dataesr/scientific-paragraphs-categorization huggingface dataset:
| Postgenotyping analysis Following MassARRAY genotyping, SNPs were concatenated and converted to Phylip format for phylogenetic analysis. UPGMA trees were calculated in Geneious R9  (Kearse et al. , 2012). The RAxML BlackBox Web server was used to run ML analysis with default settings  (Stamatakis, Hoover, & Rougemont, 2008) , and trees were uploaded to iTOL for annotation  (Letunic & Bork, 2016). The Eurasian white oaks (Q. petraea, Q. robur, Q. fabri, and Q. aliena) were used to root the ML tree for purposes of visualization. Dataset templates retrieved from the iTOL help page were used for color-coding species (with the exception of the Eurasian white oaks, which were colored the same) and to highlight mother trees and learning samples (Supplementary File 4). Admixture in acorns from mother trees was investigated using an admixed population genetic model as implemented in STRUCTURE v.
2. 3. 4  (Pritchard, Stephens, & Donnelly, 2000). For every sample, SNP alleles were coded as integers (A = 1, C = 2, T = 3, G = 4, and missing data = -1), and the species of each sample was coded in the population column. Analysis was conducted under the "admixture" ancestral model with allele frequencies correlated among populations and lambda (λ) set to the default value (1. 0). Population F I G U R E 3 STRUCTURE analysis to assess rate of hybrid offspring by non-native North American white oaks within the oak living collections at The Morton Arboretum. (a) Genetic assignment of 247 individuals at 66 loci according to MCMC clustering method implemented in STRUCTURE  (Pritchard et al. , 2000). DISTRUCT v1. 1 was used for the visualization  (Rosenberg, 2004). Vertical black lines represent grouping of individuals according to their population assignment.
| RE SULTS Using the methods described above, we worked from a set of 32,959 RAD-seq loci to a candidate set of 844 SNPs. We developed primers for 344 loci (Table  1 ) based on inspection of the candidate set, of which 291 were screened using the MassARRAY system; 187 SNPs amplified successfully, and 119 were selected for multiplexing using the MassARRAY primer design tool (Figure  1 ). From this, we developed a final DNA barcode for 15 oak species/species groups based on 80 SNPs, which can be run in two MassARRAY multiplexes. We then used this DNA barcode to validate identity of the 59 learning samples used to develop the barcode and successfully evaluate species status of 37 previously nongenotyped adults and 228 acorns from nine mother trees in an Arboretum setting.
| SNP identification in RAD-seq data Two different approaches were used to generate a list of potential SNPs for barcoding. First, the stringent screening approach resulted in the identification of 1090 loci of potential utility, which were exported as FASTA files (Figure  1 , dashed line). We generated a list of 395 potential SNPs of which the majority (264 SNPs) were discarded by the primer design tool and 131 showed primer compatibility across four multiplexes. Of the 131 SNPs, 83 (63%) were added to the list of verified SNPs. The majority of these SNPs were diagnostic for Q. bicolor, Q. michauxii, or Q. lyrata. For Q. michauxii, we found more than fixed SNPs that were considered for the genotyping toolkit. The relaxed screening approach (Figure  1 , green line) was then used primarily to identify SNPs for Q.
| Primer design and SNP genotyping The Assay Design Suite was used to design suitable primers pairs for more than 844 potential SNPs, 395 from the stringent selection approach and 449 from the relaxed selection approach. The primer design software rejected 59% of these due to the presence of ambiguous nucleotides close to the SNP site, potential primerdimer formation, or placement of SNP sites too close to the 5′ or 3′ end of the amplified region. Primers were designed for the remaining 344 (40%) potential SNPs, of which we combined 291 SNPs into eight multiplexes for screening (Table  1 ); 187 of these SNPs were confirmed as fixed (44) or nearly fixed (143) in a species, while 93 SNPs were identified as false positives based on failure to discriminate species in a broader range of 63 samples.
| Analysis of SNP data The developed barcoding toolkit was used to genotype a total of 324 samples. The sample set consisted of 228 acorns, 37 adult trees not previously genotyped (including nine mother trees), and 59 learning samples drawn from the 63 samples originally sequenced using RAD-seq and used to identify SNPs. Maximum-likelihood analysis of the SNP dataset from parent trees and learning samples recovers a topology comparable to that described in previous studies  (Hipp et al. , 2014 (Hipp et al. , , 2018;; McVay, Hipp et al. , 2017)  (Figure  4 ). UPGMA analyses provide no additional insights over maximum-likelihood analyses and are excluded from further discussion. A total of 70 SNPs in two multiplexes were successfully amplified. Both multiplexes together were required to identify species.
