dataesr/scientific-paragraphs-categorization huggingface dataset:
melanogaster SNP data set presented here. The mapping pipeline includes the following major steps. Prior to mapping, we removed sequencing adapters and trimmed the 3 0 ends of all reads using cutadapt DEST Population Genomics. doi:10. 1093/molbev/msab259 MBE  (Martin 2011). We enforced a minimum base quality score !18 (-q flag in cutadapt) and assessed the quality of raw and trimmed reads with FASTQC. Trimmed reads with minimum length <75 bp were discarded and only intact read pairs were considered for further analyses. Overlapping paired-end reads were merged using bbmerge  (v. 35. 50; Bushnell et al. 2017). Trimmed reads were mapped against a compound reference genome ("hologenome") consisting of the genomes of D. melanogaster (v. 6. 12) and D. simulans  (Hu et al. 2013)  as well as genomes of common commensals and pathogens, including Saccharomyces cerevisiae (GCF_ 000146045.
2), Wolbachia pipientis (NC_002978. 6), Pseudomonas entomophila (NC_008027. 1), Commensalibacter intestine (NZ_AGFR00000000. 1), Acetobacter pomorum (NZ_ AEUP00000000. 1), Gluconobacter morbifer (NZ_AGQV000 00000. 1), Providencia burhodogranariea (NZ_AKKL0000 0000. 1), Providencia alcalifaciens (NZ_AKKM01000049. 1), Providencia rettgeri (NZ_AJSB00000000. 1), Enterococcus faecalis (NC_004668. 1), Lactobacillus brevis (NC_008497. 1), and Lactobacillus plantarum (NC_004567. 2), using bwa mem (v. 0. 7. 15; Li 2013) with default parameters. We retained reads with mapping quality greater than 20 as well as those with no secondary alignment using samtools  (Li et al. 2009). PCR duplicate reads were removed using Picard MarkDuplicates (v. 1. 109; http://broadinstitute. github. io/picard/, last accessed September 6, 2021).
Incorporation of the DGN Data Set We incorporated population allele frequency estimates derived from inbred line and haploid embryo sequencing data from populations sampled throughout the world using an in silico pooling approach. These samples have been previously collected and sequenced by several groups  (Langley et al. 2012; Mackay et al. 2012; Pool et al. 2012; Grenier et al. 2015; Kao et al. 2015; Lack et al. 2015 Lack et al. , 2016) )  and together form the Drosophila Genome Nexus data set (DGN;  Lack et al. 2015 Lack et al. , 2016)). We included 25 DGN populations with !5 individuals per population, plus the D. simulans haplotype w 501 built as part of the DGN data set. The DGN populations that we used are primarily from Africa (n ¼ 18) but also include populations from Europe (n ¼ 2), North America (n ¼ 3), Australia (n ¼ 1), and Asia (n ¼ 1).
We used liftover  (Kuhn et al. 2013)  to translate genome coordinates to Drosophila reference genome release 6 (dos  Santos et al. 2015)  and formatted them to match the gSYNC format (described below). Scripts for reformatting the DGN data can be found in the GitHub repository for this project (https://github. com/DEST-bio/ DEST_freeze1, last accessed September 6, 2021).
SNAPE-pooled is expected to be more sensitive to rare private polymorphisms  (Raineri et al. 2012; Guirao-Rico and Gonz alez 2021). The SNP calling step is built using the snakemake (Mo ¨lder et al. 2021) pipeline and the parameters to run the two callers can be found at https://github. com/DEST-bio/DEST_freeze1 (last accessed September 6, 2021).
gSYNC Generation and Filtering Our pipeline utilizes a common data format to encode allele counts for each population sample (SYNC;  Kofler, Pandey, et al. 2011). A "genome-wide SYNC" (gSYNC) file records the number of A, T, C, and G for every site of the reference genome. Because gSYNC files for all populations have the same dimension, they can be quickly combined and passed to a SNP calling tool. They can be filtered and are also relatively small for a given sample ($500 Mb), enabling efficient data sharing and access. The gSYNC file is analogous to the gVCF file format as part of the GATK HaplotypeCaller approach  (McKenna et al. 2010)  but is specifically tailored to Pool-Seq samples. We generated gSYNC files for both PoolSNP and SNAPE. To generate a PoolSNP gSYNC file, we first converted BAM files to the MPILEUP format with samtools mpileup using the -B parameter to suppress recalculations of per-base alignment  Kapun et al.
