dataesr/scientific-paragraphs-categorization huggingface dataset:
We evaluated these three metrics across a 2x2 filtering scheme: two MAF filters (0. 001, 0. 05) and two sample sets (the whole dataset of 246 samples; and the 226 samples that passed the We also compared AF estimates between the two callers using the dataset of 226 populations applying a local MAF filter of 0. 05 in the SNAPE-pooled dataset (see Supplementary Material online, supplementary table  S2 ). Among the positions identified as polymorphic by both calling methods, our frequency estimates were identical for the great majority of SNPs (92%-99. 67%) in all samples analyzed. Between 0. 1% and 7. 1 % of the polymorphic SNPs differed by less than 5% frequency between the two methods, 0. 003 to 2. 1% of polymorphic SNPs differed by 5%-10% frequency and only up to 0. 3% varied >10% frequency (supplementary table 4).
0052;  Kapun et al. 2020). Estimates of Tajima's D were positive when using PoolSNP, and slightly negative using SNAPE. These results are expected given biases in the detection of rare alleles between these two SNP calling methods. In addition, our estimates for π, Watterson's θ and Tajima's D were positively correlated with previous estimates for the 48 European populations analyzed by  Kapun et al. (2020)  (all p<0. 01). Notably, slightly lower levels of Tajima's D in North America as compared to both Africa and Europe (Figure  6C ) may be indicative for admixture  (Stajich and Hahn 2005)  which has been identified previously along the North American east coast  (Caracristi and Schlötterer 2003; Kao et al. 2015; Bergland et al. 2016). Phylogeographic clusters in D. melanogaster. We performed PCA on the PoolSNP variants using samples from the North American (DrosRTEC), European (DrosEU), and African (DGN) datasets (excluding all Asian and Oceanian samples).
Prior to analysis we filtered the joint datasets to include only high-quality biallelic SNPs. Because LD decays rapidly in Drosophila  (Comeron et al. 2012) , we only considered SNPs at least 500 bp away from each other. PCA on the resulting 100,000 SNPs revealed evidence for discrete phylogeographic clusters that correspond to geographic regions (Supplementary Material online, supplementary fig. S14B ). PC1 (24% variance explained  [VE] ) partitions samples between Africa and the other continents (Figure  7A ). PC2 (9% VE) separates European from North American populations, and both PC2 and PC3 (4% VE) divide Europe into two population clusters (Figure  7B ). As expected, North American samples are intermediate to European and African samples, presumably due to recent secondary contact  (Kao et al.
Probability that all populations containing a polymorphic site come from the same phylogeographic cluster (as defined by principal component space, Figure  7  and supplementary fig. 14 ). The y-axis is the probability of "x" populations belonging to the same phylogeographic cluster. The axis only shows up to 60 populations since, after 40 populations, the probabilities approach 0. The colors are consistent across panels. Geographically-informative markers. An inherent strength of our broad biogeographic sampling is the potential to generate a panel of core demography SNPs to investigate the provenance of current and future samples. We created a panel of geographically-informative markers (GIMs) by conducting a discriminant analysis of principal components (DAPC) to discover which loci drive the phylogeographic signal in the dataset.
This behavior is consistent for both AF discretization methods (binomial and counts). PoolSNP results also vary as a function of the AF discretization method. Based on these results, we chose to use only PoolSNP data for the implementation of our demographic inference. To further evaluate the behavior of PoolSNP's estimates as a function of the AF discretization method, we explored values of the raw parameter outputs by moments. We explored the values of the  " parameter (the ancestral population size; see Materials and Methods). In general, the counts method produced  " estimates which are sparser and less stable, by an order of magnitude, relative to binomial draws ( "  1"234 = 0. 938,  "  53627! = 3. 72). In addition,  " generated from the counts method produce highly skewed distributions, particularly for jSFS estimated for population pairs in eastern Europe (Figure  10B ).