Amplification of SNPs varied among samples. The MassARRAY amplification success rate per mother tree was assessed by calculating the percentage of missing data per seedling for the two multiplexes and summarized by mother tree (Table  3 ). Overall, the rate of MassARRAY amplification is high, ranging from 57% to 100%, with the exception for two mother trees, Q. montana (602-2000*3) and Q. muehlenbergii (704-46*2 sd T) (Table  3 ). Species were recovered as monophyletic based on the barcode SNP set with bootstraps values between 62% and 100% (Figure  4 ).
| Species integrity in a botanical garden We used the barcoding toolkit to genotype nine mother trees from five species not native to the region and 228 acorns from them, as well as 28 additional adult trees (Table  3 ). STRUCTURE analysis was performed with 247 samples using 66 loci to estimate rates of hybridization in the genotyped samples. A K of 6 explained substantial variance in the data without overfitting (Figure  3b ). Of 228 acorns, 191 acorns could be assigned to a single species, whereas 24 acorns showed >5% admixture (Figure  3 ). or Q. stellata or Q. macrocarpa (Figure  3a ). We observed no hybridization among acorns from the Q. muehlenbergii mother tree; however, two acorns from Q. prinoides 745-52*1 showed admixture with Q. macrocarpa. An interesting result was observed for Q.
| Comparing pipelines: Stacks vs PyRAD The PyRAD does when indels are frequent  (Eaton, 2014). In our study, the use of PyRAD for locus detection resulted in a higher number of SNPs that we initially screened (233 SNPs) and subsequently verified via MassARRAY and considered for the barcode (100 SNPs). Using Stacks, we identified ~25% as many SNPs (58 SNPs screened, 19 suitable for genotyping). PyRAD is expected to outperform Stacks in phylogenetic samples  (Eaton, 2014) , making it a better fit to our multispecies sample. Stacks is more suitable for the analysis of population data  (Catchen et al. , 2013)  and may be as effective as PyRAD for within-species studies or studies among two close relatives, but our results support previous findings that at least in a phylogenetically structured multispecies context, PyRAD is likely to be more efficient at detecting shared loci.
| Constraints on primer design The RAD-seq loci we used to design primers were approximately 85 bp in length (ignoring indels), which introduces a strong constraint on primer design. The quality of flanking sequences of a SNP contributes strongly to its suitability for multiplexing, although the MassARRAY primer design software does accept ambiguities within the flanking region. using RAD-seq. This limitation could easily be circumvented using longer sequencing reads. In our study, primer design and multiplex suitability cut our list of potential SNPs by half: Of more than 800 potential SNPs, only 187 reached the final set of SNPs that we considered in detail for the final reactions. The high rate of attrition expected during any such project makes increasing sequence read length at the outset of the project-thus increasing the percentage of the genotype sequenced that is suitable for SNP amplification-an efficient way of increasing the number of potential SNPs.
We were not certain at the outset of the study that we would find sufficient numbers of fixed or nearly fixed SNPs to develop a barcode in oaks, which are renowned for hybridization and lineage sorting  (Hipp, 2015). In our study, we found 40 apparently fixed SNPs unique to nine species (based on our admittedly small sampling within species) and 147 nearly fixed SNPs, as well as two fixed SNPs at the clade level that were useful for distinguishing southern species from northern species in our sample (the clade comprising Quercus stellata and its relatives; Table  2 ). We found very few fixed or nearly fixed SNPs in Q. stellata and Q. macrocarpa, probably for two reasons: in Q. macrocarpa, we started with a larger candidate pool in the RAD-seq dataset (N = 9 individuals), which may have rendered our F ST threshold for SNP discovery too stringent; and both species are wide in geographic range and known to hybridize with numerous other white oak species  (Burger, 1975; Hardin, 1975; Rushton, 1993; Stoynoff & Hess, 2002; Whittemore & Schaal, 1991).