doi:10. 1093/molbev/msab259 MBE qualities and filtered for a minimum mapping quality with the parameter -q 25. Next, we converted the MPILEUP file containing mapped and filtered reads to the gSYNC format using custom python scripts. To generate a SNAPE-pooled gSYNC file, we ran the SNAPE-pooled version specific to Pool-Seq data for each sample in MPILEUP format with the following parameters: h ¼ 0. 005, D ¼ 0. 01, prior¼'informative', fold¼'unfolded', and nchr¼number of flies (x2 for autosomes and x1 for the X and Y chromosomes) following Guirao-Rico and Gonz alez (2021). We converted the SNAPE-pooled output file to a gSYNC file containing the counts of each allele per position and the posterior probability of polymorphism as defined by SNAPE-pooled using custom python scripts. We only considered positions with a posterior probability !0.
9 as being polymorphic and with a posterior probability 0. 1 as being monomorphic. In all other cases, positions were marked as missing data. We masked gSYNC files for PoolSNP and SNAPE-pooled using a common set of filters. Sites were filtered from gSYNC files if they had: 1) minimum read depth <10; 2) maximum read depth >the 95% coverage percentile of a given chromosomal arm and sample; 3) located within repetitive elements as defined by RepeatMasker; 4) within 5-bp distance up-and downstream of indel polymorphisms identified by the GATK IndelRealigner. Filtered sites were converted missing data in the gSYNC file. The location of masked positions for every sample was recorded as a BED file.
VCF Generation We generated three versions of the variant files, which differ in their inclusion of the DGN samples and the SNP calling strategy. For PoolSNP variant calling, we generated two variant tables: the first version incorporates all 272 samples of the  Pool-Seq (DrosRTEC, DrosEU)   To generate the PoolSNP versions, we combined the masked PoolSNP-gSYNC files into a two-dimensional matrix, where rows correspond to each position in the reference genome and columns describe chromosome, position, and reference allele, followed by allele counts in SYNC format for every sample in the data set. This combined matrix was then subjected to variant calling using PoolSNP, resulting in a VCFformatted file. We performed SNP calling only for the major chromosomal arms (X, 2L, 2R, 3L, 3R) and the 4th (dot) chromosome.
Data for heterochromatic arms of the autosomes, the Y chromosome, and the mitochondrial genome can be extracted from the MPILEUP files provided at https://dest. bio (last accessed September 6, 2021). We evaluated the choice of two heuristic parameters applied to PoolSNP: global MAC and global MAF. Using all 272 samples, we varied MAF (0. 001, 0. 01, 0. 05) and MAC (5-100) and called SNPs at a randomly selected 10% subset of the genome. Based on SNP annotations with SNPeff (version 4. 3; Cingolani et al. 2012), we calculated p N /p S , which is the ratio of nonsynonymous to synonymous polymorphisms, and used this value to tune our choice of MAF and MAC and to identify egregious outlier samples. We found that a global MAC ¼ 50 provided qualitatively identical estimates of p N /p S across all populations (fig.
2B ) and that the results were insensitive to MAF (results not shown). We therefore used these parameters for genome-wide variant calling (see Identification and Quality Control of SNP Polymorphisms). We kept a third heuristic parameter, the missing data rate, constant at a minimum of 50%. To generate the SNAPE-pooled VCF files, we combined the 246 masked SNAPE-pooled gSYNC files into a twodimensional matrix, as described above, and generated a VCF formatted output based on allele counts for any site found to be polymorphic in one or more populations. We evaluated p N /p S across a range of local MAF thresholds (fig. 2C ) and found that p N /p S is largely insensitive to local MAF, once accounting for some problematic samples (see below). Final VCF files with annotations from SNPeff (version 4.
We only considered sites identified as being polymorphic by PoolSNP or SNAPE-pooled for analysis, using the -snpfile option of npstat. For the DGN populations, chromosome-wide summary statistics were estimated only for samples with less than 50% missing data per chromosome. Due to small sample sizes, Tajima's D was not estimated for seven African DGN populations that consisted of only five haploid embryos. To compare population genetic estimates between the PoolSNP versus SNAPE-pooled data sets, we performed Pearson's DEST Population Genomics. doi:10. 1093/molbev/msab259 MBE correlation on 226 populations present in both data sets (see Identification and Quality Control of SNP Polymorphisms) using the stats package of R v. 3. 6. 3. The effects of pool size (number of individuals sampled per population) on genomewide estimates of p, Watterson's h and Tajima's D S estimates were examined for European and North American populations using the PoolSNP data set and a linear model in R v.
