dataesr/scientific-paragraphs-categorization huggingface dataset:
Estimation of the distribution of fitness effect of mutations In the two self-incompatible species, Ae. mutica and Ae. speltoides, and in two highly selfing species, Ae. tauschii and T. urartu, we estimated the distribution of fitness effect of mutations from the unfolded synonymous and non-synonymous site frequency spectra using the PolyDFE method  (Tataru et al. , 2017; Tataru & Bataillon, 2019)  with a model averaging approach (as in  Muyle et al. , 2021). Confidence intervals were obtained by bootstrapping SNPs 1000 times. For the four species, uSFS were polarized using T. caput-medusae as an outgroup. We performed three analyses: on the whole set of SNPs or by splitting the dataset into two subsets: SNPs from genes in the high or low recombining regions. GC-biased gene conversion (gBGC), a recombination associated processes mimicking selection in favor of G and C nucleotides, is known to be active in grasses  (Clément et al.
Acknowledgements and funding sources We thank  G. Sarah ,  Y. Holtz  and  P. Joncour  for help with bioinformatic analyses and  T. Bataillon ,  M. Lascoux  and  D. Schoen  for helpful discussions and suggestions on the manuscript. The work was fundend by the  French Agence Nationale de la Recherche (ANR)  ( ANR-11-BSV7-013-03 ). CB has also received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No. 839643.
The ELISA plates were then washed again with PBS containing 0. 2 % Tween 20. Subsequently, 100 μL of HRP substrate (Ncm TMB One; New Cell and Molecular Biotech Co. Ltd, Suzhou, China) was added into each well. After 15 min incubation, the reaction was stopped by adding 50 μL of 2 M H2SO4 solution and analyzed on a microplate reader at 450 nm wavelength. Waning model. We modelled waning over time in longitudinal anti-N-CTD and anti-ORF8 ELISA OD responses since date of diagnosis using a Bayesian Generalised Additive Mixed Model, with individual-level random intercepts and severity group-level smoothers for time since date of COVID-19 diagnosis. All parameters were assigned mildly informative priors equal to the standardised normal distribution. Posterior distributions were estimated via Hamiltonian Markov Chain Monte Carlo using the brms package  [10].
Acknowledgements This study was funded by the  Health and Medical Research Fund  -Commissioned Research on the  Novel Coronavirus Disease  ( COVID-19 ) [reference nos. COVID190126 ] from the  Health Bureau , the  Hong Kong Special Administrative Region , and partially supported by a grant from the  Research Grant s Council of the Hong Kong Special Administrative Region, China  [Project No. T11-705/21-N ]; Commissioned Research on the  Novel Coronavirus Disease (COVID-19 ),  Hong Kong SAR  [Grant No. COVID1903003 ],  Emergency Key Program of Guangzhou Laboratory, China  [Grant No. EKPG22-30-6 ] and from  InnoHK , an initiative of the  Innovation and Technology Commission , the  Government of the Hong Kong Special Administrative Region.
Prdm9-independent hotspots and DNA methylation datasets Data on the location of DSB hotspots in Prdm9 / knock-out mice , detected by DMC1 ChIP-seq experiment, were retrieved from the study by  Smagulova et al. (2016). The dog LD-based recombination map and the position of hotspots were retrieved from the study of  Auton et al. (2013). We excluded hotspots that were High prevalence of Prdm9-independent recombination hotspots in placental mammals larger than 20 kb as they do not fit the definition of hotspots. Data sets of hypomethylated regions in mouse and dog sperm (identified using Bi-sulfite sequencing) were retrieved from the literature  (Hammoud et al. , 2014; Qu et al. , 2018). Even though methylation data on spermatocytes would have been more fit for the task at hand, only sperm is available in the literature for dogs.
Estimation of the mutation bias Germline mutations rates have been measured by sequencing parent-offspring trios in 36 mammalian species  (Bergeron et al. , 2023). However, for most species, the number of detected mutations is too limited (typically less than 100 de novo mutations) to estimate GC µ accurately. Thus, measures of GC µ based on empirical data are associated with very large confidence intervals  (Wong et al. , 2016; Milholland et al. , 2017; Wang et al. , 2020). Even when there is enough statistical power, the results vary substantially between different datasets  (Wong et al. , 2016; Milholland et al. , 2017). In addition to the issue of reproducibility, it has been demonstrated that the mutation spectra can vary rapidly within human populations  (Harris and Pritchard, 2017).