cies and their hybrids. The developed barcoding toolkit should be useful in predicting which species are more likely to hybridize, monitoring hybridization in response to climate change, and ultimately predicting how species introductions and planting strategies will impact hybridization in artificial plantings. Such attention to genetic conservation will help to guide and manage future plantings of oaks in common gardens, living collections, and elsewhere. ACK N OWLED G M ENTSFinancial support by the Transnational Access to Research Infrastructures activity in the seventh Framework Programme of the EC under the Trees4Future project (OAKCODING project, no. 284181) and mobility grant (LabEx COTE) for conducting the research is gratefully acknowledged by E. F. A. L. H. , and M. H. ; generation and analysis of RAD-seq data were supported by U.
S. National Science Foundation (Award #1146488 to A. L. H. ); and permission to use prepublication RAD-seq data for creation of this barcode was granted by collaborators P. Manos, J. Cavender-Bares, and A. Gonzalez-Rodriguez. Part of the experiments (DNA preparations and MassARRAY experiments) were performed at the Genome Transcriptome Facility of Bordeaux (grants from the Conseil Régional d'Aquitaine n°20030304002FA and 20040305003FA, from the European Union FEDER n°2003227 and from Investissements d'Avenir ANR-10-EQPX-16-01). We would also like to thank Thibault Leroy for helping with the preparation of the in-house scripts. Last but not least, we would like to thank Dr. Remy Petit for insights at the outset of this project. conceived and designed the study and formulated analyses. ALH and MH generated and analyzed the RAD-seq dataset.
Funding information  U. S. National Science Foundation , Grant/ Award Number:  1146488 ;  Seventh Framework Programme , Grant/Award Number:  284181 ;  Conseil Régional d'Aquitaine , Grant/Award Number:  20030304002FA  and  20040305003FA ;  Investissements d'Avenir , Grant/Award Number:  ANR-10-EQPX-16-01.
DATA ACCE SS I B I LIT Y FASTQ (RAD-seq) files are available under NCBI BioProject PRJNA376740 (https://www. ncbi. nlm. nih. gov/bioproject/376740). Scripts and auxiliary data utilized in this article are available from the Dryad Digital Repository: https://doi. org/10. 5061/dryad. dg5hv06. O RCI D Andrew L. Hipp http://orcid. org/0000-0002-1241-9904 R E FE R E N C E S.
We used GODomainMiner to predict GO-domain associations between each of the 3 GO ontology namespaces (MF, BP, and CC) and the Pfam, CATH, and SCOP domain classifications. Overall, GODomainMiner yields average enrichments of 15-, 41-and 25-fold GO-domain associations compared to the existing GO annotations in these 3 domain classifications, respectively. Conclusions: These associations could potentially be used to annotate many of the protein chains in the Protein Databank and protein sequences in UniProt whose domain composition is known but which currently lack GO annotation.
Background Proteins are macromolecules which carry out many biological functions in living organisms. At the molecular level, protein functions are often performed by highly conserved structural regions identified from sequence or structure alignments, which may be classified into families of domains. Because many protein domains fold into characteristic three-dimensional (3D) structures, there is often a close relationship between protein structure and protein function  [1]. Currently, the Pfam database is one of the most widely used sequence-based classifications of protein domains and domain families  [2]. The CATH  [3]  and SCOP  [4]  databases are examples of structural domain classifications. As well as sequence-based and structure-based classifications, proteins may also be classified according to their function.
For example, the Gene Ontology (GO)  [5]  consists of a controlled vocabulary of GO terms which describe the gene products in a cell. Each GO term has a name, a distinct alphanumeric identifier, and a "namespace" (ontology) which has one of the following 3 values: biological process (BP), molecular function (MF), or cellular component (CC). The GO ontology is structured as a rooted Directed Acyclic Graph (rDAG) in which terms are nodes connected by different hierarchical relations. However, most protein domain classification systems annotate domains only according to the entire protein to which it belongs. One interesting exception is the dcGO database  [6]  which provides multiple ontological annotations (such as GO) for protein domains. Nonetheless, we found that there are several manually curated GO-Pfam associations from InterPro  [7]  which are not present in dcGO.
