dataesr/scientific-paragraphs-categorization huggingface dataset:
Nested PCR products were visualized on agarose gels after electrophoresis to identify infected samples. This nested PCR protocol does not allow to detect co-infections between parasites of the genera Haemoproteus and Plasmodium. Therefore, all positive samples were sequenced in both directions as in  van Rooyen et al. (2013)  and identified by performing a local BLAST search in the MalAvi database (http://mbio-serv2. mbioekol. lu. se/Malavi/;  Bensch et al. 2009). Co-infections by Haemoproteus and Plasmodium were identified by analyzing double nucleotide peaks on sequence chromatographs. We re-amplified and resequenced all the samples for which the chromatograph could not reliably identify the parasite sequences. All sequences were edited using Geneious v8. 0. 5. Birds not infected by any parasites were classified as "not infected", birds infected with a single parasite genus were classified as "single infected" and those infected with at least two different genera as "co-infected"  (Pigeault et al.
(b) Climatic niche breadth, climatic niche position and geographic range size Estimating species climatic realized niches (i. e. the set of suitable environmental conditions accessible to the species and constrained by biotic interactions;  Jackson and Overpeck (2000) ) requires data for the full geographical range of species together with the corresponding environmental variables  (Guisan et al. , 2017). For each species, we estimated its climatic niche by cross-referencing IUCN range maps (https://www. iucnredlist. org/; only considering the resident and the breeding range) with climatic data. Climatic layers for 19 bioclimatic variables were extracted from worldclim (https://www. worldclim. org/) at a 10' resolution (roughly 340 km² at the equator). From these variables, we performed a PCA from which we extracted the two first axes, which explained 55% and 19% of the total variance, respectively.
Infection prevalence among avian phylogenetic tree To illustrate the effects of host evolutionary history on single and co-infection probabilities, we followed the methods of  Barrow et al. (2019). Specifically, we used 1000 trees from BirdTree. org (backbone tree from  Hackett et al. 2008)  to generate a consensus tree using the ls. consensus function (package phytools v. 0. 6-00, Revell 2020). The prevalence of infections (i. e. the proportion of birds infected by haemosporidian parasites altogether and by each haemosporidian genus separately) was calculated for each species and mapped on the phylogenetic tree using the contMap function (package phytools). The prevalence of infection was visualized over the whole phylogeny using species for which there were at least 5 samples.
These variables were thus removed from the analysis. However, we also tested the effect of these variables in separate models where they replaced the variables they were correlated with. We built two types of phylogenetic multilevel models using Bayesian multilevel models from the brms package  (Bürkner et al. 2021). First, we used a phylogenetic multinomial model with a "Categorical" error distribution to study the effects of species attributes and phylogeny on the probability to be single and co-infected. Second, we used three phylogenetic generalized linear multilevel models with a "Bernoulli" error distribution to study the effects of species attributes and phylogeny on the probability to be infected by each parasite genus, separately. For all models, host species and phylogeny were treated as random effects.
Continuous predictors were standardized to z-scores (mean=0, variance=1) to improve model convergence and parameter estimation. We used the default priors of the brms package (i. e. weakly informative priors) and ran three chains with 11,000 iterations. The first 1,000 iterations were considered as burn-in and were thus discarded. Chains were thinned every 10 iterations. To account for phylogenetic uncertainty, we randomly sampled 100 trees from the set of trees extracted from BirdTree. org and ran the analyses on each tree. We then combined all models using the combine_models function. Note that this approach critically depends on the assumption that all trees are equally likely. We followed the methods of  Barrow et al. (2019)  to compute the phylogenetic signal of single and co-infection probabilities estimated from the multinomial model and of infection probability by each genus from the three generalized linear models.
g. Trypanosoma, Babesia) or by macroparasites such as helminths (e. g. Trematodes, Nematodes, Cestodes). We also concentrated on coinfections with different parasites genera and did not study mixed infections (involving different parasite lineages belonging to the same parasite genus). In order for future work to include mixed infections in their analyses, new specific PCR primers as well as more data (i. e. parasite lineages) in databases such as Malavi  (Bensch et al. 2009 ) are needed to make effective use of computational phasing. Despite these limitations, our dataset and the associated results provided new insights regarding the role of species attributes and phylogeny in predicting bird (co-)infection status.