3. 6. 3. Finally, for 48 European populations we estimated Pearson's correlations between p, Watterson's h and Tajima's D as estimated from the PoolSNP data set versus previous estimates by  Kapun et al. (2020)  using the stats package of R v3. 6. 3. Next, we examined patterns of between-population differentiation by calculating window-wise estimates of pairwise F ST , based on the method from  Hivert et al. (2018)  implemented in the computePairwiseFSTmatrix() function of the R package poolfstat (v1. 1. 1). This analysis was performed for the data set composed of 271 samples (all samples excluding the D. simulans reference strain) processed with PoolSNP, focusing on SNPs shared across the whole data set. Finally, we averaged pairwise F ST within and among phylogeographic clusters identified in our analyses: Africa (17 samples), North America (76 samples), Eastern Europe (83 samples), and Western Europe (93 samples).
Acknowledgments We thank four reviewers and the handling editor for helpful comments on previous versions of our manuscript. We are grateful to the members of the  DrosEU and DrosRTEC consortia  for their long-standing support, collaboration, and for discussion. DrosEU was funded by a  Special Topic Networks (STN) grant  from the  European Society for Evolutionary Biology (ESEB). M. K. was supported by the  Austrian Science Foundation  (grant no. FWF P32275 ); J. G. by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  ( H2020-ERC-2014-CoG-647900 ) and by the  Spanish Ministry of Science and Innovation  ( BFU-2011-24397 ); T. F. by the  Swiss National Science Foundation  (SNSF grants  PP00P3_133641 ,  PP00P3_165836 , and  31003A_182262 ) and a  Mercator Fellowship  from the  German Research Foundation  (DFG), held as a EvoPAD Visiting Professor at the  Institute for Evolution and Biodiversity, University of Mu  ¨nster; AOB by the  National Institutes of Health  ( R35 GM119686 ); M.
Data Availability All scripts to make figures and perform analyses associated with this manuscript are available at: https://github. com/ DEST-bio/data-paper (last accessed September 6, 2021). All scripts to build the data set, including the mapping pipeline, SNP calling scripts, and metadata are available at: https://github. com/DEST-bio/DEST_freeze1 (last accessed September 6, 2021). All output from the DEST pipeline, including intermediate output files, metadata, etc. can be found at: https://dest. bio (last accessed September 6, 2021). Datafiles available via the website can also be downloaded through the command-line interface. The genome browser associated with the DEST data set can be found at: http://dest-bio. uab. cat, last accessed September 6, 2021. The dockerized mapping pipeline can be found at https://hub.
, 2009). The main objective of this study was to identify demographic and environmental drivers of FSGS across the species range of silver fir, as well as comparing FSGS across seedling and adult cohorts and assessing population variation in dispersal parameters. Silver fir is a wind pollinated and dispersed, monoecious, mixed-mating but generally outcrossing species  (Wolf, 2003) , thus we expected to find an overall weak FSGS across the range. We studied natural silver fir populations with no or close-to-nature management, so we assumed that differences in FSGS were due to demographic history and/or environmental constraints/adaptation. We assembled published single nucleotide polymorphism (SNP) marker data for adult trees  (Brousseau et al. , 2016; Heer et al. , 2018; Roschanski et al. , 2016)  from eight sites sampled across the species range.
| Environmental variables The habitat at each site was characterized using a combination of climate, soil, and species distribution data. Downscaled historical climate data at 1 km resolution was obtained from the CHELSAcruts time-series  (Karger et al. , 2017 ; https://chels a-clima te. org/chels acrut s/) from 1901 to 1990. Monthly mean, minimum, and maximum temperatures and monthly precipitation were extracted and used to calculate the annual mean temperature, isothermality, and annual total precipitation using the R package dismo  (Hijmans et al. , 2017). We calculated only these climatic variables because they have been shown to drive adaptation in silver fir (e. g. ,  Csilléry et al. , 2020; Gazol et al. , 2015) , and we had to limit the number of variables to be tested because of the limited number of observations (16 populations).