Indeed, from the results of a previous version of our approach  [8, 9] , we estimated that dcGO associations can only annotate 43% of the unannotated structures in the Protein Databank (PDB)  [10]. More generally, there are many millions of protein sequences that currently lack GO annotations. On the other hand, only a relatively small number of distinct protein domain families exist, which are re-used and combined in different ways in different proteins. Indeed, compared to the vast number of different sequences that exist, current domain classifications contain of the order of only 15,000 distinct protein domain families. Therefore, it is natural to suppose that if known protein structure and sequence annotations could be assigned GO terms at the domain level, many of these annotations could be transferred to a potentially very large number of unannotated proteins.
Firstly, the problem is formalised as a bipartite graph enrichment problem in the setting of a tripartite graph. The core CODAC algorithm solves this problem using the vector cosine similarity model, from which it creates new weighted edges between items of the bipartite graph on the basis of their graph neighbourhood similarity. This approach is augmented using techniques to handle the problems of multiple data sources, bias due to identical items, the influence of the hierarchical organisation of the GO ontology, and statistical significance. Here, the overall approach is applied to 9 different bipartite graphs involving the 3 GO ontologies (BP, MF, and CC) and 3 popular protein domain classifications (Pfam, CATH, and SCOP). Our results show that the GO-domain associations discovered by this approach represent an average of 15-, 41-and 25-fold increase in the number of edges on the concerned bipartite graphs.
Gold standard of positive and negative examples In order to determine an edge similarity threshold, we need to define a "gold standard" set of positive and negative examples of associations. Here, we take all of the P = |E 3 | existing associations present in G 3 as positive examples. To create negative examples, we shuffle the edges of G 1 and G 2 in order to rearrange in a random way all edges between X and Z, and between Y and Z. During shuffling, the node degrees of each x i , y j and z k is kept constant, and the shuffled edges are constrained not to overlap the original edges. The shuffled graphs are denoted by G # 1 and G # 2 , from which a new shuffled cosine similarity matrix, C # , may be calculated. This matrix is then used to select |N| = |P| negative examples at random. Taken together, the P positive and N negative examples constitute our "Gold Standard" dataset.
Determining the score threshold We randomly split the Gold Standard dataset into two groups with equal distributions of positive and negative examples to give a "Training" and a "Test" subset. We then rank the scores of all members of the Training subset, and label them "positive" or "negative" according to a score threshold that is varied from 0. 0 to 1. 0 in steps of 0. 001. This allows us to determine the numbers of true positive (TP), false positive (FP), true negative (TN), and false negative (FN) predictions for each threshold. We then calculate the recall, R = TP/(TP + FN), precision, P = TP/(TP +FP), and the F-measure, F 1 = 2RP/(P +R). The similarity threshold T that gives the best F-measure with the Training subset is verified using the Test subset and retained to calculate a filtered cosine similarity matrix, C * , according to C * i,j = C i,j if C i,j > T or if the (x i , y j ) edge already exists in E 3 , otherwise, C * i,j = 0.
Combining multiple datasets There may often be more than one configuration for a graph G , that has the same G 3 but different Z, E 1 , and E 2 in G 1 and G 2. In our instantiation this corresponds to the fact that GO terms and Pfam domains can be indirectly connected either through UniProtKB/SwissProt sequences  [12]  or through PDB chains in SIFTS  [13]. To handle multiple datasets, each input tripartite graph is processed separately to calculate its respective cosine similarity matrix C d. The cosine similarity scores are then combined as a weighted average to give a consensus similarity matrix, CS (Algorithm 2). Whenever there is no data for a given pair (x, y) in an input graph, the corresponding score C d x,y is set to zero. Receiver-operator-characteristic (ROC) analysis provides an objective way to measure the ability of an information retrieval system to retrieve positive documents as first ranked, i.
e. with the best scores  [14]. One advantage of ROC-based approaches is that they are rather insensitive to the particular numbers of the positive and negative instances used  [15]. Here, in order to find the best values for the dataset weights w d , each weight is varied from 1 to 10 in steps of 0. 1, and for each combination of weights a ROC performance curve is calculated using the complete ranked list of consensus scores and our Gold Standard set of positive examples. The combination of weights that gives the largest area under the curve (AUC) is selected and used to calculate the best consensus similarity matrix CS. Then, the PredictAssociations procedure determines the best threshold to filter the consensus similarity matrix CS and to deduce the resulting enriched bipartite graph G * 3.
