dataesr/scientific-paragraphs-categorization huggingface dataset:
As the populations that resulted from these two introductions have historically exchanged migrants  (Yalcindag et al. 2012) , it is also possible to determine the importance of migration in the adaptive evolution of the introduced populations. In the present study, we compiled four whole genome polymorphism datasets of P. falciparum from the Americas, the source area (Africa), and other world areas (Asia and Oceania). Using these data, we studied the population genetic structure and colonization history of the American may have responded to the selection imposed by the colonization of novel environments in the New World. As selection may have occurred at different time points in the history of the introduced populations, we used complementary methods to identify recent and also more ancient signals of selection.
Compilation of whole genome polymorphism data We obtained whole-genome polymorphism data for P. falciparum from publicly available datasets. Specifically, we retrieved the single nucleotide polymorphism (SNP) data (VCF files) from the MalariaGen Project  (Pearson et al. 2019 ) that includes 7,113 isolates from 29 countries, particularly Africa (3,877 isolates), Asia (2,942 isolates), Oceania (231 isolates), and South America (39 isolates), and also laboratory samples (16 isolates) and samples collected from travelers (8 isolates). As this dataset only included two countries from the Americas (Colombia and Peru), we also used population data from Brazil (23 isolates)  (Moser et al. 2020) , French Guiana (36 isolates)  (Pelleau et al. 2015) , and Haiti (21 isolates)  (Tagliamonte et al. 2020).
The total dataset before filtering included 7,193 infections from 32 countries in the Americas, Africa, Asia, and Oceania. For the MalariaGen dataset, we quality filtered the SNP data and samples following the recommendation by  Pearson et al. (2019). We kept only high-quality bi-allelic SNPs from the core genome (i. e. , regions present in all individuals, as defined by  Pearson et al. (2019) ) and 5,970 samples that corresponded to the analysis set as defined by  Pearson et al. (2019). We excluded the only sample from Mozambique, from a traveler, to avoid bias due to a population made of only one individual. For all populations, we removed samples with >20% missing data (11 samples) and with multi-strain infections (i. e. multiple P. falciparum genotypes), reported as FWS >0. 95 (Supplementary Fig.
S1 ). In addition, for all pairs of strains displaying excessive relatedness (pairwise-IBD>0. 5), we excluded one strain (Supplementary Fig. S1 ). The final sample included 2,635 individuals (Fig. 1A , Supplementary Table  S1 , Supplementary Fig. S2 ). Then, from this dataset, we removed SNPs with >10% of missing data. We set a minimum allele frequency (MAF) filter at 0. 01%, to delete polymorphisms associated with sequencing errors, and a minimum coverage depth at 15X. In the end, our dataset included 78,036 SNPs for 2,635 samples, with a mean SNP density of 3. 69 per kilobase. The filtering steps are described in Supplementary Figure  S2. Following  Pearson et al. (2019) , we defined eight geographic regions for the analyses: the Americas (SAM), West Africa (WAF), Central Africa (CAF), East Africa (EAF), South Asia (SAS), West Southeast Asia (WSEA), East Southeast Asia (ESEA) and Oceania (OCE).
Two distinct admixed genetic clusters of P. falciparum in the Americas To obtain insights into the genetic relationships among isolates from all over the world, we first investigated P. falciparum population structure using two complementary approaches: i) a genetic ancestry analysis with the ADMIXTURE v1. 3. 0 software and ii) principal component analysis (PCA). For these analyses, we used a dataset of 41,921 SNPs from 2,434 individuals after linkage disequilibrium (LD) pruning, MAF filtering, and missing data filtering (see Material and Methods for more details). The genetic ancestry analysis with ADMIXTURE identified nine distinct genetic pools as the optimal number of clusters (K) of P. falciparum populations worldwide (Supplementary Fig. S3 ). With K=2, Asian populations formed one cluster, and African and American populations the other (Supplementary Fig.
For both analyses, we used genome data of Plasmodium praefalciparum, the closest known relative of P. falciparum, as an outgroup to root the tree/network, and a LD-pruned dataset composed of 20,943 SNPs for 2,638 individuals. The TreeMix analysis identified three migration edges as optimal for our dataset (Supplementary Fig. 5A-B ). The resulting TreeMix consensus tree (Fig. 2A ) confirmed the global genetic structuration given by the PCA and ADMIXTURE analyses, particularly the close relationship between the African and American populations. Many African populations were very closely related, with very short branches. In other words, they were all part of the same cluster that did not drift much in recent times. The tree showed that the two American clusters (SAM North and SAM South) connected to different positions in the tree.
