dataesr/scientific-paragraphs-categorization huggingface dataset:
Getting started using the homepage and navigation bar The homepage of AraQTL offers an overview of the studies and datasets currently hosted and the navigation tools for investigation and comparison (Figure  1 ). The online manual (http://www. bioinformatics. nl/AraQTL/?mode=manual) provides a detailed and user-friendly description of how to navigate through the database. The search box can be used to find the eQTL profiles for one or more genes in one or all experiments. Moreover, GO terms can be used to find the eQTL profiles of all genes annotated with that GO term. Any query term not directly linked to a gene ID or GO term will report the genes and GO terms with that query term in their description. Investigation and exploration of the data is divided over different functions and pages, all interactively linked (Figure  2 ).
Experiments to be visualised can be pre-selected from the experiment overview table. This table also offers some basic information such as population used, plant tissues and original publication. The publication titles are linked to their PubMed pages for easy access and the data hosted in AraQTL can be downloaded in flat text format, for instance for further analysis with programming languages such as R or Python. The navigation bar provides the main form of interactivity (Figure  2 ) and can be used for selection of graphical overviews, investigations and information. From left to right: the 'AraQTL' button enables quick return to the home page and search box; the 'Experiment' option can be used to go to a genome-wide overview for each experiment; the 'Correlation' option can be used to find genes with correlating eQTL profiles per experiment; the 'Locus' option can be used to find all genes with an eQTL at a specific marker or genomic coordinate; and 'Help' offers information about AraQTL, a link to the Manual and 'Frequently asked questions'.
Benefits of using AraQTL AraQTL was developed to unlock the potential of eQTL datasets for further research. The major feature which sets AraQTL apart from other databases is that it enables groups of genes to be selected based on a shared genetic effect on their expression. To enable direct comparisons of eQTLs between experiments the genetic maps of the different populations have been integrated based on physical marker positions. AraQTL uniquely allows researchers to identify whether a group of genes (defined by a specific GO term) have a shared eQTL hotspot. This is followed by integrative tools for finding which other genes have co-locating eQTLs and exporting those as a list to an external analysis platform. Altogether, groups of genes with a shared genetic architecture can be very easily investigated within and beyond AraQTL, making it a very versatile tool for plant scientists.
DISCUSSION AraQTL is designed to improve the accessibility of Arabidopsis eQTL data, serving a wide range of scientists using Arabidopsis as a model. Storing eQTL data in one platform in a single, unified format provides an easy way of visually inspecting eQTL patterns. These patterns are directly useful for analysis because they indicate whether a gene varies in expression and which loci are likely to be affecting its expression levels. Very often the underlying papers do not include the eQTL profiles as supporting information [but see  Cubillos et al. (2012)  in QTLstore; http://qtlstore. versailles. inra. fr]. Furthermore, the original publications seldom include all the required data, tools and methods required to re-map the eQTLs, such as normalised expression data and genotypes or mapping models and scripts.
Having the results of these datasets, the eQTL profiles, together in AraQTL facilitates integration and allows for comparisons which would otherwise be cumbersome and laborious. Even though all eQTL sets can now be explored in a combined way, some limitations manifest themselves when comparing them. This mainly has to do with the different technologies used in these experiments to measure gene expression. Early microarray technology used probes spotted on a glass slide, which may lead to false positives and false negatives  (Alberts et al. , 2007). The absence of a probe prevents the interrogation for that gene and closely related genes can cross-hybridize due to probe similarity. Moreover, eQTLs could be mapped where transcript abundance variation is misrepresented by differences in hybridisation due to sequence polymorphisms.
This effect is stronger with shorter probes, as for example used on Affymetrix arrays, but is certainly not absent from arrays using longer probes. Consequently, the end-user should be cautious when comparing results from different populations. Some genes may be absent from one specific population, not because of lack of variation, but due to lack of interrogation or because their variation is diluted due to a nonspecific probe (false negative). On another note, the mapping power in different populations may vary, since the number and distribution of recombination sites between the RILs as well as the number of markers used to genotype them are different between the sets, even though the type and number of RILs used in the current datasets is rather uniform. AraQTL hosts the eQTL profiles as they were originally mapped, which means that differences in specific models and mapping procedures could give rise to some of the differences between the different sets of eQTLs.