2020). The strong phylogenetic signal that we found regarding co-infection risk further suggest that co-infections can act as a strong selective pressure that drive host's evolution. These co-infections have rarely been considered in studies investigating the evolutionary ecology of host-parasite interactions and we therefore encourage future studies on this topic to do so. Expanding our understanding of the distribution of coinfection risk across multiple host species together with its effect on the evolution of both hosts and parasites will not only help us understand why some species are more susceptible to (co-)infection than other, but will also be of importance to address urgent public health problems regarding the emergence and evolution of infectious diseases. Analyses carry out on the full data set and on the (B) data set where full and facultative migrant species were excluded.
Authorship: RP, MC, PC and OG designed the study, MB, MA, AC, AK and OG collected data, RP and MC performed modeling work and analyzed output data, RP, MC and CSC wrote the manuscript with input from all authors. Data accessibility: Data supporting the results are archived in Dryad Digital Repository (https://doi. org/XXXXXXXXXXX) and are also provided in Supplemental Materials Appendix 1 to directly accompany this publication.
Drosophila melanogaster is a leading model in population genetics and genomics, and a growing number of whole-genome datasets from natural populations of this species have been published over the last years. A major challenge is the integration of disparate datasets, often generated using different sequencing technologies and bioinformatic pipelines, which hampers our ability to address questions about the evolution of this species. Here we address these issues by developing a bioinformatics pipeline that maps pooled sequencing (Pool-Seq) reads from D. melanogaster to a hologenome consisting of fly and symbiont genomes and estimates allele frequencies using either a heuristic (PoolSNP) or a probabilistic variant caller (SNAPE-pooled). We use this pipeline to generate the largest data repository of genomic data available for D.
melanogaster to date, encompassing 271 previously published and unpublished population samples from over 100 locations in >20 countries on four continents. Several of these locations have been sampled at different seasons across multiple years. This dataset, which we call Drosophila Evolution over Space and Time (DEST), is coupled with sampling and environmental meta-data. A web-based genome browser and web portal provide easy access to the SNP dataset. We further provide guidelines on how to use Pool-Seq data for model-based demographic inference. Our aim is to provide this scalable platform as a community resource which can be easily extended via future efforts for an even more extensive cosmopolitan dataset. Our resource will enable population geneticists to analyze spatio-temporal genetic patterns and evolutionary dynamics of D.
2014; Lack et al. 2016; Machado et al. 2016 Machado et al. , 2021;; Kapun et al. 2016 Kapun et al. , 2020)). The availability of high-quality reference genomes  (Adams 2000; Celniker and Rubin 2003; dos Santos et al. 2015)  and genetic tools  (Schneider 2000; Duffy 2002; Jennings 2011; Hales et al. 2015; Haudry et al. 2020)  facilitates placing evolutionary studies of flies in a mechanistic context, allowing for the functional characterization of ecologically relevant polymorphisms (e. g. ,  de Jong and Bochdanovits 2003; Paaby et al. 2010 Paaby et al. , 2014;; Mateo et al. 2014; Kapun et al. 2016; Durmaz et al. 2018 Durmaz et al. , 2019;; Ramaekers et al. 2019). Recently, work on the evolutionary biology of Drosophila has been fueled by a growing number of population genomic datasets from field collections across a large portion of D.
2012; Burke 2012; Kofler and Schlötterer 2014). However, the rapidly increasing number of genomic datasets processed with different bioinformatic pipelines makes it difficult to compare results across 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 studies and to jointly analyze multiple datasets. Differences among bioinformatic pipelines include filtering methods for the raw reads, mapping algorithms, the choice of the reference genome or SNP calling approaches, potentially generating biases when combining processed datasets from different sources for joint analyses (e. g. ,  Gautier et al. 2013; Hoban et al. 2016). To address these issues, we have developed a modular bioinformatics pipeline to map Pool-Seq reads to a hologenome consisting of fly and microbial genomes, to remove reads from potential Drosophila simulans contaminants, and to estimate allele frequencies using two complementary SNP callers.
Using this pipeline, we generated a unified dataset of pooled allele frequency estimates of D. melanogaster sampled across a large portion of its world-wide distribution, including Europe, North America, Africa, Australia, and Asia. This dataset is the result of the collaborative efforts of the European DrosEU  (Kapun et al. 2020)  and DrosRTEC  (Machado et al. 2021 ) consortia and combines both novel and previously published population genomic data. Our dataset combines samples from 100 localities, 55 of which were sampled at two or more time points across the reproductive season (~10-15 generations/year) for one or more years. Collectively, these samples represent >13,000 individuals, cumulatively sequenced to >16,000x coverage or ~1x per fly. The cost-effectiveness of Pool-Seq has enabled us to estimate genome-wide allele frequencies over geographic space (continental and subcontinental) and time (seasonal, annual, and decadal) scales, thus making our data a unique resource for advancing our understanding of fundamental adaptive and neutral evolutionary processes.