Acknowledgments We wish to thank  Nicolas Lartillot ,  Carina Farah Mugal , and  Bernard de Massy  for very useful reviews on a previous version of this manuscript, and  Anaïs Duhamel  for help with the figures. This work was performed using the computing facilities of the CC LBBE/PRABI. Funding:  Agence Nationale de la Recherche , Grant  ANR-19-CE12-0019 /  HotRec. Author contributions: Original idea: J. J. ; Model conception: J. J. ; Code: J. J. , D. P, A. L, T. T and L. D; Data analyses: J. J. , D. P, A. L, T. T and L. D; Interpretation: J. J. , D. P, A. L, T. T and L. D; First draft: J. J. ; Editing and revisions: J. J. , D. P, A. L, T. T and L. D; Funding: L. D. Competing interests: The authors declare no conflicts of interest.
Data availability Analysis scripts and documentation for whole genome alignments are available at https://github. com/AlexandreLaverre/CANCGI-WGA/ The hal file of the Carnivore WGA and analysis scripts and documentation for Prdm9 annotation and study of positive selection are available at https://zenodo. org/records/10149667. The rest of analysis scripts and documentation are available at https://gitlab. in2p3. fr/julien. joseph/defhot.
Acknowledgments This research was supported by  FEDER  funds from  Région Centre-Val de Loire  and by  Agence de l'eau Loire-Bretagne  (grant  Nature 2045 , programme  9025 (AP 2015 9025 ). FEDER  also financed the doctoral grant of  L. Portillo -Lemus. Collections in Alabama were supported by the  Department of Biology at the University of Alabama at Birmingham  with field help from  S. Heiser  and  S. Shainker-Connelly.
Samples were analyzed for pH, total carbon and nitrogen, organic matter content, cation exchange capacity, and concentrations of P, Mg and Na. Data were ultimately removed from the analysis due to a lack of significant results (Appendix A). -2. 2. Soil seed bank sampling protocol Soil seed bank sampling was carried out along a 50-meter transect consisting of 8 points starting upslope within the field until the ditch at the bottom of the study site. The first four sampling points are situated within the field, with T1 at the top of the slope and T4 at the bottom of the slope. Sampling was carried out using a corer that allowed the sampling of 10 cm diameter cylinders at a depth of 10 cm. The number of interrows sampled was determined so we had at least 3 interrows with the same vegetation state.
Soil seed bank sampling representativity To gain insight into how many species we might have missed, we used calculations developed for plant species composition surveys. This method is based on computing the theoretical species accumulation curve (SAC) by calculating the sum of generalized Simpson entropies based on subsampling using jackknife methods  (Zhang and Zhou, 2010, summarized in Marcon, 2015). This method forecasts the effort needed to reach the "true" number of species in the sampled population. According to this method, we could have missed approximately 10 species in our sampled soil seed bank, which means that our results might have a bias of approximately 10%. -2. 5. Dispersal modes Species observed throughout the emergence period have been linked to their main dispersal mode using the TRY database  (Kattge et al.
Statistical analyses Eight different equations were used for generalized linear model (GLM) construction (Tab. 1), with separate GLMs constructed for each study site. All data manipulation and statistical analyses were performed using R 4. 2. 2 (R Core Team, 2023) software. The Shannon index was computed for every sample using the entropart package  (Marcon and Hérault, 2015)  with a Chao, Wang & Jost correction  (Chao, Wang and Jost, 2013)  to take undetected species into account. The number of germinated seeds observed per box (=E) was related to the area sampled (=S, equal to 0. 008 m2). Thus, the number of germinated seeds per surface unit obtained for each box, referred to as the "E/S ratio" (in number of plants per m2), was used for statistical analysis  (Gaudichet et al. , 2022). Vegetation cover was visually inspected at multiple points in every interrow sampled.
, 2009). Based on a visual inspection, normality of the residuals and homogeneity of variances were met. Models were then tested using ANOVA tests. To confirm that greenhouse position did not impact emergence, a GLM linking the E/S ratio to the sample location within the greenhouse was also constructed and tested with ANOVA. Greenhouse position did not have an impact on the number of seeds that germinated (F = 0. 44, p = 0. 66). Differences between the coefficients computed for each GLM were tested by performing post hoc Tukey tests with the emmeans package  (Lenth et al. , 2023).
