dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Availability The cryo-EM maps and associated coordinates have been deposited in the EMDB and on the PDB: KC-1 (EMDB: 4624, PDB: 6QS6), KC-2 before AAA2F classification (EMDB: 4625), KC-2A (EMDB: 4626, PDB: 6QS7), KC-2B (EMDB: 4627, PDB: 6QS8), KC-3 (EMDB: 4621, PDB: 6QS4), KC M-domain conformation 1 MD-1 (EMDB: 4622), KC M-domain conformation 2 MD-2 (EMDB: 4623) WT-1 (EMDB: 4940, PDB: 6RN2), WT-2A (EMDB: 4941, PDB: 6RN3), WT-2B (EMDB: 4942, PDB: 6RN4).
The numerical model The numerical simulation platform ELLE (http://www. elle. ws; Jessell et al. , 2001; Bons et al. , 2008) , including the full-field viscoplastic code based on the Fast Fourier Transform algorithm (VPFFT;  Lebensohn, 2001; Lebensohn et al. , 2008) , is used to study the microstructural and mechanical evolution of aggregates of solid grains (referred to as "solid") with a second phase, referred to as "melt". ELLE couples different processes acting on the microstructure in a sequential order for small time steps  (Jessell et al. , 2001). ELLE has been applied to a range of problems in geology  (Bons et al. , 2008) , of which the most relevant to this study are simulations of static and dynamic grain boundary migration in single and twophase materials  (Bons et al. , 2001; Becker et al.
1c ). Each unode represents a small area with a certain lattice orientation, defined by three Euler angles  (Llorens et al. , 2016a) , and a dislocation density. The VPFFT code uses these unodes for the viscoplastic deformation calculations. The change of the position of bnodes is calculated according to the deformation velocity field and grain boundary migration (GBM). Both VPFFT and ELLE use both horizontally and vertically wrapping boundaries, so that the upper and lower parts of the model are connected, as well as the left and right ones (Fig. 1 ;  Jessell et al. , 2001). This makes the model periodic in all directions. Regions falling out of the square bounding box are repositioned within the box after each calculation step, so that the unit cell remains square through deformation in simple shear (see Fig.
, 2011). The ratio of CRSS for slip systems in the melt with respect to those in the solid was set to 100. This value was chosen as a compromise between computational time and accuracy of the simulations (both increasing with increasing CRSS ratio). The same value of stress exponent of n=3 was set for all the slip systems in both melt (soft) and solid (hard) phases. This approach has been previously compared with an updated version of the VPFFT code  (Lebensohn et al. , 2011; 2013)  that avoids any crystallography and imposes zero stiffness to unodes in the soft phase. As shown in  Steinbach et al. (2016) , the results are essentially the same using both approaches, validating the use of a CRSS ratio of one hundred. Solid grains have randomly assigned initial Euler angles for their lattice orientation (Fig.
Acknowledgements This work was founded by the joint project " Rheology of the continental crust in collision ", funded by the  Procope scheme of PHC Egide in France  and by the  DAAD PPP  scheme in Germany. M-GL acknowledges the support of the  Juan de la Cierva programme of the Government of Spain's Ministry for Science, Innovation and Universities. EGR acknowledges the support of the  Beatriu de Pinós programme of the Government of Catalonia's Secretariat for Universities and Research of the Department of Economy and Knowledge  ( 2016 BP 00208 ). This work benefited from discussions with  Pi L. Jolivet  and  E. Burov  within the  ERC  project  RHEOLITH. We thank  Elisabetta Mariani  and  Marcin Dabrowski  for their helpful comments, together with the editorial guidance of  Dave Healy  and  Bill Dunne.
e. , <1 supporting read per sample or the reads supporting the splice site account for <5% of all reads, referred as weak splice sites). The remaining 20%, corresponding to 217,792 high-activity splice sites, were composed of 56,049 sites that were active in a subset of transcripts (i. e. , reads supporting the splice site account for 5-95% of all reads, alternative splice sites), and 161,743 sites that were constitutively active in our dataset (>95% of supporting reads, constitutive splice site). These observations, which set the bases of our study, indicate pervasive, AS in resting and activated monocytes. Reduced long-term conservation of immune gene splicing. We first investigated the evolutionary pressures characterising the various types of splice sites (weak, alternative and constitutive) in the whole dataset, using the Genomic Evolutionary Rate Profiling (Gerp) statistical framework  42.
