFS, and how they affect demographic inference based on the SFS.
5 cM/Mb at WS sites (Figure  1C , left) is entirely due to gBGC. Note that the exact same pattern holds for SGDP populations (Figure  1 -figure supplement 4C). Moreover, if we analyze all possible types of substitutions separately, gBGC appears to affect the 12 types of SNP according to whether the SNP type belongs to the SW, WS, or WW +SS class (Figure  1 -figure supplement 7. These results suggest that SNPs located in regions where recombination is higher than 1. 5 cM/Mb are affected by gBGC and not by BGS (Figure  1C , Figure  1 -figure supplement 6C). Therefore, WW and SS sites with a recombination rate above 1. 5 cM/Mb (representing 2. 88% and 2. 94% of all SNPs for 1000G and SGDP datasets, respectively) should be optimal for demographic inference, as they appear to evolve mainly neutrally.
Impact on demographic inferences To investigate the impact that the choice of SNPs may have on demographic inference, we estimated demographic parameters for the Yoruba and Japanese populations using three different SFSs (Figure  3A ): the synonymous SFS commonly used in exome resequencing studies; the SFS inferred on non-transcribed regions at least 50 kb away from coding regions (NTR-50kb), and on our best-filtered dataset (WW + SS sites in !1. 5 cM/Mb regions), hereafter called the 'neutral' dataset. Note that this neutral SFS was computed over both TR and NTR regions since they show the same SFS (Figure  2C ). Interestingly, the SFS observed at synonymous sites differs markedly from that observed at neutral sites, as it comparatively shows a significant deficit of low-frequency variants and a large excess of high-frequency variants (Figure  3A , Figure  3 -figure  supplement 1A ).
The ancient demography is markedly different with a significantly more ancient second bottleneck and a significantly lower ancient population size inferred from both synonymous and NTR-50 kb SFS. The Japanese demography inferred from the three data sets shows more similarity over the last 600 ky but the demography inferred from the neutral data set suggests a stronger recent bottleneck (pre LGM) and no population expansion as compared to what is inferred from the synonymous SFS neutral data set. Our results thus clearly show that very different demographies can be inferred from neutral and non-neutral SFSs. However, even though BGS and gBGC affect the SFS of populations with distinct histories in a qualitatively similar way, they have different consequences on their reconstructed demography.
(2014) ;  Graur, 2017)  have an important but indirect influence on most of the genome. As expected, the effect of BGS is clearly mediated by local recombination rate, but it extends well beyond coding regions in humans  (Hernandez et al. , 2011)  (Figure  1 ), and it is thus not restricted to species with a large effective size  (Corbett-Detig et al. , 2015). Our results also show that the influence of gBGC is not restricted to recombination hotspots  (Spencer et al. , 2006; Glemin et al. , 2015) , but that it has also a strong footprint in regions with a recombination rate larger than 1. 5 cM/Mb, but note that it could affect (to a lesser degree) regions with an even lower recombination rate (see Figure  1 -figure supplement 5D. These regions represent about 15. 9% and 16. 2% of the polymorphic positions for the 1000G and SGDP datasets, respectively.
g. Li and Durbin, 2009; Sheehan et al. , 2013; Schiffels and Durbin, 2014)  should also be sensitive to BGS and gBGC, since they assume that heterozygosity levels within individuals is not driven by local recombination rates nor selection. In this respect, the history of human populations as well as that of other species might be more readily inferred from methods that can conveniently analyze restricted sets of neutrally evolving sites interspersed across the genome. Similarly, other types of inference using a biased neutral SFS as a reference could also be affected, such as inferences of the distributions of fitness effects (DFE)  (Keightley and Eyre-Walker, 2010; Kim et al. , 2017; Tataru et al. , 2017) , even though the magnitude of the effect remains to be investigated. In conclusion, we show that BGS and gBGC had a pervasive effect on most of our genome, but that we can conveniently define a set of sites (representing about 3% of all polymorphic sites of both 1000G and SGDP datasets) that should not be too influenced by these two evolutionary forces, even though some sites close to conserved elements could still be affected by BGS.
Datasets We analyzed two distinct whole genome datasets. The first one consisted of 100 individuals from ten 1000G populations  (Auton et al. , 2015). For each 1000G population, we selected the ten individuals with the highest depth of coverage (coverage >10Â), such as to maximize the number of sites having no missing data. We also analyzed 20 individuals from panel C of the Simons Genome Diversity Project (SGDP)  (Mallick et al. , 2016). These individuals were selected from ten SGDP populations that were geographically close to those analyzed for the 1000G project. Coverage was higher for the SGDP individual and ranged between 31 Â and 64Â (see Supplementary file 1 -Table  S1  for IDs and location of the 1000G and SGDP samples).
Data processing and annotations We processed the 1000G and SGDP datasets identically. We removed all sites with any missing data and kept only diallelic sites from autosomal chromosomes. The ancestral state of each variant in these genomes was set to the chimpanzee reference genome (panTro4 genome assembly) to avoid any discrepancy between African and non-African populations. Only diallelic SNPs for which one of the variants observed in the 1000G or SGDP datasets corresponded to the chimpanzee ancestral state were kept for later analyses. In addition, we removed the CpG sites that present a peculiar mutation profile and are correlated with recombination rate  (Arbeithuber et al. , 2015). We used the LD-based Yoruba-specific recombination map from the 1000 Genomes project  (Frazer et al. , 2007)  to obtain the local recombination rate (RR) surrounding each SNP.
