dataesr/scientific-paragraphs-categorization huggingface dataset:
albicans  7, 12  (Fig. 2a  and Supplementary Figs. 2, 3 ). In addition, we found an excess of heterozygous SNPs within clusters when analyzing SNPs specific to this cluster (Meselson effect  23  , Supplementary Fig. 4 ), further confirming the clonal expansion of this human fungal pathogen. We performed a neighbor-net analysis using the network approach to visualize possible recombination events within and  Fig. 2  Phylogenetic relationships and population structure of Candida albicans. a Maximum likelihood tree showing phylogenetic relationships between the 182 isolates used in this study; Thick bars represent bootstrap supports >95% (bootstrap analysis of 1000 resampled datasets); branch lengths are shown and the scale bar represents 0. 04 substitutions per site. We used the midpoint rooting method to root the tree.
albicans genetic clusters. Detection of recombination (Fig. 3 ) led us to further investigate possible footprints of admixture in our dataset. We inferred individual ancestry based on genotype likelihoods from realigned reads by assuming a known number of admixing populations ranging from 2 to 16, using the software NgsAdmix  25. At K = 2, two well-delimitated populations were found, separating strains from cluster 13 (in black, Fig. 2b ) from all other strains. Because cluster 13 isolates showed different SNP patterns compared to other strains (Fig. 1  and see below), we reanalyzed the data after removing this cluster. At several K (from 8 to 16), two genetic clusters always appeared admixed, namely clusters A and B (Fig. 2c ). Genomic scans of statistics designed for measuring population differentiation, i.
Methods Sampling. A total of 182 isolates of Candida albicans were collected previously from different continents and origins (Supplementary Data 1). Production of whole-genome sequencing data. Genomic DNA was extracted from the colonies using the phenol chloroform protocol previously described in ref. 15  or the QiaAmp DNA Mini Kit (Qiagen). The genomes were sequenced either at the Biomics Pole-Genomic Platform of Institut Pasteur, the Department of Genetics at Stanford University or the Sequencing facility of the University of Exeter (see Supplementary Data 1 for details) using the Illumina sequencing technology. Paired-end reads of 100-125 bp were obtained. Reads have been deposited at the NCBI Sequence Read Archive under BioProject ID PRJNA432884. Each set of paired-end reads was mapped against the C.
albicans reference genome SC5314 haplotype A or haplotype B 53 downloaded from the Candida Genome Database  54  (version A22 06-m01) using the Burrows-Wheeler Alignment tool, BWA version 0. 7. 7  55  , with the BWA-MEM algorithm, specifically designed for sequences ranging from 70 bp to 1 Mb and recommended for high-quality queries. SAMtools version 1. 2  56  and Picard tools version 1. 94 (http:// broadinstitute. github. io/picard) were then used to filter, sort and convert SAM files. SNPs were called using Genome Analysis Toolkit version 3. 1-1  [57] [58] [59]  , according to the GATK Best Practices. SNPs and indels were filtered using these following parameters: VariantFiltration, QD < 2. 0, LowQD, ReadPosRankSum < -8. 0, LowRankSum, FS > 60. 0, HightFS, MQRankSum < -12. 5, MQRankSum, MQ < 40.
0, LowMQ, HaplotypeScore > 13. 0, HaploScore. Coverages were also calculated using the Genome Analysis Toolkit. We created two tables encompassing all 182 isolates from VCF files using custom scripts. One encompassed 264,999 confident SNPs across the 182 isolates containing no missing data. Besides passing GATK's filters, we also checked for read depth (it had to be between 0. 5 and 1. 5 of the mean genome coverage), heterozygous positions should have an allelic ratio of number of alternative allele reads/total number of reads comprised between 15 and 85% and homozygous positions should have an allelic ratio of number of alternative allele reads/total number of reads >98% (Supplementary Data 2). The second table encompassed ) on vaginal-simulative medium (VSM) and YPD; For saliva simulative medium (SSM), we calculated colony size as mean of two perpendicular measures of diameters using ImageJ.
5 and 1. 5 of the mean genome coverage; some positions could have several filters which did not pass: a combination of-and ## gave && and + +and ## gave ** (Supplementary Data 3). Phylogenetic analyses and distance trees. RAxML 60 was used to infer phylogenetic relationships between the 182 isolates using the dataset of 264,999 confident SNPs using 1000 bootstraps replicates. As our dataset does not include any outgroup, we used the midpoint rooting method to root our tree, in which the root is set at the midpoint between the two most divergent isolates. We also created neighbor-joining trees using insertion/deletion events by coding no indel as 0, heterozygous indel as 1 and homozygous indel as 2, using the R package 61 ape  62. The distance matrix was calculated by counting the number of differences.