I. = 225 y to 362 y; median = 231 y). The western cluster has a mean divergence within populations of 648 y (95% C. I. = 627 y to 668 y; median = 626 y). We evaluated the relationship between spatial distance and divergence time. Similar to our proximity analysis (Figure  8 ), the biological expectation is that populations in close proximity are likely to display low divergence estimates. Our results fit with this expectation, with neighboring populations within clusters displaying low divergence estimates (Figure  11B ). Lastly, we estimated other population genetic parameters of these population clusters such as effective population size (NE) and migration rates (M). Kapun et al. 2020). We assembled this dataset using two SNP calling strategies that differ in their ability to identify rare polymorphisms, thereby enabling future work studying the evolutionary history of this species.
We are dubbing this data repository and the supporting bioinformatics tools Drosophila Evolution over Space and Time (DEST). The DEST data repository was built using two different SNP calling pipelines, SNAPEpooled  (Raineri et al. 2012)  and PoolSNP  (Kapun et al. 2020). These two methods differ fundamentally in their approach to SNP identification, yield datasets amenable to different types of analyses and each approach has its own specific limitations. The fundamental difference between the datasets produced by these methods is the number of rare and endemic SNPs identified. This difference will result in biased estimates of parameters from site frequency spectrum based demographic models. As a consequence, some care should be taken when interpreting different analyses based on these datasets.
SNAPE-pooled treats each Pool-Seq sample separately and calculates the posterior probability that a site is polymorphic based on read depth, alternate allele count, and a prior estimate of nucleotide diversity; this approach was designed to identify rare polymorphisms and has been validated using both simulations and empirical approaches (Guirao-Rico and Gonzalez 2021). Here, we also provide evidence that rare and private SNPs identified by SNAPE-pooled are enriched for true positives (Figure  8 ) after applying rigorous filtering and excluding 20 population samples likely affected by problems during library preparation which may have resulted in elevated error rates. The dataset based on SNAPE-pooled could therefore be useful for studies that rely on rare SNPs, such as those investigating recent demographic events  (Keinan and Clark 2012  problem is apparent when comparing the number of polymorphisms identified by SNAPEpooled and PoolSNP (Figure  3 ).
Third, any demographic inference done with SNAPE must be limited to cases where a SNP is discovered in at least three populations or more, since the caller appears to produce too many false positives when only two populations are considered (see Figure  8B  and our demographic inference with moments, which uses a pairwise, twopopulation, model). In addition, studies that rely on the SNAPE-pooled dataset should exclude the 20 samples we flagged here (Figure  2A , Supplementary Material online, supplementary table  S1 ). PoolSNP, on the other hand, is useful for analysis of common variants and allows studying aspects of population structure and local adaptation based on shared polymorphism. Such analyses could include the inference of migration out of Africa (Kapopoulou et al. 2020), admixture  (Bergland et al.
2016) , and back-migration to Africa  (Pool and Aquadro 2006). PoolSNP is an extension of the approach developed elsewhere  (Kofler et al. 2011a,b; Kapun et al. 2020)   S15 , and discussed in the Results). Thus, there does not appear to be significant bias when combining these datasets, at least when integrating information across the genome. Nonetheless, some care should be taken when interpreting allele frequency differences based on datasets generated by different means. However, any real-time monitoring activity will likely suffer from the rapidly changing landscape of sequencing technologies. One of the biggest challenges in the present "omics" era is the rapidly growing number of complex large-scale datasets which require technically elaborate bioinformatics know-how to become accessible and utilizable.