Future directions The aim of AraQTL is to make the data needed for re-mapping easily accessible, so eQTLs from different experiments can be detected by the same method and therefore better compared. This allows alternative models to be explored or expression levels of different experiments to be included in one eQTL mapping model. Genetic maps can also be improved by including gene expression markers  (West et al. , 2006)  or through sequencing. This will lead to eQTLs with a higher resolution and better regulatory prediction. In future updates of AraQTL these functions will be implemented, yet easy access to the data already enables an efficient start so that anyone can for further exploit eQTL and other system genetics data. Combining established high-throughput measurement techniques like next generation sequencing, proteomics, metabolomics and phenomics offers great potential for plant sciences and quantitative genetics (Figure  5 ).
Combining highly detailed molecular data, such as transcription factor-binding and histone-binding sites or protein-protein interactions, will allow for even more powerful analyses. AraQTL has been designed from the start to easily store and share upcoming RNA-seq data and eQTLs from these data and QTLs from metabolomics and proteomics and to visualise and analyse these together. Comparing sets of genes through functional enrichment will enable an even better, more targeted, approach to candidate gene selection and network generation to link gene expression, genetic variation and function. In the near future, tools will be developed to investigate genetic variation in a more systematic genome-and population-wide manner, enabling more complete and higher-resolution system genetics. We believe the eQTL data in AraQTL will greatly benefit the Arabidopsis research community, providing a rich source of genetic interactions to plant biologists.
EXPERIMENTAL PROCEDURES eQTL profiles mapped in the original papers were used for visualization and investigation, except for those found in  Lowry et al. (2013)  which were re-mapped from the original genotypes and gene expression data by a single marker model using a linear model in R. Original determined genome-wide threshold levels may be applied to call significant eQTLs (Table  1 ). The thresholds for comparing eQTLs will vary depending on the number of genes, eQTLs and populations involved. The physical positions of the markers were used to compare and integrate the genetic maps of the different populations and can be obtained from the AraQTL website. All pairwise correlations between eQTL patterns were calculated using the Pearson correlation coefficients between the eQTL patterns of genes within an experiment using the R function 'cor', on the LOD scores.
Searching for genes with an eQTL at a specific locus is implemented by selecting genes that have a LOD score above the given threshold at the marker closest to the specified locus. To select trans-eQTLs genes with a cis-eQTL can be excluded based on their physical distance to this marker. GO terms were downloaded from The Arabidopsis Information Resource (TAIR10) and gene descriptions from Ensembl BioMart  (Kinsella et al. , 2011). AraQTL was developed using the Python Django web framework. The backend runs on an Ubuntu 16. 04. 1 LTS Linux server, using the Apache web server version 2. 4. 18 and a MySQL 5. 7. 13 database. The web frontend is implemented via Django templates in HTML and Javascript, using the D3 library and Jquery. The cis/ trans plot and QTL profile plots are built upon work by Karl Broman  (Broman, 2015).
When stored in, and visualised from, the same platform, the SNPs and phenotypes enable the integration of QTL mapping and GWAS investigation, further increasing the detection power of both methods. For example, eQTL datasets have been successfully combined with results from transcriptomic GWAS(Kawakatsu et al. , 2016)and allele-specific expression(Cubillos et al. , 2014)RNA-seq experiments.
We designed customized filters to increase the accuracy of SNP calling. A small proportion of positions called by Varscan (~16%) passed these filters. Among positions that we discarded: ~30% contained > 50% of missing data; ~30% became monomorphic after filtering for read-depth variation, multiple mapping, and genotype uncertainties (LRT not significant); and another ~40% were suspected to belong to duplicated regions based on error count, heterozygotes count, elevated read-depth variation and/or proportion of multiple mapped reads. We obtained a dataset of 22,294,769 Single Nucleotide Polymorphisms (SNPs) after filtering, of which 86. 0% were located outside genes, 5. 4% in exonic regions and 8. 6% in intronic regions. In total 34,350 out of the 39,423 genes of the maize genome were covered by at least one SNP.