Genetic structure. We used the dataset of 264,999 confident SNPs to infer the finer population structure within C. albicans. We performed NgsAdmix from the ANGSD package  63  to look for admixture in our dataset, from K = 2 to K = 16. After K = 13, clusters with the highest number of isolates, i. e. , clusters 1 (n = 40) and 4 (n = 27), were split into sub-clusters, which likely reflect a problem of number of isolates within clusters rather than a biological meaning. Statistics of population genetics. Nucleotide diversity (π) using VCFtools  64  with the-site-pi option was computed within each cluster. We also used ANGSD  63  to measure differentiation between populations (weighted F ST )  65. Three measures of divergence, F ST (using ANGSD) and df, the number of fixed differences between populations were computed using custom scripts.
This was done along sliding windows of 50 kb using steps of 5000 bp. Plots were done using the R package ggplot2  66. Linkage disequilibrium (LD) was computed as r 2 , the coefficient of correlation between a pair of SNPs, with PLINK version 1. 07  67  , excluding SNPs with minor allele frequency lower than 0. 05. LD (r 2 ) was calculated for each cluster with >10 individuals (clusters 1, 2, 3, 4, 11, and 13) for each chromosome. Mean r 2 -values for each cluster for each chromosome were plotted using the R package ggplot2  66. Neighbor-net analyses. We used the R package phangorn  68  for performing neighbor-net analyses. Hierarchical clustering based on the coverage of repeats. To confirm the predominantly clonal propagation of C. albicans, we calculated the sequencing depth of 121 features annotated as "long_terminal_repeat", "retrotransposon" and "repeat_regions"  54.
Some strains showed very poor growth on VSM, preventing measurement of colony size. These strains were not included in the figure. The tool Iris  71  was used for image analysis, resulting in tables of morphology scores and colony sizes (Supplementary Data 7; some data are missing for strain CEC5019, one of 35 cluster 13 strains) using an R script provided with Iris. For SSM results, Iris was unable to detect limits of colonies and we thus used ImageJ  72  to capture two perpendicular measures of diameter per colony. Analyses of variance (ANOVA) were performed using R, as well as post-ANOVA comparisons, Tukey's HSD (honest significant difference) tests  73. Graphic representations (boxplots) were also performed using R. Check of premature stop codons by Sanger sequencing. To confirm the presence of premature stop codons detected in silico in some ORFs, we Sanger sequenced ~400 bp regions within the AFG1, BMT6, SFL1, VTA1, and ZCF29 ORFs, using DNA from 14 strains (two strains from cluster 1: SC5314 and CEC4496, two strains from cluster 2: CEC4493 and CEC4482, two strains from cluster 3: CEC3597 and CEC3681, two strains from cluster 4: CEC3536 and CEC3716, two strains from cluster 11: CEC3704 and CEC4525, four strains from cluster 13: CEC4103, CEC4104, CEC4878, CEC5030).
Acknowledgements This work was supported by grants from the  Agence Nationale de Recherche  ( ANR-10-LABX-62-IBEID ), the  Genoscope  (projet # 15 AP2008/2009 SNP C. albicans ) and the  Swiss National Science Foundation  ( Sinergia CRSII5_173863/1 ) to C. E. , J. R. was supported by a  Pasteur-Roux fellowship  from  Institut Pasteur. D. D. was the recipient of a PhD fellowship from  Institut National de la Recherche Agronomique. E. P. was the recipient of a post-doctoral fellowship from the  Wellcome Trust  ( WT088858MA ). M. M. -H. and T. G. were supported by a grant from the  Spanish Ministry of Economy and Competitiveness ,  BFU2015-67107  cofunded by the  European Regional Development Fund (ERDF). C. E. , M. -E. B. , S. H. K. , and J. H. S. were supported by a grant from the  French and Korean Ministries for Foreign Affairs  ( PHC STAR 2011 25841YA ).