We identified 28 high-confidence human-specific splice sites (Supplementary Data 1), several of which involved immune genes such as the interferon-inducible gene NUB1, the leukocyte immunoglobulin-like receptor LILRB4, or the cytokine CSF3 that is responsible for the regulation of granulocyte survival and proliferation (Fig. 1d ). Altogether, these results support a more recent emergence of splice sites at immune genes, with respect to non-immune genes, and suggest that AS has been an important substrate of innovation in the evolution of human immune responses. Widespread impact of immune activation on isoform usage. To characterise the variability of splice site usage in humans, we next quantified inter-individual differences in AS at the basal state and following immune stimulation. Focusing on highly expressed genes (FPKM > 10), we quantified 39,030 AS events and calculated the percent-spliced-in (PSI) index, which reflects the frequency at which a specific splicing event occurs.
These enrichments remained significant when excluding the HLA region (GOseq p < 2. 4 × 10 -5 ), and accounting for gene expression levels (resampling p < 1. 0 × 10 -4 ) or a higher rate of AS events at immune response genes (resampling p < 1. 0 × 10 -4 ). After excluding the HLA region, 66% of sQTLs were located within 10 kb of their respective AS event (Fig. 4a ), with 38% falling directly within the boundaries of the AS event. We found that sQTLs were strongly enriched in exonic and near-exonic regions (<300 nucleotides from splice site, OR > 5. 7, Fisher's exact p < 1. 5 × 10 -84 ), and in loci predicted to alter splicing (SPIDEX database  36  , OR = 4. 7, Fisher's exact p < 1. 7 × 10 -19 , Fig. 4b ). Consistently, we found a strong enrichment of sQTLs in splicing regulatory regions (e.
Methods Ethics statement. All experiments involving human primary monocytes from healthy volunteers, who gave informed consent, were approved by the Ethics Board of Institut Pasteur (EVOIMMUNOPOP-281297) and the relevant French authorities (CPP, CCITRS and CNIL). Samples and dataset. The high-density genotyping and RNA-sequencing data used in this study were generated as part of the EvoImmunoPop project  38. Briefly, the EvoImmunoPop cohort is composed of 200 healthy, male donors of selfreported African and European ancestry, all living in Belgium (100 individuals of each population). Genotyping data was obtained, for all individuals, using both Illumina HumanOmni5-Quad BeadChips and whole-exome sequencing with the Nextera Rapid Capture Expanded Exome kit, leading to a total of 3,782,260 SNPs after stringent quality control.
This dataset was then used for imputation, based on the 1,000 Genomes Project imputation reference panel (Phase 1 v3. 2010/11/23), leading to a final set of 19,619,457 high-quality SNPs, including 7,650,709 SNPs with a MAF >5% in our cohort. Details on filtering criteria and quality control have been provided elsewhere  38. RNA was collected from non-stimulated and stimulated monocytes, which were purified from peripheral blood mononuclear cells (PBMCs) with magnetic CD14 microbeads, and sequenced at a depth of ~34 million single-end reads per sample, yielding a final dataset of 970 transcriptional profiles. The purity of the CD14 + monocyte fraction and its lack of contamination by neutrophils were assessed by flow cytometry (Supplementary Note 6; Supplementary Fig. 8 ). Monocytes from each individual were exposed, for 6 h, to four stimuli including LPS (bacterial lipopolysaccharide, activating TLR4, n = 184), Pam 3 CSK 4 (synthetic triacylated lipopeptide, activating TLR1/2, n = 196), R848, (imidazoquinoline compound, activating TLR7/8, n = 191) and to a human seasonal IAV (strain A/USSR/90/1977 (H1N1), n = 199), and compared with resting monocytes from the same individuals (n = 200).
Normalised gene expression values (FPKM) were computed with CuffDiff and corrected for GC content, ratio of average gene body coverage at 5′/3′ ends of the gene, using linear models and for date of experiment and date of library preparation using ComBat  70. In addition, htseq-count was used to measure, for each sample, the total number of intronic read counts per gene (defining intronic regions as genic regions that are not part of any known exon). Intronic read counts were then divided by total intron length and total read count of the sample, to yield intronic RPKM that were used as a proxy of the transcription rate in downstream analyses. Quantification and definition of splice sites. Reads from all samples were aligned on hg19 genome using the HISAT2 software version 2. 0. 1  71  and the publicly available genome_snp index, which includes all common SNPs from dbSNP 144.