Founder effect associated with P. falciparum colonization of the Americas. To assess the demographic history of the American populations, and particularly whether they went through bottleneck events during the colonization process, we calculated the Tajima's D values  (Tajima 1989) , and analyzed the effective population size (Ne) changes through time using Stairway Plot 2  (Liu and Fu 2020). For these analyses, we only kept the American populations grouped by cluster (excluding the highly admixed Peruvian population), three African populations (Democratic Republic of Congo, Tanzania, and Senegal) and one Asian population (Myanmar) for comparison. The polarized dataset included 31,892 SNPs for 500 samples, from which we could generate an unfolded site frequency spectrum (SFS). The Tajima's D values of the American populations were positive and not different between the SAM genetic clusters (Wilcox test, Bonferroni adjusted p-value: 1.
The dataset for these analyses included 78,036 SNPs for 98 samples. In the SAM North cluster (Colombia -Haiti), the XP-EHH and Rsb tools detected respectively 20 and 19 genes with significant SNPs in their coding sequence (CDS) or untranslated region (UTR) (Supplementary Fig. S6 , and Fig. 4 ), of which 17 were in common. In the SAM South cluster (Brazil -French Guiana), 16 genes displayed significant SNPs in the CDS or UTR regions with XP-EHH (Supplementary Fig. S6 ) and 16 with Rsb (Fig. 4 ); 14 genes were in common. All candidate genes identified as potentially under positive selection in the American clusters are listed in Supplementary Tables  S2-4. These selection tests provided primary evidence of recent selection signals. Indeed, older signals, dating back to the first generations of colonization history (between 400 and 10,000 generations ago according to  Yalcindag et al.
We used the dataset already exploited for the XP-EHH and Rsb analyses, to which we added 103 isolates from Myanmar. We found significant signals of positive selection for 109 and 188 genes with CDS or UTR regions included in outlier windows that putatively underwent positive selection in SAM North (Supplementary Table  S2  and S4 ) and in SAM South (Supplementary Tables  S3  and S4 ), respectively. We identified several genes implicated in interactions with the hosts (Anopheles spp. and/or humans) and with drug resistance (Fig. 4 ). None of these genes overlapped with the results obtained with the haplotype-based tests (XP-EHH and Rsb).
2009; Gharbi et al. 2012). Thus, by mixing the two populations, we artificially reduced the signal of selection and were likely not able to detect it with the tools used. It is therefore important for future studies to increase the sample size within each population to be able to use these tools on each of them independently and detect population-specific selection signals. Furthermore, French Guianan populations have recently evolved a compensatory mutation on the CRT gene that makes it sensitive to chloroquine. The frequency of this mutation has increased from 2. 7% in 2002 to 58% in 2012  (Pelleau et al. 2015). Such rapid allele frequency increase could suggest a recent adaptation signal that could have been detected with XP-EHH and/or Rsb. Since we used the whole dataset from  Pelleau et al.
In our dataset, such signal of adaptive migration/selection on standing variation between clusters was observed for five genes, including TRAP that plays a key role in sporozoite motility and invasion (Fig. 5C ). Although haplotypic diversity is very important in Africa, only few dominant haplotypes remain in South America, thus concomitantly confirming the strong positive selection that occurred on this gene in the New World and also suggesting that only few related haplotypes spread throughout South America through migration. It is not known how this variant was selected in this new environment. On the other hand, in Africa and Asia, TRAP is more under balancing selection whereby it maintains high levels of genetic and haplotypic diversity  (Naung et al. 2022 ). Studies using model-based statistical approaches (Lee and Coop 2017) will be needed to investigate the different modes of convergent adaption in the American populations.