We provide data in two file formats (VCF and GDS:  Danecek et al. 2011; Zheng et al. 2017) , thus allowing researchers to utilize a variety of tools for computational analyses. Our dataset also contains sampling and environmental meta-data to enable various downstream analyses of biological interest. We further employed demographic modeling to investigate the evolutionary history of two distinct genetic clusters in Europe using the DEST Pool-Seq dataset and developed guidelines for using pooled sequencing data for modelbased demographic inference using the python package moments. 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.
Materials and Methods Data sources. The genomic dataset presented here has been assembled from a combination of Pool-Seq libraries and in-silico pooled haplotypes. We combined 246 Pool-Seq libraries of population samples from Europe, North America and the Caribbean that were sampled through space and time by two collaborating consortia in North America (DrosRTEC: https://web. sas. upenn. edu/paul-schmidt-lab/dros-rtec/) and Europe (DrosEU: http://droseu. net) between 2003 and 2016. Of these 246 Pool-Seq samples, 121 samples represent previously unpublished samples generated by DrosEU, 48 DrosEU samples previously reported in  Kapun et al. (2020) , and 77 samples previously reported in  Machado et al. (2021). In addition, we integrated genomic data from >900 inbred or haploid genomes from 25 populations in Africa, Europe, Australia, and North America available from the Drosophila Genome Nexus dataset  (DGN v1.
Mapping pipeline. The joint analysis of genomic data from different sources requires the application of uniform quality criteria and a common bioinformatics pipeline. To accomplish this, we developed a standardized pipeline that performs filtering, quality control and mapping of any given Pool-Seq sample (see supplementary fig. S1 ). This pipeline performs quality filtering of raw reads, maps reads to a hologenome (see below), performs realignment and filtering around indels, and filters for mapping quality. The output of this pipeline includes quality control metrics, bam files, pileup files, and allele frequency estimates for every site in the genome (gSYNC, see below). Our pipeline is provided as a Docker image and will facilitate the integration of future samples to extend the worldwide D.
melanogaster SNP dataset presented here. The mapping pipeline includes the following major steps. Prior to mapping, we removed sequencing adapters and trimmed the 3' ends of all reads using cutadapt  (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 (Andrews 2010). 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)   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://picard. sourceforge. net). Sequences were re-aligned in the proximity of insertions-deletions (indels) with  GATK (v3. 4-46; McKenna et al. 2010). We identified and removed any reads that mapped to the D. simulans genome using a custom python script, following methods outlined previously  (Kapun et al. 2020; Machado et al. 2021;  for a more in-depth analysis of D. simulans contamination see  Wallace et al. 2021). Although this method of decontamination by D. simulans accurately estimates contamination rate and removes the vast majority of D.
simulans reads  (Machado et al. 2021) , care should be taken when analyzing samples with higher contamination rates at sites that are shared polymorphisms between the two species. Incorporation of the DGN dataset. 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  (Pool et al. 2012; Mackay et al. 2012; Langley et al. 2012; Grenier et al. 2015; Kao et al. 2015; Lack et al. 2015 Lack et al. , 2016) ).
SNAPE-pooled is expected to be more sensitive to rare private polymorphisms  (Rainieri et al. 2012, Guirao-Rico and González 2021). The SNP calling step is built using the snakemake  (Mölder et al. 2021)  pipeline and the parameters to run the two callers can be found at https://github. com/DESTbio/DEST_freeze1. gSYNC generation and filtering. Our pipeline utilizes a common data format to encode allele counts for each population sample (SYNC;  Kofler et al. 2011b). 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   (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 upand downstream of indel polymorphisms identified by the GATK IndelRealigner.