Acknowledgments The authors wish to thank the two anonymous reviewers for their helpful comments, which helped to highlight the strengths of our study. The authors warmly thank  Pauline MARTIN  and  Léo BOULAIS  for their high involvement in the seedling emergence experiment. The authors are also very grateful to  Léa GENTY ,  David FAGES ,  Manon LAGACHERIE ,  Sandrine NEGRO , and  Sébastien TROIANO  for their technical support in the experiment and collection of the data. The authors also wish to thank the  Environmental Research Observatory OMERE  (http://www. obs-omere. org), whose policy drives the availability of part of the data used in the current study. Last, the authors wish to thank the vinegrowers for giving them access to their vineyards. The PhD of M. F. was funded through  LabEX AGRO 2011-LABX-002  (under the  I-Site Muse framework ) coordinated by  Agropolis Fondation.
Data, scripts, code, and supplementary information availability Data are available in this article (Tables and Figures) (citation of the data Gaucherel et al, 2023); Scripts and code are available online: DOI:10. 1111/2041-210X. 13242 of the webpage hosting the data https://github. com/fpom/ecco (citation of the scripts and code Pommereau et al. , 2022);.
Methods The overall process for our chip design and validation is described in Fig. 1. We provide all the data and Markdown/HTML les (Table  S1 , File S1 to S16) showing the step-by-step procedure of all analyses and the results of ancestry analyses run on the Yale High-Performance Clusters (HPC), to facilitate analysis replication. The raw data and all the les required to replicate analyses are available in Zenodo (https://doi. org/10. 5281/zenodo. 10048029), and the code is also available in GitHub (link).
Samples and dataset To identify variable SNPs, we used WGS data from 819 individuals worldwide (see Table  S2 , Fig. 2 ) that were sequenced as part of the Aalbo1200 genomes project (Jacob's preprint). Overall, we genotyped 401 samples with the chip, including single-pair matings from two laboratory colonies (N = 152; two crosses within an invasive North American population -"MAN", three crosses from a native Malaysian sample -"KLP", one cross between MAN x KLP) (Figure  S1 ) and wild caught animals (N = 261) (Table  S3 ). For the technical replicates analyses we used three replicates for ve randomly selected samples, genotyping the same samples three times (Table  S4  and S5 ). For the comparison between SNP chip and WGS we created different datasets to compare the impact of different sample sizes (Figure  S2 ).
Dataset A and Y included 18 samples from two sampling sites, Kathmandu, Nepal (KAT, N = 6) and Saint Augustine, Trinidad and Tobago, (SAI, N = 12; Tables  1  and 2 ) that were genotyped using both platforms. Datasets B and X include all the samples that were genotyped from the two sampling sites above (SNP chip N = 95; WGS N = 30). Dataset C and W included all the samples genotyped with both methods (SNP chip N = 479; WGS N = 819). For the geographic population genomic analyses, we used 237 samples from 28 sampling sites (Fig. 2 , Table  S3 ) from the native range of the species.
SNP discovery We processed 819 whole genome sequencing (WGS) datasets generated by Verily Life Sciences to discover single nucleotide polymorphism (SNP) in the Ae. albopictus AalbF3 genome assembly  24. Our objective was to carry out de nitive, unambiguous genotype determinations -known as hard genotype calls -at SNP sites that met a minimum coverage of 10x. The data were exported in Plink format  25  for downstream analyses. The step-by-step methodology to select SNPs for the chip are detailed in supplementary material (File S1). The WGS sample, detailed in Table  S2 , averaged 100 million 150bp paired-end reads per sample, with an average coverage of 12x. Procedures for the WGS data collection are reported in (Jacob's paper ref).
In the large data set (N = 819) allele-speci c read depth should have the highest reliability, as individual variances average out, most accurately re ecting allele frequencies at common sites. For the SNP chip data sets of different sizes similar considerations hold as the one indicated above for the WGS datasets. For all analyses we only used the 175,396 SNP sites that were in common between the WGS and the SNP chip data sets and were selected for array tilling. We use ANGSD  34  to perform the population-based genotype calls using Samtools model  35. We compared the genotypes within and between each technology using a combination of python and R scripts. We used Samtools  35  to obtain the number of reads mapping to each allele from the cram les (a compressed format used for storing aligned reads data), testing different number of samples, and then checked if there was a correlation between read depth and genotype mismatches.