Data mapping, SNP calling, and compilation The sequencing data for the isolates added to the MalariaGen dataset and the samples from the outgroup were retrieved as FASTQ files (Supplementary Table  1 ). Then, sequencing reads were trimmed to remove adapters and preprocessed to eliminate low-quality reads (--quality-cutoff=30) using the cutadapt program  (Martin 2011). Reads shorter than 50 bp and containing "N" were discarded (--minimum-length=50 --max-n=0). Sequenced reads were aligned to the Pf3D7 v3 reference genome of P. falciparum  (Gardner et al. 2002)  using bwa-mem  (Li and Durbin 2009). A first filter was applied to exclude isolates with a mean genome coverage depth lower than 5X. The Genome Analysis Toolkit  (GATK, version 3. 8. 0, McKenna et al. (2010) ) was used to call SNPs in each isolate following the GATK best practices.
Duplicate reads were marked using the MarkDuplicates tool from the Picard tools 2. 5. 0 (broadinstitute. github. io/picard/) with default options. Local realignment around indels was performed using the IndelRealigner tool from GATK. Variants were called using the HaplotypeCaller module in GATK and reads mapped with a "reads minimum mapping quality" of 30 (-mmq 30) and minimum base quality of >20 (--min_base_quality_score 20). During SNP calling, the genotypic information was kept for all sites (variants and invariant sites, option ERC) to retain the information carried by the SNPs fixed for the reference allele. Therefore, the VCF files obtained with the MalariaGen Project dataset could be merged without losing information for the sites fixed in American populations and with the same nucleotide as the reference genome.
SNP data filtration The variant filtration steps were performed using VCFtools v 0. 1. 16 (Danecek et al. 2011)  and BCFtools v1. 10. 2  (Li 2011; Danecek et al. 2021). The within-host infection complexity was assessed by calculating the FWS values  (Amegashie et al. 2020)  with vcfdo (github. com/IDEELResearch/vcfdo; last accessed July 2022). An FWS threshold of >0. 95 was used as a proxy for monoclonal infection (Supplementary Figure  S1 ). Highly related samples and clones could have generated spurious signals of population structure, biased estimators of population genetic variation, and violated the assumptions of the model-based population genetic approaches used in this study (e. g. , ADMIXTURE, TreeMix, ADMIXTOOLS2)  (Wang 2018). Therefore, the relatedness between haploid genotype pairs was measured by estimating the pairwise fraction of the genome identical by descent (IBD) between strains within populations using the hmmIBD program, with the default parameters for recombination and genotyping error rates, and using the allele frequencies estimated by the program  (Schaffner et al.
Population structure, admixture, and relationships between populations As PCA and ADMIXTURE analyses require a dataset with unlinked variants, SNPs were LDpruned with PLINK v. 2  (Chang et al. 2015). All SNPs with a correlation coefficient >0. 1 (parameters: --indep-pairwise 50 10 0. 1) were removed using a window size of 50 SNPs and a step of 10. PCA was carried out with PLINK v. 2  (Chang et al. 2015)  and the following parameters: --geno --maf 0. 001 -mind. The MAF was set at 0. 1% to remove doubletons. Then, the maximum likelihood clustering method implemented in the ADMIXTURE v 1. 3. 0 software  (Alexander et al. 2009 ) was used with different cluster (K) numbers, from 2 to 15, with 15 replicates for each K to check consistency among replicates. The optimal K value was estimated using the cross-validation index, and convergence was checked with pong  (Behr et al.
2016). TreeMix and ADMIXTOOLS2 were used to estimate the most likely population tree or network topology and reticulations among them, based on variance-covariance in allele frequency. When adding the outgroup with three P. praefalciparum samples from  Otto et al. (2018) , only biallelic SNPs with at least no missing data in one P. praefalciparum sample were kept. As TreeMix and ADMIXTOOLS2 require unlinked SNPs, the dataset was LD-pruned as done for the PCA and ADMIXTURE analyses. For TreeMix, the number of migration events (m) that best fitted the data was calculated by running TreeMix 15 times for each m value, with m ranging from 0 to 15. The optimal m value (m=3) was estimated using the OptM R package  (Fitak 2021). Then, a consensus tree with bootstrap node support was obtained by running TreeMix 100 times and post-processing using the BITE R package  (Milanesi et al.
2017). To find the best network topology with ADMIXTOOLS2, the function find_graphs was used for 0 to 12 admixture events with 100 replicates each, and at most 300 generations for each. For the five best network topologies (i. e. those displaying the likelihood score closest to zero), the goodness of fit was computed with the R package admixture-graph  (Leppälä et al. 2017). This approach allows comparing the observed f4 statistics among the different alternatives and identifying the graph(s) that best fit the data. Two graphs with the best goodness of fit and the least f4 statistics outliers were selected (Supplementary Fig. S9 ). Figure  2B  shows only the graph with the lowest number of f4 statistics outliers.