We found no significant difference between the two sets with a two-sided Kolmogorov-Smirnov test (P-value = 0. 873), thus indicating that gene paralogy is well accounted for heterozygous calls. We further estimated miscalling of both homozygotes and heterozygotes by comparing our HTS genotype calling to that obtained with the MaizeSNP50k genotyping array (S1 Table, ~38,000 positions analyzed). We found a rate of discrepancy between the two datasets that ranged from 0. 016% to 0. 21% per line-with an average value of 0. 038% across lines. For a subset of 20 lines (S1 Table ), our MaizeSNP50k and HTS genotypes were obtained from the same DNA extractions. Considering this subset, the error rate of genotype calling in our HTS data for homozygotes dropped to 0. 036%. In contrast, for heterozygotes (181 positions on average per individual), the percentage of false negatives-proportion of homozygotes in our HTS data among heterozygotes in the MaizeSNP50k data-and false positives-proportion of false heterozygotes among heterozygotes in our HTS data-was 18.
Along with previously characterized genes, we also revealed new candidates such as ZCN5, which is also known as PEBP5. This gene harbored strong evidence of selection both within the Tropicals (S10C Considering candidate genes detected among all pairwise comparisons in both DS_ and GWA_ approaches, we found significant enrichment in 19 MapMan ontologies (S5 Table ). We further grouped MapMan ontologies into 23 main categories, and found enrichment in 2 out of the 23 main categories: carbohydrate metabolism, proteins (S5 Table ). We also tested for a global enrichment of differential selection signals (using Fst) at the gene network level rather than at the individual gene level, following a method proposed by  [30]. In this purpose, we used 294 maize gene networks described in the MaizeCyc database.
Characterizing regions with unexpected patterns of heterozygosity Heterozygotes are much more challenging to call than homozygotes particularly with coverage <20x  [35]. Consistently, we found a much higher rate of false positives (34%) for heterozygotes. This rate still compares advantageously to previous estimates from 103 lines, which had a false positive rate of nearly 75%  [20]. A small proportion of heterozygotes miscalling may be explained by genotyping errors in the data generated by the MaizeSNP50 array. We indeed found examples of array-called heterozygous SNPs in our sample of 3 doubled-haploids (0. 16% of genotypes on average). Although heterozygotes calling in our HTS dataset was error prone, we have postulated that extended segments of heterozygosity (along a region or among lines) provide evidence for either mapping artifacts caused by underlying structural variants (duplications) or regions that are resistant to homozygosity.
Regarding a putative third independent introduction of the Italian material from Argentina  [16] , our results were more consistent with the emergence of Italian lines from an admixture between Spanish and European Flints (Fig  2 ). Letters actually attest to the donation of maize from Spain to the Vatican in Rome after the second voyage of Columbus as early as 1494  [46]. Finally, we have substantiated-with a much larger dataset than  [15, 16] -the admixed origin of the European Flints (EFs) from two contributors, the ENFs and the Tropicals. Altogether our results show that while genetic diversity is significantly lower in Europe than in America (.
Conclusion We scored over 22 million SNPs in American and European maize germplasm with high accuracy. This dataset helped refine a scenario consisting of two major introductions of maize to Europe. The range of latitudes in Europe represents a restricted subset of latitudes in the North America. We found that independent introduction sources and subsequent admixtures are keys to the spread of maize through Europe, mirroring the emergence of the admixed Corn Belt Dents in the US. This repeated pattern of admixture with newly introduced groups has contributed to adaptive innovations, paralleling recent findings in humans. Future work should help unravel the genome-wide specific contribution of parental groups to these admixed groups.