To obtain equal representation of SNPs between the chip and WGS data across different types of genomic features, we designed an analysis pipeline to sample SNPs in the chip data that match the same proportions of SNPs in intron, exons, and intergenic regions as in the WGS data. We created two sets of 10 SNP datasets each, each set included ~ 34,500 SNPs. The rst set was designed to sample SNP variants in the chip that, according to their functional annotation, followed the same proportions found in each WGS dataset. Out of these, we selected three sets: C3, C5, and C10. The second set included randomly sampled SNPs. Out of these we selected three datasets: U2, U8, and U9 (Table  S8 ). On these six data sets we carried out ancestry and PCA to evaluate if there was a bias when using the SNP chip data sets corrected to re ect the same proportions of SNPs in different genomic features as in the WGS dataset compared to the uncorrected ones (Table  S8 ).
Analyses of wild samples Quality control for chip data: Once we genotyped all the samples, we further reduced the number of SNPs for downstream analyses, using stringent quality control measures to lter the SNPs dataset using Plink v 1. 9 or v 2. 0. We discarded either samples or SNPs that failed these quality control steps, which are described in detail in File S7. Brie y, quality control ltering consisted of removing: 1) loci with more than 10% missingness, 2) individuals with more than 20% missing loci, 3) loci that failed Hardy-Weinberg tests with a threshold of 0. 00001 for each population, 4) loci with a minor allele frequency smaller than 10%, 5) samples whose expected heterozygosity values deviated more than ± 4 standard deviations from the mean of all samples, which might indicate low DNA quality, contamination, or high inbreeding 18 , and 6) related individuals using a relatedness score of 0.
Creating datasets for population genetic analyses We evaluated how adjusting the LD thresholds for pruning impacted the outcomes of downstream analyses. Different LD levels modify the distribution of allele frequencies, potentially altering Fst ( xation index) calculations and skewing genetic differentiation interpretations. In the PCA, changing LD thresholds could rearrange the primary components of genetic variation, leading to different individual distributions on PCA plots. For ancestry analysis, the choice of LD threshold can in uence the set of SNPs used, and this selection can shift ancestry proportions and assignments among individuals. The r 2 statistic in LD analysis represents the squared correlation coe cient between two alleles at different loci. It quanti es the strength and direction of the association between these alleles.
These thresholds also enhance computational e ciency, minimize potential biases, and align with standard practices for more straightforward comparisons with other studies. Finally, for our population structure analyses we followed the recommendations of the algorithm's manuals. For example, we used the recommended r 2 threshold of 0. 01 for Neural Admixture  39  , and the recommended threshold of 0. 1 for Admixture  40. To evaluate the impact of different LD parameters on downstream population genomic analyses, we created two datasets with SNPs obtained using both r 2 values: 0. 01 (LD1: 20,931 SNPs) and 0. 1 (LD2: 57,780 SNPs) (Table  S10 ). Using outFlank  19  , we also created a dataset with quasi-neutral SNPs (intergenic: 9,483 SNPs) by excluding intergenic SNPs with high genetic differentiation values, as measured by Fst values (Fst > 0.
We also ran PCA to visualize patterns of genetic variation within and between groups, helping pinpoint signi cant axes of differentiation to complement the ancestry/clustering analyses described above. These analyses were run using the R package LEA 46 , Plink  25  , and adegenet  47, 48  (File S7 and S10). We ran the PCA with all three SNP sets (intergenic, LD1, and LD2) to evaluate the reproducibility of the results. We also run PCA and Clustering (LEA) analyses on the six SNP sets created to assess potential biases in the chip's coding regions ("corrected': C3, C5, and C10 and "uncorrected": U5, U8, and U9), described above in the section on variant functional annotation, Table  S8 ). We visualized all SNP sets together for PCA in a faceted plot crafted with ggplot2. Methods for the LEA analysis for the same six datasets is detailed in File S14.
Comparison of WGS and chip genotypes The analysis of genotype data using either WGS or SNP chip methodologies across three distinct data set sizes (labeled a, b, c for SNP chips and y, x, w for WGS) is shown in Figure  S2 , performing one genotype calls for each dataset. Figure  2  shows that for both methods, the genotype error rate decreases as the sample size increases. Speci cally, for the SNP chip datasets, error rates declined from 1. 16-0. 41% (Table  1 ). In contrast, error rates for the WGS datasets range from 1. 35-3. 09% (Table  1 ). Across both platforms, the SAI samples from Saint Augustine, Trinidad and Tobago, displayed slightly elevated error rates compared to other samples, ranging from 0. 46-1. 42% for SNP chips and 3. 36-9. 25% for WGS. The average mismatch rate was 8. 11% between the SNP chip and WGS when analyzing the same 18 individuals (ay, Table  1 ).