Three P. praefalciparum genomes from the study by  Otto et al. (2018)  were used to polarize the ancestral vs. derived states of SNPs and create an unfolded SFS. Only bi-allelic SNPs without missing data in at least one P. praefalciparum sample were considered for this analysis. The SFS was generated with easySFS (github. com/isaacovercast/easySFS). For these analyses, a mutation rate of 4. 055x10 -9  (Otto et al. 2018 ) was assumed and an observed number of sites equal to the number of P. praefalciparum sites that are in the core genome of this P. falciparum dataset (10,007,378 sites)  (Miles et al. 2015).
Acknowledgments We would like to thank the i-trop bioinformatics platform at IRD Montpellier, the  South Green Platform , and the  French Bioinformatics Institute (IFB)  for providing access to highperformance computing cluster (HPC) resources. We also thank the  Center for Information Technology of the University of Groningen  for their support and for providing access to the Peregrine high-performance computing cluster. This work was supported by the  French ANR   MICETRAL  ( ANR-19-CE35-0010 ) and  ANR GENAD  ( ANR-20-CE35-003 ).
Selection of the methods included in our benchmark We used the following criteria to select the methods included in our benchmark: (i) Availability of code. We included only methods for which the authors have made their code openly available in order to avoid reimplementation issues and to ensure the full reproducibility of our results. (ii) Scalability. Since we are interested in performing population studies over large sets of individuals, we excluded methods that do not provide acceptable scalability. (iii) Unsupervised methods. Finally, as motivated in the introduction, we focus on unsupervised methods in the present study. The methods that satisfy these selection criteria are mALS  [50] , mSync  [45] , CAO  [51]  and MatchEig  [67]. We also identified the following methods for being relevant in our context, but they did not meet our inclusion criteria: HiPPI  [49]  was not included because the code was not provided; we never managed to run GAMGM  [61]  on our graphs despite our efforts (we suspect some well known stability issues with Sinkhorn's algorithm  [68]  but investigating such limitation was out of the scope of this article); we were not able to get interpretable results from LPMP  [69] , due to the sensitive tuning of many parameters; and IRGCL  [70]  did not scale with the memory requirement from our experiments, contrary to the other methods tested.
We provide a detailed description of each of the methods included in our evaluation framework below. In our experiments, these multigraph graph-matching techniques will be compared with the pairwise approach KerGM, and with the two methods from the literature specifically designed for labeling sulcal graphs already described in Sec. 1. 3. 1: Auzias et al. [16]  and Kaltenmark et al. [29]. All the computations from this work were executed on a computing server with 32 physical cores of Intel XEON CPUs and 96 GB of RAM. All of the graph matching methods presented in this study were applied using the code provided by the authors, except for MatchEIG for which a very straightforward Matlab pseudo code was provided in their paper, allowing for re-implementation in just a few lines of code (available in our repository).
Preprocessing of real data. For the evaluation on real data, we use the sulcal graphs from 137 young healthy adults (69 females and 68 males) selected from the publicly available database OASIS  [77]. The preprocessing of these data (brain tissues segmentation, mesh extraction and sulcal graphs construction) has been detailed in  [16, 18]. Across this population, the number of nodes is 88±4, with a maximum size of 101 nodes/pits. Dummy nodes are thus added to all other graphs to get a constant size of 101, as explained above.
More specifically, our expectation is that graph matching techniques should allow solving potential anatomical ambiguities in a much more relevant way than Auzias et al. and Kaltenmark et al. , by exploiting the topological information of the neighbouring folding pattern. Furthermore, we also aim to validate our findings in larger, multi-modal databases like HCP  [96] , thereby confirming the reproducibility of the acquired nomenclature and to gain a understanding on its relevance with respect to the functional organisation of the brain.