R. C. M. was supported by project  MitoFun , funded by the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-2013)/ ERC  Grant Agreement No. 614562  and by a  Wolfson Research Merit Award  from the  Royal Society. R. C. M. and K. V. were funded by the  Surgical Reconstruction and Microbiology Research Centre , which is supported by the  National Institute of Health Research, UK. G. S. was supported by the  NIH  grants  R01-HG003468  and  RO1-DE015873. C. E. and T. G. are members of the  CNRS GDRI 0814 iGenolevures consortium. High-throughput sequencing has been performed on the Genomics Platform, member of  France Génomique consortium  ( ANR10-INBS-09-08 ). We thank  Bernard Dujon  and  Tatiana Giraud  for providing insights on an earlier version of this manuscript.
Code availability. All code is available upon request to the authors. Data availability. Raw reads have been deposited at the NCBI Sequence Read Archive under BioProject ID PRJNA432884 [https://www. ncbi. nlm. nih. gov/ bioproject/432884]. Supplementary Data 2 and 3 are SNP datasets (see Methods section for filters). Supplementary Data 7 reports morphology sizes for colony growth on vaginal-simulative medium (VSM), saliva-simulative medium (SSM), and YPD at different temperatures (18 °C, 30 °C, 37 °C and 42 °C). All other relevant data are available from the corresponding author upon request.
Recently, the progress of high-throughput and cost-effective genotyping techniques has offered a unique opportunity to analyze large datasets of domesticated species to study genome changes in response to domestication events  (Druet, Pérez-Pardal, Charlier, & Gautier, 2013; Ma et al. , 2015). In livestock species, genomewide analyses have already shown promising results in mapping traits of economic interest, such as genomic regions related to milk production in cattle (Bos tourus)  (Qanbari et al. , 2011) , muscle development in pig (Sus scrofa)  (Amaral et al. , 2011) , coat pigmentation and skeletal morphology in sheep (Ovis aries)  (Kijas et al. , 2012) , or gait and size in horses (Equus caballus)  (Petersen et al. , 2013). The practical aspect of these population genomics studies lies on the possibility of detecting selected genes associated with traits of economic interest and acts as complement to gene mapping approaches (e.
| Basic statistics and structure analysis To investigate population differentiation, we calculated the pairwise Weir and Cockerham's F ST  (1984)  estimator across all loci among populations, using VCFTools  (Danecek et al. , 2011). Observed and expected heterozygosity (H O and H E ) were estimated using PLINK v1. 07  (Purcell et al. , 2007) , and confidence intervals for these statistics were calculated using the R package boot with 1,000 bootstrap replicates  (Canty & Ripley, 2016). Genomic distribution of F ST was further analyzed by a kernel-smoothing approach using the R package Lokern. A local bandwidth of ~400 was used for fitting the kernel-smoothed regression line. Two clustering methods were used TA B L E 1 Atlantic salmon populations analyzed in this study with geographical origin and sample size (n) to assess the extent of genetic structure among the populations.
First, we performed a principal component analysis (PCA) implemented in the R package adegenet  (Jombart, 2008). Second, we used the maximum likelihood estimation of individual ancestries through ADMIXTURE  (Alexander, Novembre, & Lange, 2009)  software. ADMIXTURE analysis was run using 2,000 bootstraps, and the number of ancestral populations was set from 1 to 10 (K). The optimal K was selected based on the lowest cross-validation error and a visual inspection of the co-ancestry values for each individual.
tions. The algorithm uses a reversible-jump Markov Chain Monte Carlo to explore models with or without selection. To estimate the probability that a locus is under selection, the program uses a Bayes factor for two models: one assuming selection and another assuming neutrality given the data  (Foll & Gaggiotti, 2008). Bayescan was run using a pairwise differentiation test (i. e. , wild vs. domestic strains). For both independent runs on Canadian and Scottish pair of populations, we used BAYESCAN v. 2. 1 default parameters. A Bayes factor between 10 and 32 (log 10 = 1-1. 5) for one locus is considered putatively under divergent selection, while between 32 and 100 (log 10 = 1. 5-2) and Bayes factors above 100 (log 10 > 2) are considered putatively under very strong and decisive divergent selection, respectively.