We referred to Tropicals when combining South Americans, Mexicans and Caribbeans (19 Lines). Sample information with name, origin, provider, and status is available in S1 Table. Genomic DNAs of all lines were extracted from fresh leaf tissue of a single plant using the Macherey-Nagel MaxiKit and sent to Integragen (Evry, France) for library construction and sequencing. Sheared total genomic DNA was used to generate Illumina paired-end libraries (500 bp insert size). Each library was paired-end sequenced (2 x 101 bp) with a target sequencing depth of 15x. Sequencing data volume for each line is detailed in S1 Table. DNA-sequencing reads from all lines were deposited in the European Nucleotide Archive (ENA) under the study accession number PRJEB14212.
01; (2) the distribution of the error counts among samples significantly differs from a Poisson distribution of parameter the average error count at the position (Kolmogorov-Smirnov test, α = 5%). Heterozygosity rate. We eliminated heterozygous genotypes located in close vicinity (< 500 bp) to heterozygous insertion-deletions (indels). Local alignments are indeed known to be less accurate in regions encompassing such indels. When a heterozygote was detected among the 67 Lines, we retained only positions for which at least one homozygote of each variant was detected in other lines. We extracted all positions that passed our stringent filters and determined the distribution of missing genotyping information across SNPs as well as the percentage of missing data per line. We annotated SNPs and classified them in two categories: within and outside genes.
We expect no significant difference in the heterozygosity rate between those two sets of genes if our filters were efficient to discern true heterozygotes from homozygotes for different alleles at two duplicated loci. We retained genes with less than 30% of missing data-for a total of 2,788 genes with paralogs and 3,949 single genes-and computed the rate of heterozygosity per gene across our sample of 67 lines. We tested difference in the rate of heterozygosity between the two sets of genes using a bilateral Kolmogorov-Smirnov test (α = 5%). Third, we extracted positions of the Illumina Maize SNP50k array from our HTS data to compare genotypes. For 42 lines, array genotyping was already available  [72]  but the DNAs used for genotyping came from different seed lots than the ones we used for HTS.
Because first-cycle inbred lines exhibit residual heterozygosity, a substantial amount of heterozygous genotypes may differ between and within seed lots derived from the same line. We therefore also generated for another 20 lines new MaizeSNP50k genotyping from the exact same DNAs that served to produce the HTS data (S1 Table ). We used Genome Studio (Illumina, v1) to call genotypes by applying the manually curated clustering of  [73]. We first restricted our analysis to homozygotes both in Maize SNP50k array and HTS data. We calculated the error rate of genotype calling in our HTS data as the ratio of the number of discrepancies between the 2 datasets divided by the total number of genotypes evaluated, and considered either all 62 lines or the subset of 20 lines. Second, we used the subset of 20 lines to calculate errors rates for heterozygotes.
For better visualization, we first merged independently significant (resp. non-significant) adjacent segments; second we removed segments containing a single SNP; and third we repeated the merging. In the end, we retained segments containing at least 5 SNPs. In the following we denoted segments displaying unexpected patterns of heterozygosity as heterozygous segments. For heterozygous segments, we calculated the rate of heterozygosity in our subset of 10 HDs. We also determined the corresponding P-values using the test procedure described above. We next asked whether heterozygous segments were enriched for deleterious mutations. To this purpose, we extracted genome wide genic SNPs and used SIFT4G_Annotator_v2. 3 with the corresponding maize database  [31]  to identify both tolerated and deleterious variants.
Site frequency spectra and summary statistics To evaluate the distributions of allele frequencies in our samples, we generated folded Site Frequency Spectra (SFS) using the DaDi (Diffusion approximation for Demographic inference) python library  [78]. When constructing spectra, we projected down the sample size to account for missing data and differences in sample size. We generated folded SFS for (1) the entire sample of lines restricted to positions of the Maize SNP50k array, and (2) the entire sample (SFS projected down to 60 samples). Additionally, we retrieved all SNP positions available from Tripsacum dactyloides HTS data  [20]  to infer the ancestral versus derived status of our SNPs, and built an unfolded SFS considering the entire set of 67 Lines (projected down to 60 as above). In order to further test the impact of introduction bottlenecks, we generated SFS on American and European lines (projected down to 29 samples) using non-genic SNPs.