Known splice sites were extracted from Ensembl Genes v70 using the hisat2_extract_splice_sites script, and used as input to the -known-splicesiteinfile option to guide the mapping of spliced reads. All other parameters were left to their default value. Spliced reads with an overhang of at least six nucleotides into each exon were then extracted from HISAT2 aligned reads using the filter_cs script from leafcutter package  72  , and reads spanning over 100 kb, or falling outside genic regions, were discarded to reduce the risk of mapping errors. The resulting set of >7. 2 billion spliced reads was used to identify the boundaries of spliced-out introns, and we defined donor and acceptor sites as the two-base pairs located at each end of the detected introns. We then compared the detected splice sites with those of the intropolis database  73  , which regroups 42,882,032 introns previously observed in the European Nucleotide Archive, and estimated the frequency of non-GT/AG splice sites as a measure of false positive rate (Supplementary Fig.
Remaining sites were considered as weakly active (weak splice sites). Weak splice sites that are absent from Ensembl annotations and show no evidence of conservation (GerpRS < 2, see below) were considered as cryptic. Among high-activity splice sites, those that are used in over 95% of transcripts were considered as constitutive, whereas others were considered as alternative. In addition, splice sites were annotated as coding if they were located next to coding exons in Ensembl v70, and as non-coding otherwise. Long-term evolutionary analysis of splice sites. To measure the conservation of splice sites across mammals, we retrieved pre-computed base-wise GerpRS scores  42  for hg19 from the website of the Sidow Lab (http://mendel. stanford. edu/SidowLab/ downloads/gerp/index. html). To reduce the risk of misalignments, we excluded from our analysis positions that (i) had a null GerpRS score, (ii) were absent from the UCSC MULTIZ-46way alignments  74  or (iii) had a negative score in the MULTIZ-46way alignments.
Human transcripts were retrieved from Biomart (Ensembl Genes 70-GRCh37), and SUPPA eventGenerator script was used to annotate local AS events (AS events; script available at https://github. com/comprna/SUPPA). This resulted in a total of 163,525 annotated AS events (Alternative first exon-AF: 68,751, Alternative last exon-AL: 17,536, Alternative 3′ splice site-A3: 15,054, Alternative 5′ splice site-A5: 14,274, Mutually exclusive exons-MX: 4,768, Retained intron-RI: 5,789, Skipped exon-SE: 37,353). To ensure sufficient power in the detection of both isoforms at AS events, we first focused, for each condition of immune stimulation, on genes with an average FPKM > 10, and AS events where each possible alternative intron was supported by at least 30 spliced reads. We then quantified the resulting 39,030 AS events using MISO to obtain PSI values  12  and filtered out, in each condition, rare AS events with a mean PSI value under 5% or over 95%, as well as AS events with >5% of missing values (due to the lack of informative reads in the sample).
We next computed correlations between PSI value profiles of overlapping AS events, and considered AS events as related events when their PSI values showed an absolute correlation > 0. 5. We thus defined a graph of AS events relationships, and used the cluster_walktrap function from the igraph R package to define clusters of highly connected AS events. We then selected a single representative event for each cluster, based on total counts of spliced reads supporting the events. After filtering overlapping AS events, we obtained a final set of 16,173 AS events for analysis (A3: 2,625, A5: 2,062, AF: 2,018, AL: 1,167, MX: 566, RI: 1,415, SE: 6,320), which we adjusted for batch effects and technical covariates using ComBat 70 (Supplementary Note 2; Supplementary Fig. 2f ). To assess the putative consequences of AS events at the protein level, we retrieved transcript annotations from Ensembl v94 and considered for each AS event, the set of transcripts that are compatible with each possible splicing (reference and alternative isoforms) and the corresponding protein(s).
We mapped local sQTLs, likely cis-acting, with Matrix-EQTL  76  , using a 1 Mb window on each side of the splicing event boundaries. sQTL mapping was performed separately for each condition and merging both populations. PC1 and PC2 of the genotype matrix were included in the model to account for population stratification, and PSI values were rank transformed to a normal distribution before mapping, to reduce the impact of outliers. FDR was computed by mapping sQTLs on 100 permuted datasets, in which genotypes were randomly permuted within each population. We then kept, for each permutated dataset, the most significant p-value per AS event, across all conditions, and computed the FDR associated with various p-value thresholds ranging from 10 -3 to 10 -50. We subsequently selected the p-value threshold that provided a 5% FDR (p < 4.