Soil available water capacity (AWC) was extracted at a 250 m resolution from the SoilGrids data set  (Hengl et al. , 2017). Measures of tree species composition were derived from the EU-Forest, which is a 1 km resolution tree occurrence database for Europe  (Mauri et al. , 2017). The occurrences of 14 tree species within areas of 0. 3 radius circles (~33 km) around each study populations were extracted.
| Statistical analyses First, we assessed genetic diversity parameters and IBD patterns across the species range. We characterized genetic diversity and divergence at the level of sites and populations. Expected heterozygosity (H E ) and observed heterozygosity (H O ) were calculated for each site, population and cohort (i. e. , adults or seedlings) using SpaGeDi 1. 5b  (Hardy & Vekemans, 2002) , as well as pairwise F ST among sites, between low and high elevation population pairs and between cohorts. Statistical significance of pairwise F ST values was tested by 10,000 permutations. We performed a hierarchical analysis of molecular variance (AMOVA) to determine the partition of the genetic variation within and among sites and populations using the R package pegas  (Paradis, 2010). We applied discriminant analysis of principal components (DACP) to identify genetic clusters  (Jombart et al.
, 2010)  implemented in the R package adegenet  (Jombart, 2008). DACP identifies synthetic variables, so called Linear Discriminants (LD), that differentiate among a priori defined groups (here, populations), while minimizing variation within inferred genetic clusters. We used the function Xval. dapc to find the optimal number of principal components (PCs) to be retained, and find. clusters to find the F I G U R E 1 (a) Location of the study sites (main map, yellow triangles) and distribution of silver fir (Abies alba Mill. ) (dark green). Small maps show the position of the high and low elevation populations within each site. Site names are abbreviated using three letter codes; see Table  1  for full names. (b) Genetic clusters across the study sites inferred using a discriminant analysis of principal components (DACP) and the R package adegenet  (Jombart, 2008)  optimal number of clusters based on the Bayesian information criterion (BIC).
, 2002 (Liepelt et al. , , 2009)). Second, FSGS was characterized using two methods, the Sp statistic  (Vekemans & Hardy, 2004 ) and multisite spatial autocorrelation analyses  (Smouse et al. , 2008). Sp is estimated from a regression of pairwise kinship coefficients against the logarithm of pairwise spatial distances as -b-log/(1-F 1 ), where b-log is the slope of the regression and F 1 is the average kinship coefficient among individuals of the first distance class  (Vekemans & Hardy, 2004). We estimated Sp for each population and each age cohort using the software SpaGeDi 1. 5 b. We used the pairwise kinship coefficient (F ij ) of  Loiselle et al. , (1995) , both with even distance classes (20 m) and even sample sizes per distance class. The spatial extent of FSGS was estimated as the distance where F ij was significant with the even sample size method, as this option ensured that an adequate number of individuals were placed in each distance class.
The resampling was performed by applying three strategies: (1) resampling randomly within the sampling area, (2) reducing the size of the sampling area, and (3) resampling along linear transects. The first resampling approach was performed selecting 50 random individuals 20 times using R (R Core Team, 2019). The second resampling approach was carried out in the program QGIS  (QGIS Development Team, 2016) , where equal-sized (0. 45 ha for VEN_H and 0. 65 ha for VEN_L), choosing 20 circles with random centres placed within the central area of the population, so that the whole circle could always be inside the population. Finally, the third resampling approach required sampling across 20 linear transects, TA B L E 1 Geolocalization of the eight sampling sites, sample sizes, and the maximum distance between sampled adult trees in metres.
At each site, a low and a high population was sampled. At three sites seedlings were also sampled and their sampled sizes are in parenthesis randomly drawn across each population using the R packages "sf"  (Pebesma, 2018) , "sp"  (Pebesma & Bivand, 2005) , and raster  (Hijmans et al. , 2015). The maximum and minimum coordinates within each population were used to create the transect, and then the transect was rotated 20 times by 9° relative to the centre of the transect. FSGS analyses of the resampled data sets were carried out using an in-house python script that automated SpaGeDi, and using the even sample size option. We tested whether the resampling would have changed our conclusions in terms of the strength and extent of FSGS using one sample t test, assuming that the estimates from the full data sets were the true values.
Finally, we assessed the effect of sampling using all populations. For this, we calculated the correlation between summary statistics of FSGS (F 1 , b-log, Sp) and summaries of the sampling design, such as the area of the sampling site (m 2 ), the maximum distance among the furthest individuals (m), and the density of sampling (number of samples/area of the sampling site) using R. Fourth, we used a structural equation modelling (SEM) approach to identify the potential demographic and environmental drivers of FSGS using the lavaan R package  (Rosseel, 2011). Since we had only 16 populations, we were able to test a limited number of variables. To partially circumvent this problem, we defined two models. In the first model, we tested the effect of demography. We included synthetic variables from the DAPC analyses (i.