For KAT the values vary from 0. 03 to 1. 25%, while for SAI it varies from 0. 08 up to 4. 12% (Table  2 ). On a broader scale, the SNP chip genotype data stays consistently below an error rate of 1. 30%, irrespective of the sample size in question. Meanwhile, the WGS data oscillate, exhibiting error rates between 1. 35% and 3. 09%, contingent on the dataset size. The average error rate rises to between 6. 70% and 8. 11% when cross-comparing genotype calls from the two platforms (Figure  S5 , Table  1 ). A pattern emerges where reduced read depths in WGS data correlate with heightened mismatch rates between methods (Figure  S8 ). Core metrics such as the Fisher Linear Discriminant (FLD) and call rate (CR) manifest as in uential variables affecting these mismatch rates (Figures  S9  and S10 ).
A PCA analysis compared the WGS and SNP chip data for the 18-sample dataset, post-SNP ltration based on criteria like FLD, CR, and a read depth greater than 20. The latter factors were deduced to correlate with mismatch rates (Fig. 4 , Figures  S9  and S10 ). The PCA displays a signi cant overlap between WGS and chip samples (Fig. 4 ). Therefore, while sample size exerts minimal in uence on error rates at a sequencing depth of about 20x, depth combined with chip-speci c metrics (FLD and CR) signi cantly dictates genotype consistency.
The chip bias is especially pronounced in the overrepresentation of variants in functionally signi cant regions like coding exons (synonymous and nonsynonymous) and untranslated regions (UTRs). To correct for this, we turned to WGS data as a gold standard for unbiased variant representation. We performed a comprehensive functional annotation on both the WGS and SNP chip datasets to deepen our understanding of the variant landscape. This allowed us to categorize each SNP based on its genomic position or functional annotation category as described above. We then compared the proportions of SNPs across these categories between the two datasets. Regarding intronic and intergenic variants, the chip generally underperforms WGS, as evidenced by negative bias percentages of -5. 32% and - 12. 47%, respectively.
82. Twelve samples, all from Ho Chi Minh (HOC), Vietnam, failed DQC indicating possible species misidenti cation prior to DNA extraction, which was con rmed by PCR, as they failed to amplify using the Ae. albopictus cytochrome oxidase I speci c primers (Table  S6 , File S17). These samples were thus not included in Table  2  and subsequent analyses. Additional samples from Ho Chi Minh from a different collection time, con rmed to be Ae. albopictus, were subsequently genotyped (Table  S3 ) and added to the dataset used for population genomic analyses. All the other samples from this collection were con rmed to be Ae. albopictus by PCR.
The Fst estimates for samples from South Asia (India, Maldives, Malaysia, and Nepal) were the highest (average FST = 0. 12 for the region), followed by the samples from Southeast and East Asia. To evaluate the chip bias effect, we used six SNP dataset sets. Three (C10, C3, and C5) were corrected by sampling the SNPs based on their functional category and replicating the proportions observed in the WGS data (Table  5 ). The other three SNP data sets (U2, U8 and U9) included the same number of randomly sampled SNPs. The main difference between these sets of SNPs were the number of shared SNPs: the three SNP sets with corrected bias (the C set) shared 48. 02% of the SNPs, whereas the SNP sets without bias correction (the U set) shared only 8. 35% of the SNPs (Figure  S22 ). The sNMF-LEA analyses with corrected bias (the C set) generated the same overall patterns observed with the other three SNPs sets (intergenic, LD1 and LD2, Figure  S16  and S17).
Comparison between WGS and SNP chip genotype calls Comparison of the two genotyping methods revealed that, while results from WGS and our chip were similar, read depth for the WGS data, sample size, and chip ltering thresholds in uenced mismatch rates. We had three different sized datasets for each method (Figure  S2 ). In both Chip and WGS genotyping methods, the error rate in genotypes increased as more samples were included in the study. For example, among 18 individuals genotyped with the chip, the 12 SAI samples from Saint Augustine (Trinidad and Tobago) had a higher mismatch rate of 1. 42%. This was greater than the 0. 95% mismatch rate in the 6 KAT samples from Kathmandu, Nepal (Tables  1  and 2 ). The higher error rate in SAI samples might relate to their invasive range and island origin, factors that could lead to allele loss due to drift, but it is mostly likely due to differences in the DNA quality, most likely due to shipping issues.