In order to estimate the probability of finding edges by chance, one could generate multiple random graphs by shuffling the edges of a given input graph, as described above for constructing the Gold Standard Negative examples. However, this is quite impractical given the very large numbers of items in X, Y, and Z and the complexity of the filtering procedure that would have to be repeated for each shuffled version of the dataset. Instead, we assume that the probability for finding an edge (x, y) by random chance is given by a hypergeometric distribution of the number of common neighbours (x, z) and (y, z). Letting N z denote the total number of items in Z, N x the number of neighbours of x in Z, and N y the number of neighbours of y in Z, the hypergeometric probability distribution is given by return G 1 = G Cl 1 , G 2 = G Cl p(K K x,y ) = min (N x ,N y ) v=K x,y N x v N z -N x N y -v / N z N y , (1) where p(K K x,y ) is the predicted probability of having a number, K, equal to or greater than the observed number K x,y of common neighbours z of both x and y.
GODomainMiner data preparation In this paper, the CODAC approach is applied to discover new weighted GO-domain associations. In our Flat data files of SIFTS (June 2017), UniProt (June 2017), and InterPro (version 63. 0) were downloaded and parsed using in-house Python scripts. Associations between PDB chains and GO terms, and associations between PDB chains and protein domains (Pfam, CATH, and SCOP) were extracted from the SIFTS data. All CATH and SCOP domain families were transformed into their corresponding superfamilies, and all Pfam "repeat" and "motif " domain types were discarded. Associations between UniProt sequence accession numbers (ANs) and GO terms and AN-Pfam associations (as well as AN-CATH and AN-SCOP associations) were extracted from the UniPro-tKB/SwissProt and UniProtKB/TrEMBL sections of UniProt to give two datasets of UniProtKB/SwissProt associations and UniProtKB/TrEMBL associations, respectively.
Then, using the evidence code of the GO term, the associations in the SIFTS, UniPro-tKB/SwissProt, and UniProtKB/TrEMBL datasets were divided into two groups, namely one group for which the GO term evidence code indicated manual curation, and one group for GO terms with evidence code "inferred from electronic annotation" (IEA). Here, the resulting 6 datasets are called SIFTS, SIFTS-IEA, SP, SP-IEA, TR, and TR-IEA. Thus, there are 6 input tripartite graphs for each of the 9 combinations of the X and Y source datasets. All PDB chain IDs and UniProt ANs having identical sequences were clustered using the Uniref non-redundant cluster annotations  [17]. We do not make any distinction between the various possible manual evidence codes. However, we note that the GO_REF field for IEA currently covers 12 annotations sources, namely InterPro2GO, UniProt Keywords2GO, UniProt Subcellular Location2GO, EC2GO, UniRule2GO, UniPathway2GO, Ensembl Compara, Ensembl Fungi, Ensembl Metazoa, Ensembl Plants, Ensembl Protists, and the Gene Ontology Consortium.
Dataset weights and threshold scores For each of the 9 settings of this study, the weights assigned to each dataset have been optimised. The procedure is described in Methods (Algorithm 2) and is based on a ROC-plot analysis of the ranking of our Gold-Standard InterPro-based positive examples versus all other associations computed from all the datasets and considered as background. Then the best threshold is determined from the consensus scores calculated with the optimised set of weights, using the Gold Standard Training and Test subsets of positive and negative examples. Table  1  shows that our procedure gives greater weight to GO-Pfam associations from the IEA sections of the SIFTS, UniProtKB/SwissProt, and UniProtKB/TrEMBL than to associations from the experimental and manually curated sections of SIFTS and UniProtKB/SwissProt datasets.
In order to investigate this further, we re-calculated the AUC-based weight optimization with all IEA weights forced to zero (Additional file 1). This caused our optimal AUC to fall from around 0. 96 to less than 0. 60. This reflects the fact that in this setting, we do not consider the propagated InterPro2GO annotations in UniProtKB, and consequently GODomainMiner retrieves fewer Gold-Standard associations. However, as IEA annotations are extracted from several other data sources as well as Inter-Pro, setting the IEA weight to zero also excludes these other data sources (refer to previous section). We therefore decided to include all IEA data in the rest of this study.