This hurdle often prohibits the exploitation of already available genomics datasets by scientists without a strong bioinformatics or computational background. To remedy this situation for the Drosophila evolution community, our bioinformatics pipeline is provided as a Docker image (to standardize across software versions, as well as make the pipeline independent of specific operating systems) and a new genome browser makes our SNP dataset available through an easy-to-use web interface (see Supplementary Material online, supplementary figures S2, S3; available at https://dest. bio). The DEST data repository and platform will enable the population genomics community to address a variety of longstanding, fundamental questions in ecological and evolutionary genetics. The current dataset might for instance be valuable for providing a more accurate picture of the demographic history of D.
melanogaster populations, in particular in Europe and North America, and with respect to multiple bouts of out-of-Africa migration and recent patterns of admixture. Such analyses can be strongly affected by chromosomal inversions that are known to impact LD and haplotype variation  (Kapun and Flatt 2019; Durmaz et al. 2020). We have therefore provided frequency estimates for the seven most common cosmopolitan inversions (In(2L)t, In(2R)NS, In(3L)P, In(3R)C, In(3R)K, In(3R)Mo and In(3R)Payne; Lemeunier and Aulard 1992), which allows accounting for the effects of inversions in population genetic inference (e. g. ,  Kapopoulou et al. 2020). The DEST dataset will likewise be useful for an improved understanding of the genomic signatures underlying both global and local adaptation, including a more fine-grained view of selective sweeps, their evolutionary origin and distribution (e.
g. , see  Glinka et al. 2003; Beisswanger et al. 2006; Ometto 2010; Stephan 2016; Kapun et al. 2020). In terms of local adaptation, the broad spatial sampling across latitudinal and longitudinal gradients on the North American and European continents, encompassing a broad range of climate zones and areas of varying degrees of seasonality, will allow examining the parallel nature of local (clinal) adaptation in response to similar environmental factors in greater depth than possible before (e. g. ,  Turner et al. 2008; Kolaczkowski et al. 2011; Fabian et al. 2012; Reinhardt et al. 2014; Bergland et al. 2014 Bergland et al. , 2016;; Kapun et al. 2016 Kapun et al. , 2020;; Bogaerts-Márquez et al. 2020; Waldvogel et al. 2020; Machado et al. 2021). Another major opportunity provided by the DEST dataset lies in studying the temporal dynamics of evolutionary change.
While our analyses of the DEST sequencing data already led to novel insights into the evolutionary history of Drosophila, we believe that the real value of the DEST dataset lies in the future: its long-term utility will grow as natural and experimental populations are continually being sampled, re-sequenced and added to the repository by the community of Drosophila evolutionary geneticists. The pipeline that we have established will make future updates to the data repository straightforward. Furthermore, since it is not easily feasible for any single research group to sample flies densely through time and across a broad geographic range, the growing value of the DEST dataset will depend upon the synergistic collaboration among research groups across the globe, as exemplified by the DrosRTEC and DrosEU consortia.
Due to small sample sizes, Tajima's D was not estimated for 7 African DGN populations that consisted of only 5 haploid embryos. To compare population genetic estimates between the PoolSNP versus SNAPE-pooled datasets, we performed Pearson's correlation on 226 populations present in both datasets (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 genome-wide estimates of π, Watterson's θ and Tajima's DS estimates were examined for European and North American populations using the PoolSNP dataset and a linear model in R v. 3. 6. 3. Finally, for 48 European populations we estimated Pearson's correlations between π, Watterson's θ and Tajima's D as estimated from the PoolSNP dataset 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 windowwise estimates of pairwise FST, 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 dataset composed of 271 samples (all samples excluding the D. simulans reference strain) processed with PoolSNP, focusing on SNPs shared across the whole dataset. Finally, we averaged pairwise FST 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). Samples from China and Australia were not included due to limited sampling. These FST tracks at windows sizes of 100kb, 50kb and 10kb are available at https://dest.
Blue lines in both panels show average genome-wide pN/pS ratios across 271 and 246 populations, respectively. The blue ribbons depict the corresponding standard deviations. The 20 outlier samples, which are characterized in panel A, are highlighted red. In addition, pN/pS ratios of the DGRP Pool-Seq sample (NC_ra_03_n) are shown at different cut-offs as green diamonds and the empirical values from the DGRP freeze2 dataset are indicated as dashed green lines.