We also estimated local RR by using three other maps: the LD-based CEU or JPT-specific recombination maps  (Frazer et al. , 2007)  and the sex-averaged pedigree-inferred deCode map  (Kong et al. , 2010). For each of these maps, we filtered out SNPs without RR information (see Supplementary file 2 -Table  S2 ). We used the Yoruba-specific map to define hotspots as regions with RR >10 cM/Mb. Using Biomart (http:// grch37. ensembl. org/biomart/martview/), we assigned SNPs to transcribed (TR) and non-transcribed regions (NTR). For each site, we inferred the distance to the closest exonic region in cM and in bp using the Ensembl exon positions (ftp://ftp. ensembl. org/pub/grch37/release-90/gtf/homo_sapiens/ Homo_sapiens. GRCh37. 87. gtf. gz). The B-statistic  (McVicker et al. , 2009)  (indicative of the strength of local background selection) associated with each SNPs was retrieved from http://www.
phrap. org/ othersoftware. html and lifted over from the hg18 to the hg19 reference genome using the UCSC lift-Over tool. Genomic Evolutionary Rate Profiling (GERP) rejection scores  (Davydov et al. , 2010 ) that quantify the level of evolutionary constraint acting on polymorphic sites and conserved elements identified using PhastCons on the primate subset of 46 vertebrates  (Siepel et al. , 2005)  were downloaded from the UCSC platform  (Speir et al. , 2016). The number of SNPs from the 1000G and SGDP datasets retained for each filter is reported in Supplementary file 2 -Table  S2. We finally retrieved 37,574 potentially neutral regions of 1 kb (e. g. Gronau et al. , 2011; McManus et al. , 2015; King and Wakeley, 2016; Veeramah et al. , 2018)  from http://compgen. cshl. edu/GPhoCS/ data. php to make comparisons between our neutral set of SNPs to another possible sets.
Differences in mutation rates across the genome might also affect DAF i for some regions, but should not lead to individual differences, unless mutation rates are different in specific populations. For both SGDP and 1000G data sets, we ranked SNPs according to their associated recombination rate and binned them into 20 equal-sized classes of increasing recombination rates. We performed a similar binning for the different groups of SNPs we considered (the three types of mutations, within a transcribed region or not, etc. ) or after ranking SNPs according to their distance to the nearest exon, to hotspots or to conserved elements. We then computed DAF i for each bin b as DAF ib ¼ n ib = 2S tot;b À Á.
Site frequency spectrum We estimated the unfolded SFS for ten 1000G population samples using different filters (e. g. different recombination classes, different types of mutations). The SFS was then normalized  (Lapierre et al. , 2017)  by dividing each entry by its expectation in a stationary population. To estimate if two SFSs are statistically different, we used a permutation approach. We first computed a distance between the two SFS as the sum of the squared difference in site frequencies over all SFS entries (noted D obs ). We divided the SNPs into three categories: those shared by the two SFS (if any), and those that were private to one of the SFS. We then randomly permuted sites among the two latest categories and re-evaluated the distance noted D est. When one SFS was based on a subset of variants from another SFS, we subsampled sites from the largest dataset and re-evaluated D est.
Demographic inference We estimated the parameters of the demographic model shown in Figure  3C  from the SFS of two 1000G populations (Japan and Yoruba) using the program fastsimcoal2  (Excoffier et al. , 2013)  ver 2. 6. We used the following command line options:. /fsc26 -t pop. tpl -n200000 -d -e pop. est -M -l25 -L50 -q À0 -C1 -c1 -B1, where pop denotes either the Japan or the Yoruba population. We used the tpl and est setting files defined in Supplementary file SF1. For each population, we performed 50 independent estimations and retrained the parameters that maximized the model likelihood. The confidence intervals of the parameters were estimated from 100 block-bootstrapped SFS obtained in a way similar to that described above. For each population, estimations were performed on each bootstrap dataset independently, using the maximum likelihood (ML) parameters values estimated above as initial values.
Individual-based simulations We performed individual-based simulations using the software SLiM v. 2. 3  (Haller and Messer, 2017)  to check that BGS could reproduce observations. We simulated the demographic scenario inferred from the 'neutral' SFS (i. e. from WW + SS sites with r ! 1. 5 cM/Mb) for the Japanese (JPT) and Yoruba (YRI) 1000G populations as described above (Demographic inference). We simulated a linear genome of 50 Mb made up of 1000 regions of 5 kb. Each of these regions consisted of a 1 kb stretch experiencing purifying selection against deleterious mutations, followed by a 4 kb stretch with neutral mutations. We also simulated an alternative genomic architecture with 10,000 regions of 500 bp, each consisting of a 100 bp stretch under purifying selection, followed by a 400 bp stretch with neutral mutations.