We used a Bayes factor of 10 (log 10 = 1. 30) as threshold to select candidate loci under selection. Second, we performed an environmental association approach using latent factor mixed models implemented in the software LFMM  (Frichot et al. , 2013). This method calculates the correlations between allele frequencies and environmental variables. We defined Domesticated and Wild conditions as an environmental dichotomous variable (0 = wild; 1 = domesticated). This method can efficiently estimate random effects due to population history and isolation-by-distance and can reduce false-positive associations compared to genome scans  (Frichot et al. , 2013). We applied the "latent factor mixed models" algorithm and calculated the |z| scores for all of the SNPs in both independent comparisons for Canadian and Scottish populations, using 20,000 iterations, 10,000 burn-in iterations in the Gibbs Sampling algorithm and a seed of 1,000.
| SNP annotation and functional enrichment analysis Genomic regions harboring each locus putatively under selection, detected by at least one method (BAYESCAN, LFMM or Rsb), were interrogated for genes annotated to the Atlantic salmon genome reference ICSAG_v2 (GenBank: GCA_000233375. 4) using SnpEff v4. 3  (Cingolani et al. , 2012). This functional annotation is a complete representation of the genome containing 37,206 high-confidence protein-coding genes that have been assigned to a putative functional annotation based on homology within the SwissProt database  (Lien et al. , 2016). Using the salmon transcripts, we performed a blastx on the zebrafish (Danio rerio) peptide reference database (downloaded from http://www. ensembl. org). For each unique peptide in this species, the corresponding gene id and associated gene name were retrieved from ensembl biomart database (retrieved from http://www.
Venn diagrams showing shared SNPs with evidence of selection among three independent tests in Canada populations (a) and Scottish populations (b). Also shown is the overlap of identified loci between both Canada and Scotland across all three tests (BayeScan, LFMM, Rsb [c]) Manhattan plots showing the results of the BayeScan, LFMM, and Rsb outlier analyses applied across the domesticated strain/wild population pairs for Canada (Can; a-c) and Scotland (Sct; d-f). Salmon linkage groups were mapped are indicated on the x-axis. The dashed red line indicates the significant threshold applied to detect the outlier SNPs from the entire dataset. In BAYESCAN analysis this line represents the q-value threshold at log 10 (0. 05) = 1. 32, which corresponds to Bayes factor of 10. In LFMM, the threshold was set at z-score = 10, while Rsb significant threshold was set at -log 10 one-tailed p-value = 3.
F I G U R E 2 2 Venn diagrams showing shared SNPs with evidence of selection among three independent tests in Canada populations (a) and Scottish populations (b). Also shown is the overlap of identified loci between both Canada and Scotland across all three tests (BayeScan, LFMM, Rsb [c]) Manhattan plots showing the results of the BayeScan, LFMM, and Rsb outlier analyses applied across the domesticated strain/wild population pairs for Canada (Can; a-c) and Scotland (Sct; d-f). Salmon linkage groups were mapped are indicated on the x-axis. The dashed red line indicates the significant threshold applied to detect the outlier SNPs from the entire dataset. In BAYESCAN analysis this line represents the q-value threshold at log 10 (0. 05) = 1. 32, which corresponds to Bayes factor of 10. In LFMM, the threshold was set at z-score = 10, while Rsb significant threshold was set at -log 10 one-tailed p-value = 3.
f A , f B , f C = Proportion of Pb from each source. X A , X B , X C = Source isotopic signatures. This is an underdetermined system containing three unknowns and two equations, with no unique solution  46. However, the predicted mixture signature for each source combination can be compared to the observed mixture signature; if it matches, then mass balance is achieved and that source combination is a feasible solution. Because combinations can only be examined in discrete steps, a small tolerance for deviation from exact matches is allowed  46. These equations make interpretation of datasets with n isotope systems and more than n + 1 sources possible  45, 46, 49. Outputs are generally presented as range values, to ensure that all potential combinations are reported  45. IsoSource may provide valuable model output, and prior to development of Bayesian isotopic mixing models, it was the main approach for food web studies  55.
Here we propose an approach for applying a state-of-the-art Bayesian stable isotope mixing model (MixSIAR) to pollution sourcing and artefact tracing via Pb isotopes. "MixSIAR" is a package which runs in the R framework. Details on downloading and setting up the package may be found at https://cran. r-project. org/web/packages/MixSIAR/ index. html. Outline of the approach, with two theoretical applications. Pre-Modelling Preparation. Prior to attempting data modelling, the user must ensure that all potential sources (mining regions active or probably active during the period of study, active smelters, potential natural Pb sources) that may have contributed to the Pb isotope signal at the study site are considered in the analysis. It should be noted that for pollution or archaeological applications, the approach is likely complicated by the fact that potential sources will have changed over time, or that ores once actively mined may have been exhausted.