Downloaded from https://academic. oup. com/mbe/advance-article/doi/10. 1093/molbev/msab259/6361628 by Consejo Superior de Investigaciones Cientificas (CSIC) user on 28 September 2021 clusters harbor statistically different levels of genetic variation (T-test; t-value = -5. 22, degreesof-freedom [df] = 332. 96, p = 3. 1010 %/ ), thus suggesting potentially different demographic histories. We tested whether the split time between eastern and western D. melanogaster populations was older than within clusters, and whether split time was positively correlated with geographic distance. Prior to addressing this hypothesis, we first evaluated the behavior of the PoolSNP and SNAPE-pooled datasets in demographic inference and also evaluated different methods for converting Pool-Seq data for use with site frequency spectrum-based analysis.
Downloaded from https://academic. oup. com/mbe/advance-article/doi/10. 1093/molbev/msab259/6361628 by Consejo Superior de Investigaciones Cientificas (CSIC) user on 28 September 2021 options from the Tools menu (supplementary fig. S2C). All data, tools, and supporting resources for the DEST dataset, as well as reference tracks downloaded from FlyBase (v. 6. 12)(dosSantos et al. 2015), are freely available at https://dest. bio.
(Mackay et al 2012)ps://academic. oup. com/mbe/advance-article/doi/10. 1093/molbev/msab259/6361628 by Consejo Superior de Investigaciones Cientificas (CSIC) user on 28 September 2021Collection dates for localities sampled more than once. (C)Generalsamplefeatures of the DEST dataset. The x-axis represents the population sample, ordered by the average read depth. In order to determine appropriate SNP calling and filtering parameters, and to identify potentially problematic population samples, we first calculated the ratio of the number of non-synonymous polymorphisms to the number of synonymous polymorphisms (pN/pS) for each population sample across the whole genome. Since non-synonymous changes are expected to be under strong purifying selection(Kreitman 1983), the pN/pS metric can reflect the presence of sequencing errors that would disproportionately inflate pN relative to pS.
Our primary goal was not to provide novel estimates of pN/pS but rather to ensure that all population samples have estimates that are consistent with estimates generated from independent Drosophila datasets(Mackay et al 2012). Downloaded from https://academic. oup. com/mbe/advance-article/doi/10. 1093/molbev/msab259/6361628 by Consejo Superior de Investigaciones Cientificas (CSIC) user on 28 September 2021 ratios and numbers of private SNPs (Supplementary Material online, supplementary table.
Downloaded from https://academic. oup. com/mbe/advance-article/doi/10. 1093/molbev/msab259/6361628 by Consejo Superior de Investigaciones Cientificas (CSIC) user on 28 September 2021 (red distributions), we show the distributions of correlations estimated with random population pairs (green distributions). Watterson's θ, and Tajima's D, respectively. Higher values of genetic diversity were obtained for the SNAPE-pooled dataset, probably due to its higher sensitivity for detecting rare variants (see Patterns of polymorphism between PoolSNP and SNAPE-pooled).
PoolSNP necessarily has a limited capacity to identify rare and private SNPs because it imposes global minor allele count and allele frequency filters. Therefore, the more populations that are used for SNP calling by PoolSNP, the less likely PoolSNP is to identify Lack et al. 2016), a dataset that amalgamates whole-genome sequencing of inbred line data and haploid embryos from samples collected around the world. Although the DGN data was originally generated by multiple labs and run through a different mapping pipeline than what we used for the Pool-Seq data, these samples appear to cluster tightly with geographically close Pool-Seq samples (Supplementary Material online, supplementary figure.