05), the comparative fit index (CFI > 0. 9), Bayesian information criterion, standardized root mean square residual (SRMR < 0. 06) and root mean square error of approximation (RMSEA < 0. 05). Fifth, we tested for the presence and strength of FSGS in seedlings using the same method as described for the adult trees at the three sites where data for seedlings were available (APE, BAV, and FAG). We also tested for the heterogeneity in correlation of allele frequencies between cohorts  (Smouse et al. , 2008). Seedlings from FAG were excluded from these analyses because regeneration was limited to four patches in FAG_H and one patch in FAG_L (see Figure  S1 ). In order to estimate dispersal parameters, we carried out parentage analysis. We used the likelihood-based approach implemented in the software CERVUS 3.
The low genetic diversity of these populations are most probably the results of genetic drift due to founder effects, long term isolation or population size reduction due to recurring unfavourable environmental conditions (Excoffier F I G U R E 2 Fine-scale spatial genetic structure (FSGS) across 16 silver fir (Abies alba Mill. ) populations based on 137 SNPs using the software GenAlEx 6. 5  (Peakall & Smouse, 2012). Correlation coefficients (r) are plotted against the maximum geographical distance between individuals in each distance class. Shaded areas represent the 95% confidence interval obtained through random shuffling of individual geographic locations (1,000 times). Vertical bars around mean r values represent 95% confidence intervals generated by bootstrapping (10,000 times) pairwise comparisons within each distance class.
, 2008; Vekemans & Hardy, 2004). Although the global test of heterogeneity in spatial autocorrelation of allele frequencies did not show evidence for variation in FSGS across the range, we argue that this result appeared rather conservative given the strong population differences in Sp values and their clear link to demography (Table  2 , Figure  4 ). Unfortunately, little information is available about the performance of this test across a large number of populations. We found that a resampling experiment on our own data can be recommended for understanding the limitations of a data set. The main conclusion ) population pairs, comprising adult and seedling cohorts, using the software GenAlEx 6. 5  (Peakall & Smouse, 2012). Correlation coefficients (r) were plotted against the maximum geographical distance between individuals in each distance class.
DATA AVA I L A B I L I T Y S TAT E M E N T The following data have been made available on Dryad (https:// doi. org/10. 5061/dryad. 7d7wm 37vm): Georeferenced adult and seedling SNP genotype data, coordinates used for the resampling study in VEN, the diameter at breast height (DBH) of adult trees (sites APE, BAV and FAG), individual coordinates of the resampled data sets (site VEN), occurrence of 14 tree species at the study sites derived from the EU-Forest database, input variables for the SEM analysis (Sp values, raw climatic variables from CHELSAcruts, AWC, elevation, tree species composition PCs, variables derived from the DAPC analysis), R script for the DACP analysis, R script for the SEM analyses, R script for the transect resampling have been placed in Zenedo (https://doi. org/10. 5281/ zenodo.
isolates along with metadata. The most frequent pathogens, i. e. , M. bovis and species belonging to M. mycoides group, show stable clinicoepidemiological trends and were included for annual AST. In the absence of interpretative criteria for ruminant mycoplasmas, we compared yearly minimum inhibitory concentration (MIC) results against reference datasets. We also ran a SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis on the overall service provided by our AMR surveillance strategy. Results of the 2018-2019 surveillance campaign were consistent with the reference datasets, with M. bovis isolates showing high MIC values for all antimicrobial classes except fluoroquinolones, and species of the Mycoides group showing predominantly low MIC values. A few new AMR patterns were detected, such as M.
bovis with lower spectinomycin MICs. Our reference dataset partially complied with European Committee on Antimicrobial Susceptibility Testing (EUCAST) requirements, and we were able to propose tentative epidemiological cut-off values (TECOFFs) for M. bovis with tilmicosin and spectinomycin and for M. mycoides group with tilmicosin and lincomycin. These TECOFFs were consistent with other published data and the clinical breakpoints of Pasteurellaceae, which are often used as surrogates for mycoplasmas. SWOT analysis highlighted the benefit of pairing clinical and antimicrobial resistance surveillance despite the AST method-related gaps that remain. The international community should now direct efforts toward AST method harmonization and clinical interpretation.