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 dataset. This combined matrix was then subjected to variant calling using PoolSNP, resulting in a VCF formatted file. We performed SNP calling only for the major chromosomal arms (X, 2L, 2R, 3L, 3R) and the 4 th (dot) chromosome. Data We found that a global MAC=50 provided qualitatively identical estimates of pN/pS across all populations (Figure  2B ) and that the results were insensitive to MAF (results not shown). We therefore used these parameters for genome-wide variant calling (see Results: 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 two-dimensional 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 pN/pS across a range of local minor allele frequency thresholds (Figure  2C ) and found that pN/pS is largely insensitive to local MAF, once accounting for some problematic samples (see below). Final VCF files with annotations from SNPeff  (version 4. 3; Cingolani et al. 2012)  were stored in VCF and BCF  (Danecek et al. 2011 ) file formats alongside an index file in TABIX format  (Li 2011). Besides VCF files, we also stored SNP data in the GDS file format using the R package SeqArray  (Zheng et al.
2017). To assess population structure in the worldwide dataset, we applied principal components analysis (PCA), population clustering, and population assignment based on a discriminant analysis of principal components (DAPC;  Jombart et al. 2010 ) to all 271 PoolSNP-processed samples. For these analyses, we subsampled a set of 100,000 SNPs spaced apart from each other by at least 500 bp. We optimized our models using cross-validation by iteratively dividing the data as 90% for training and 10% for learning. We extracted the first 40 PCs from the PCA and ran Pearson's correlations between each PC and all loci. We subsequently extracted the top 33,000 SNPs with large and significant correlations to PCs 1-40. We chose the 33,000 number as a compromise between panel size and differentiation power.
Inversion frequency estimates τ ≈ FST = 1/(nm) 1/2 (Patterson Demographic inference with moments. To evaluate the efficacy of PoolSNP and SNAPEpooled in inferring reasonable demographic parameters, we ran pairwise comparisons of European Drosophila populations under four basic demographic models: (1) population divergence with symmetric migration (S+SyM), (  2 ) population divergence with asymmetric migration (S+AsyM), (3) population divergence followed by a bottleneck and growth with symmetric migration (S+BG+SyM), and (4) population divergence followed by a bottleneck and growth with asymmetric migration (S+BG+AsyM). We fit these models using the python package moments  (Jouganous et al. 2017). We converted our data to the moments input format using the genomalicious (Thia and Riginos 2019) function dadi_inputs_pools(), using either the "counts'' or the "probs" (hereafter "binomial") methods.
These are always folded-jSFS to account for unknown ancestral states. For computational purposes, we did not evaluate every possible pairwise combination in the DEST dataset. Instead, we randomly sampled 1,200 population pairs drawn from European populations that passed quality filtering (Supplemental Table  1 ). The moments simulations were run with a maximum of 50 iterations. It is important to note that running these demographic models is computationally expensive and some individual runs fail to converge across the 50 iterations, and thus some models did not run all 50 times. Nevertheless, we explicitly explored the consequences of the total number of completed runs in the performance of the model selection. Model selection was performed using maximum log-likelihood and Akaike's information criterion (AIC) for each completed simulation run.
Results Integrating a worldwide collection of D. melanogaster population genomics resources. We developed a modular and standardized pipeline for generating allele frequency estimates from pooled resequencing of D. melanogaster genomes (supplementary fig. 1 ). Using this pipeline, we assembled a dataset of allele frequencies from 271 D. melanogaster populations sampled around the world (Figure  1A , Supplementary Material online, supplementary table  S1 ). Many of these samples were collected at the same location, at different seasons and over multiple years (Figure  1B ). The nature of the genomic data for each population varies as a consequence of biological origin (e. g. , inbred lines or Pool-Seq), library preparation method, and sequencing platform. S3 ; Figures  2A  and 2B ) indicating that there may be elevated numbers of sequencing errors in some samples.
Some (n=17) of these samples had previously been found to show positive values of Tajima's D across the whole genome  (Kapun et al. 2020). We observed that, as expected, pN/pS was negatively correlated with MAC (linear regression; p<0. 001; Figure  2B ) and that applying a MAC threshold of 50 reduced the elevated pN/pS ratios of the 20 aforementioned outlier samples to values similar to the rest of the dataset, suggesting that potential sequencing errors had been largely removed. Similarity of SNP polymorphisms detected with PoolSNP and SNAPE-pooled. We calculated three metrics related to the amount of polymorphism discovered by our pipelines: the abundance of polymorphisms segregating in n populations across each chromosome (Figure  3A ), the difference of discovered polymorphisms between SNAPE-pooled and PoolSNP (defined as the absolute value of PoolSNP minus SNAPE-pooled; Figure  3B ), and the amount of polymorphism discovered per minor allele frequency bin (Figure  3C ).