8 × 10 -7 ). To compare sQTLs across conditions, we used a Bayesian framework to select the most likely model of sQTL sharing across conditions (Supplementary Note 4). sQTLs for which the gene was expressed specifically after stimulation (FPKM < 10 in the non-stimulated state and log 2 FC > 1), were annotated as sQTLs of stimulation-specific genes. rsQTLs were defined as those where (i) the gene was expressed at FPKM > 10 both before and after stimulation, (ii) the sQTL was significant specifically after stimulation (p > 0. 001 in the non-stimulated state) and (iii) the sQTL was significantly associated to changes in PSI value upon stimulation (p < 0. 01, corresponding to ~5% FDR, when testing all sQTLs across the 4 stimuli). Regulatory elements and predicted impact on splicing. The SPIDEX TM database  36  was retrieved from the deepgenomics website (https://www.
deepgenomics. com/spidex-noncommercial-download), and functionality scores (Z index) were extracted from all SNPs present in the database (i. e. , located < 300 nucleotides apart of a known splice site). Scored SNPs were annotated as exonic or intronic based on Ensembl v70 annotation, and considered deleterious when their absolute Z-score was above 2, and benign otherwise. We then used Fisher's exact test to assess the enrichment of sQTLs in exonic and intronic regions, as well as in predicted splice-altering regions, considering the peak SNP at each locus. To further determine the set of regulatory elements that contribute to regulate splicing, we annotated SNPs for various types of regulatory features including overlap with (i) donor/acceptor sites (2-bp upstream or downstream of each exon), (ii) splice site flanking regions (3-8-bp upstream or downstream of an exon), (iii) expected branchpoint regions (18-39-bp upstream of acceptor site), (iv) transcriptional regulatory elements (promoter, promoter flanking, enhancer, and CTCF binding sites) that are active in monocytes, based on Ensembl Regulatory Build v80 77 and (v) conserved regions (GerpRS > 2).
In addition, to explore how sQTLs overlap motifs of RNA-binding proteins, we retrieved a set of 107 motifs bound, in vitro, by 81 human splicing factors and RNA-binding proteins  78. We then kept a single motif per protein, giving priority to the motif with the highest information content, and used Homer 79 to scan the reference genome sequence for these motifs. Namely, we used findMotifsGenome. pl script with -mask and -rna to identify motifs of human RNA-binding proteins in a 20 nucleotides window around each SNP (considering both strands), and excluded motifs that did not directly overlap the SNP or presented a motifScore of 5 or less. We used a Fisher's exact test to assess the enrichment of sQTLs in SNPs that overlap the regulatory motifs and features described above, with respect to genome-wide expectations.
Acknowledgements This project was funded by the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-2013)/ ERC  grant agreement  281297  (to L. Q. -M. ). The  Quintana-Murci lab  is funded by the  French Government 's  Investissement d'Avenir program ,  Laboratoires d'Excellence "Integrative Biology of Emerging Infectious Diseases"  ( ANR-10-LABX-62-IBEID ) and the  Fondation pour la Recherche Médicale  (Equipe FRM  DEQ20180339214 ). M. R. was supported by a  Marie Skłodowska-Curie fellowship  ( DLV-655417 ). We thank  Etienne Patin  and  Mary O'Neill  for helpful comments and suggestions.
GBS has also recently been used to detect SNPs and small indels in a proton beam mutant population of soybean  [26]. However, we are not aware of attempts to bring GBS beyond SNP and indel genotyping other than a study which assigned ploidy levels based on GBS data  [27]  and another which called CNVs from GBS data but did not provide independent validation of these calls  [28]. In this study, we demonstrate that GBS can be used as a high-throughput and low-cost method for characterizing CNVs in a fast neutron-mutagenized population, and thus provide a more affordable approach for populationscale CNV discovery than aCGH or WGS. To do so, we subjected 92 mutants to a standard GBS protocol, 19 of which were also assessed by aCGH in order to provide a validation dataset for benchmarking the GBS pipeline.