Li and Stephan 2006;Hutter et al. 2007;Laurent et al. 201120)iSchlötterer et al. 2014;Flatt 2020onsejoSuperior de Investigaciones Cientificas (CSIC) user on 28 September 2021 2017;Machado et al. 2021)and evolutionary responses to climate change (e. g. ,Umina 2005;Rodríguez-Trelles et al. 2013;Waldvogel et al. 2020). Moreover, by integrating these worldwide estimates of allele frequencies, those from laband field-based 'evolve and resequence' experiments (E&R;Turner et al. 2011; reviewed in     Kofler and Schlötterer 2014;Schlötterer et al. 2014;Flatt 2020) and those from mesocosm experiments (e. g. ,Rudman et al. 2019;Erickson et al. 2020), we might be able to gain deeper insights into the genetic basis and evolutionary history of variation in fitness components (e. g. ,Flatt 2020). In addition to analyses of selection, the DEST dataset can also be used for preliminary demographic inference.
While Pool-Seq datasets lack important haplotype information, they have been successfully used in the past to generate demographic and biogeographic insights into both model and non-model species (e. g. ,Gautier et al. 2021;Machado et al. 2021;Nunez et al. 2021; see Figure7). Our analyses suggest that Pool-Seq data can be used for demographic model inference. A major caveat in this endeavor is that, to the best of our knowledge, Pool-Seq has not been exhaustively benchmarked for demographic inference. As such, and until proper validation has been completed, we present our results as tools for hypothesis generation and exploration. Our results from moments are in full agreement with basic biological expectations. For example, our estimates of  are concordant with previously reported values (~0.
Acknowledgements 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 is funded by a  Special Topic Networks (STN) grant  from the  European Society for Evolutionary Biology (ESEB). MK (M. Kapun) was supported by the  Austrian Science Foundation  (grant no. FWF P32275 ); JG 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 ); TF 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 Münster ; AOB by the  National Institutes of Health  ( R35 GM119686 ); MK (M.
Data availability All scripts to make figures and perform analyses associated with this manuscript are available here: https://github. com/DEST-bio/data-paper. All scripts to build the dataset, including the mapping pipeline, SNP calling scripts, and meta-data are available here: https://github. com/DEST-bio/DEST_freeze1. All output from the DEST pipeline, including intermediate output files, metadata, etc. can be found here: https://dest. bio. Datafiles available via the website can also be downloaded through the command-line interface. The genome browser associated with the DEST dataset can be found here: https://dest-bio. uab. cat. The dockerized mapping pipeline can be found at https://hub. docker. com/r/destbio/destbiodocker.
Downloaded from https://academic. oup. com/mbe/advance-article/doi/10. 1093/molbev/msab259/6361628 by Consejo Superior de Investigaciones Cientificas (CSIC) user on 28 September 2021 options from the Tools menu (supplementary fig. S2C). All data, tools, and supporting resources for the DEST dataset, as well as reference tracks downloaded from FlyBase (v. 6. 12)(dosSantos et al. 2015), are freely available at https://dest. bio. CentOS 7. 2 Linux x64 server with 16 Intel Xeon 2. 4 GHz processors and 32 GB RAM. It implements a hierarchical data selector that facilitates the visualization and selection of multiple population genetic metrics or statistics for all 271 samples based on the PoolSNP- processed dataset, taking into account sampling location and date. Importantly, our genome browser provides a portal for downloading allelic information and pre-computed population genetics statistics in multiple formats (supplementary fig.
Identification and quality control of SNP polymorphisms. (Mackay et al 2012)ps://academic. oup. com/mbe/advance-article/doi/10. 1093/molbev/msab259/6361628 by Consejo Superior de Investigaciones Cientificas (CSIC) user on 28 September 2021Collection dates for localities sampled more than once. (C)Generalsamplefeatures of the DEST dataset. The x-axis represents the population sample, ordered by the average read depth. In order to determine appropriate SNP calling and filtering parameters, and to identify potentially problematic population samples, we first calculated the ratio of the number of non-synonymous polymorphisms to the number of synonymous polymorphisms (pN/pS) for each population sample across the whole genome. Since non-synonymous changes are expected to be under strong purifying selection(Kreitman 1983), the pN/pS metric can reflect the presence of sequencing errors that would disproportionately inflate pN relative to pS.