When developing source lists, all potential sources (with sufficient data available) must be considered, regardless of locality, due to the reality of long-range transport of Pb  14, 59. Further, for pollution tracing of sediment samples, a local 'natural' Pb isotope signal should be introduced to the model, to ensure any such local input (e. g. erosion products) of Pb is considered. All data must be collated, and a database of all signatures prepared. As previously suggested, Pb isotope ratios relative to 204 Pb should be used (i. e. 208 Pb/ 204 Pb, 207 Pb/ 204 Pb and 206 Pb/ 204 Pb). This allows for as much of the possible variability in the isotopic signature to be considered during the modelling, and because each isotope originates from a different decay chain  23  , a degree of independence between the three ratios.
Discussion of this point may be disputable as all such studies investigated the behaviour of the 3-dimensional mixing space developed between the ratios 206 Pb/ 204 Pb,  207  Pb/ 206 Pb and To determine the distribution of the ore bodies considered here, and to ensure MiXSIAR is an appropriate approach, we have performed a number of tests investigating the nature of their isotopic distributions. First, using univariate Shapiro-Wilk and Kolmogorov-Smirnov tests, we determine the normality of each isotope ratio in each field individually, prior to investigation of MVN of the three-ratio system via further tests (Mardia, Henze-Zirkler and Royston). All univariate analysis was performed on SPSS 22, and multivariate analysis using the package "MVN" in R  63. Results (see Table  1 ) indicate normality is typical for the distributions of 206 Pb/ 204 Pb (78% of ore bodies), 207 Pb/ 204 Pb (82%) and  208  Pb/ 204 Pb (69%).
MVN examinations (Table  2 ) indicate most ore bodies may be considered MVN via at least one of the tests (78%), with smaller databases (e. g. Apuseni Porphyry, Mazarron and Oberlausitz) more likely to be MVN, but with larger databases also displaying MVN (e. g. Almeria, Gaul and Valsugana VMS). Notably, some complex and heterogeneous ore bodies (e. g. Apuseni Epithermal), are not MVN, indicating multiple mineralization phases, as observed at Rosia Montana, part of the Apuseni Epithermal ore body  53  , do not even out the isotope distribution range. The MVN distribution of a majority of ore bodies, and the uncertain nature of a number more appear to indicate an approach, which assumes MVN may be applicable. Crucially, the outcomes from these tests are inconclusive, neither proving nor disproving categorically an MVN nature of lead isotopes.
Example 3: Modelling of Artefact Provenance. Another potential application of MixSIAR and isotope mixing is the interpretation of the Pb isotope signal as recorded in individual metal artefacts. Determining the provenance of metal artefacts is of crucial importance in archaeology, and Pb isotopes have previously been used to this end in several studies  50, 81, 82. Here we apply MixSIAR, in much the same way as done above, to the Pb isotope mixture of a litharge roll recovered from a Roman-age mining site in Romania  53. To determine source contributions quantitatively, a dataset of potential sources has been assembled (SI Table  3 , Fig. 6 ). Due to the large number of sources, initial results indicate a wide range of potential origins for the Pb, but with Moldova Nouă ore field apparently the clearest single source (Table  5 ).
As such, we suggest caution when interpreting such outputs. When one field dominates as it does here, we suggest that the user interprets this as a single, dominant source, and does not over-interpret other contributions. A posteriori grouping may be helpful at this point, as it is in this example, where the combination with other Banatitic ores leads to the clear domination of this signal (Table  6 ). Period pollution. One of the further advantages of MixSIAR is that it was developed to investigate populations of species, and not just individuals. As such it allows the user to analyse more than one mixture at a time and provide overall results for the dataset. In practice, this can allow a user not just to obtain proportions at discrete points in time, but to get an overall indication for an entire period.
Acknowledgements We would like to thank  Northumbria University  for J. L. 's postgraduate studentship, and  Malin Kylander  for allowing us to use the Penido Vello Pb isotope data. This is a contribution to the project  PN-II-ID-PCE-2012-4-0530  " Millennial-scale geochemical records of anthropogenic impact and natural climate change in the Romanian Carpathians " supported by the  Romanian Research Council. We also thank two anonymous reviewers, who helped greatly to improve the manuscript.