Development and validation of an approach to call copy number variants using GBS data We wanted to see whether a GBS protocol could be used to call CNVs in a collection of 92 soybean mutants generated by irradiation with fast neutrons. Briefly, in this approach, the number of GBS reads falling within a 1-kb window in a given line was compared to the mean number observed across all lines within this same window (see Methods for details). The ratio between these two numbers was used to identify under-or over-represented genomic regions in much the same way as aCGH establishes a ratio between the hybridization signals seen in a test line and a control. Our approach for calling CNVs from the first GBS library relied on 32,741 1-kb informative bins in the dataset used for calling homozygous deletions and duplications, and 28,439 bins in the dataset used to call hemizygous deletions (Table  1 , Additional file 1: Figure  S1 ).
Two other duplications in the aCGH dataset, including an almost complete whole-chromosome duplication, were split into two in the GBS set of calls. Attempts at finding a set of parameters that minimized these minor inconsistencies proved unsuccessful (see Additional file 1). Similarly, both the homozygous and hemizygous sets of deletions found by GBS each showed one occurrence of an aCGH deletion split into two, and one occurrence of two aCGH deletions called as a single deletion by the GBS approach. Whether an event was detected by GBS was largely dependent on its size. To illustrate this, we labelled each of the CNVs identified using aCGH as having been successfully uncovered or not using GBS data and plotted their size distributions (Fig. 2 ). Except for one 120-kb deletion, GBS was able to detect all homozygous deletions larger than 70 kb, in addition to a few smaller deletions.
The size distribution of the events found in the complete dataset was similar to that reported for lines assessed by aCGH, homozygous deletions being on average smaller than hemizygous deletions or duplications (Fig. 4 ). The GBS CNV call set included exceptionally large events that exceeded previous records reported in this population by Bolon et al. [14]  and to our knowledge in any fast neutron population. We indeed found an 11. 2 Mb-long homozygous deletion (previous record 8. 1 Mb) as well as 11. 6 Mb-, 10. 4 Mb-and 10. 0 Mb-long hemizygous deletions (previous record 9. 3 Mb). We also found a near-complete chromosome duplication (larger than 50 Mb) of Chr19 in an individual, a size similar to a duplication reported by Bolon et al. [14]. Except for the 10. 4-Mb hemizygous deletion, which was found in an individual that was not assessed by aCGH, all these events were also validated by aCGH data.
A suspected 17. 6-Mb hemizygous deletion called from the GBS data of the first library was shown to actually consist of two separate events upon the addition of the sequencing data of the second library (see below). An unusual succession of duplications and hemizygous deletions was detected by both aCGH and GBS on Chr01 of an individual (Fig. 5 ), Fig. 1  Examples of aCGH and GBS log 2 ratio profiles of events detected using both methods. Vertical dotted lines mark event boundaries. The GBS log 2 ratio data are those generated from the dataset used to called homozygous deletions and duplications from the first GBS library in all three cases. a log 2 ratio profiles of a 11. 2-Mb homozygous deletion on chromosome 14 of individual FNMN0065. b log 2 ratio profiles of a 2. 3-Mb hemizygous deletion on chromosome 15 of individual FNMN0039.
This resulted in an increase from 32,741 to 47,036 of the number of informative 1-kb bins used for calling homozygous deletions and duplications, and from to 28, 439 to 36,709 for the dataset used to call hemizygous deletions. This increased resolution and sequencing depth made it possible to call two additional homozygous deletions measuring 22 and 60 kb, as well as one additional 128-kb hemizygous deletion (among the set of events identified by aCGH). The addition of this new library thus effectively brought our detection threshold for homozygous deletions close to 60 kb. The combined data of the two libraries resulted in a call set of 108 homozygous deletions, 36 hemizygous deletions and 20 duplications among the 79 mutant lines assessed (Additional file 3).
CNV calling with GBS: a proof of concept The first objective of our study was to develop and optimize an approach for CNV discovery in fast neutron populations from standard GBS data. Our results demonstrate that GBS can indeed be used as a high-throughput and low-cost approach for CNV assessment in fast neutron mutants. Using a pipeline specifically designed for this purpose, we were able to detect over 150 events among 79 fast neutron mutants, including exceptionally large or complex events beyond what had been previously reported in the same fast neutron population. Validation using aCGH data allowed us to quantify the performance of the GBS approach and optimize parameters such that a very low false discovery rate could be attained. Although validation using PCR and ddPCR assays did reveal a few false positive calls, these likely represented almost all false positive calls in our dataset given that we focused on the smallest events, which are the most difficult to detect.