1a ). By analyzing adaptive mutation databases, we identified a region within the RpoB subunit of the RNAP where mutations improved fitness in multiple environments. We used CREPE to generate a rich library of 6000 variants with mutations targeting this region and measured the fitness of the variants in multiple environments. According to the dominant GTME hypothesis, mutations in the RNAP allow for condition-specific adaptation. We mapped the fitness, measured at a residue-level precision, to the target sequence and structure, and identified residue clusters important for adaptation in different environments (Fig. 1a ). Adaption in some environments was associated with tradeoffs in others. So, we were able to evaluate if tradeoff-associated traits were controlled by common or modular clusters of residues.
A hotspot for adaptive mutations occurs in the RNAP Recently two databases, the Resistome, and the E. coli mutation database cataloged 5000 and 15000 mutations identified in adaptive laboratory evolution respectively  17, 18. According to both studies, the RNAP and more specifically the β subunit of the RNAP (RpoB) is the most frequently targeted site for adaptive laboratory evolution to diverse stresses  17, 18. Mutations in rpoB were selected in ~31% of the 178 evolution conditions; the most diverse compared to any other gene  17. We found that 40% of all known RNAP mutations were concentrated within a 100 amino acid region between the positions 500-600 (Fig. 1b ). Mutations within this "target" region were selected in more diverse environments compared to any other region of the RNAP. The conditions include improved fitness in minimal media with multiple sugars such as glucose 3 , lactate  10  , and glycerol 3 , and in the presence of different stresses including high osmolarity, hydrogen peroxide, and butanol  26  , high temperature  11  , antibiotic resistance and crossresistance  19  , and silver nanoparticles  15  (Fig.
105 total). d Box plots comparing stringent enrichment (left) and growth-associated fitness (right) of double synonymous mutations (136 variants, blue) and double growth-stringent mutations (91 variants, orange). The red line represents cut-off for stringent mutations (left) and the cut-off for beneficial growth-associated mutations (right). Source data are provided as a Source Data file. strains to propagate and exacerbate the Rifampicin resistance crisis  [20] [21] [22]. In a previous study, we observed significant positive epistasis amongst the double mutants in the target, with Rifampicin-resistant mutations combined with neutral mutations  32. We extracted this list of compensatory non-resistant target mutations from the previous study. We found that the compensatory mutations had significantly higher growth-associated fitness compared to synonymous mutations (KWHtest, p-value < 10 -16 , Fig.
Data analysis Within the code, we used custom packages: Python 3. 7 and packages within Python SciPy, SciKitLearn, Numpy, and Pandas. Preprocessing. All analysis was performed using a custom analysis pipeline for CREPE  44. Paired-end reads were merged using the Usearch Mergepairs algorithm  68. Then, the assembled reads were aligned with the wild-type sequence as the reference using the usearch_global tool from the usearch package  68. The alignment generated a text file with a detailed output, from which the alignment was used to identify the mismatches. Then using the unique mismatches/sequences, we obtained the value counts for each variant. We then used a custom code to extract the amino acid changes  32. Clustering and filtering of data. Often, mutations are introduced during the PCR steps of library preparation and during the sequencing itself.
Therefore, several mutations detected in the dataset are not variants present during the selection but an artifact of errors introduced. The frequency of such errors is low and the sequences associated with such errors can be clustered with their possible parent sequence. As a control, we sequenced regions around the target which were not mutated. We determined the average error rate associated with the sequencing prep using these regions of the sequence. Subsequently, we wrote a custom algorithm to cluster the possible error-associated sequences with a parent. We further filtered the data to eliminate reads that may be erroneous. Since we targeted an essential gene RpoB, the occurrence of stop codons would be impossible. Therefore, the stop codons provided another way of identifying the frequency of erroneous sequences.
The enrichment for ΔrelAΔspoT and the KeioΔrelA libraries was estimated using our previously built pipeline (https://github. com/Alaksh/CREPE-Analysis-Code). The scores were calculated using previously described algorithms by Rubin et al. 69  for fitness estimates with two-time points (pre-and post-plating on selection media): fitness,f = log c i,sel + 0:5 À Á c wt,sel + 0:5 À Á ! À log À c i,input + 0:5 Á À c wt,input + 0:5 Á ! ð4Þ where C i is the total count for a variant "i" in the library and C wt is the total count for the wild-type reference in the library. "Sel" signifies the counts obtained after selection on rifampicin, and "input" signifies counts before selection. For each score, we estimated an error using a Poisson approximation. Standard error = sqrt 1 c i,sel + 1 c wt,sel + 1 c i,input + 1 c i,input ! ð5Þ.