We used EIGENSTRAT v5  [86]  on 500,000 non-genic SNPs uniformly distributed along the 10 chromosomes of the whole data set of 67 Lines, and retained the significant axes (α = 5%). We used the function DataEllipse of the R package 'car' v2. 1-2 to draw 95% confidence ellipses of the Mahalanobis distance for genetic groups determined by FastStructure. 4. TreeMix analysis. Treemix models genetic drift to infer historical splits of populations deriving from an outgroup. But when populations are more closely related than modeled by the resulting bifurcating tree, TreeMix reconciliates the modelled covariance with the observed covariance by placing migration edges along the tree. These edges can originate either from existing populations or from unsampled, more basal populations. They are informative with regards to both the direction and the position of admixture relative to the divergence of the populations.
We inferred admixture graphs using Treemix version 1. 12 [87] using 109,580 non-genic SNPs (for which missing data did not exceed 20% of Lines in each population) oriented with the Tripsacum dactyloides outgroup. We considered all populations as defined in S1 Table. We tested 0 to 10 admixture events to build the graph. TreeMix was run on windows of 25 SNPs to account for linkage disequilibrium. We chose the graph corresponding to the first stabilized value of the likelihood, i. e. the value after which the likelihood does not substantially increase. In addition, we computed the three-population test (f3 statistics) introduced by  [88]  to further validate admixture events inferred by TreeMix (threepop program of Treemix). f3 is used to detect correlations in allele frequencies that are not compatible with evolution following a bifurcating tree.
Detection of differential selection in genic regions We employed 3 summary statistics to undertake genome scans for selection: Fst  [84] , XP-CLR  [89]  and Tajima's D-thereafter D  [83]. Fst and XP-CLR both measure genetic differentiation between two groups. XP-CLR also considers one of the two groups as a reference and incorporates local linkage disequilibrium information. D indicates deviation from the neutral SFS caused by demographic or selective processes. We computed D and Fst values for each genic region encompassing > 7 SNPs and for which missing data did not exceed > 30% of lines in each group. For XP-CLR, genome-wide values were computed on sliding windows of 0. 001 cM with 5000 bp steps. Individual SNPs were assigned a position along a genetic map by assuming uniform recombination between mapped markers.
are indicated with boxes in red (candidate genes) and TGA1 in black. (C) Zoom within the region with XP-CLR (dots) and D values (lines with the same color code as in A) presented along with the genome-wide 5% quantile (dark blue and green dotted line for D within CBDs and NFs respectively, and black dotted line for XP-CLR between CBDs and NFs). GRMZM2G138198 and SU1 positions are indicated by red boxes. https://doi. org/10. 1371/journal. pgen. 1006666. g004 breeding. We have generated whole genome sequencing to produce a dataset of SNPs with reduced ascertainment bias and allele frequencies that include low frequency variants (S4 Fig).
Acknowledgments Seeds were kindly provided by  INRA stock center  ( Saint Martin de Hinx, France ),  John Doebley  ( Wisconsin, USA ), and  CIMMYT (Mexico). We are grateful to  Jacques Laborde  and  Carine Palaffre  for maintaining seeds collection at  Saint Martin de Hinx. We are grateful to breeders from institutions that have been involved in the historical development of first cycle inbred lines used in this study. We thank  Delphine Madur  and  Valerie Combes  for handling the DNA samples and MaizeSNP50k data. Advices from  Josephine Daub  were useful to adapt her method to our dataset. We are extremely grateful to  Brandon Gaut  for his help in improving our manuscript. We truly appreciated insightful comments and suggestions made by  Peter Morell  and two anonymous reviewers that helped improving our manuscript.
This work was supported by the  French National Research Agency (ANR)  as part of the  "Investissements d'Avenir" Programme (Amaizing ;  ANR-10-BTBR-03 , AMAIZING,  France Agrimer ). JTB was successively funded by the  "Investissements d'Avenir" Programme , the  LabEx BASC  ( ANR-11-LABX-0034 ) as.