dataesr/scientific-paragraphs-categorization huggingface dataset:
This would combine the universal primer fidelity and high taxon coverage possible with ITS, with equally robust primers for secondary barcodes specific to the group or taxon of interest, enhancing precision of species identification. To take an analogy from medical diagnostics, this is an iterative diagnostic process, where the results of a primary analysis (ITS sequencing) can be used to determine what secondary analyses should be performed  (Irinyi et al. 2015a, b). In adopting additional barcodes, the obvious absence of complete reference data is a serious problem, and was one of the main arguments presented against considering Cox1 a primary barcode for fungi (other problems eventually emerged, see e. g. Gilmore et al. 2009). To be truly effective for specialised and non-specialised applications, such as phytopathology (plant diseases control and quarantine), clinical applications (diagnostics, disease control, epidemiology) or environmental studies (ecology, conservation, metagenomics), formally adopted barcodes will need to be highly predictive, and thus comprehensive reference datasets will need to be developed.
2012)  corresponding to the section 983-1567 bp (in the rust Puccinia graminis), two novel universal high fidelity TEF1α primer alternatives covering approximately the same region, three sections of the newly identified candidate region and fungus-specific gene, translation elongation factor 3 (TEF3)  (Ypma-Wong et al. 1992 , Belfield et al. 1995 , Belfield & Tuite 2006 , Greganova et al. 2011) , and one of the small ribosomal proteins required for tRNA transfer, the 60S L10 (L1)  (Brodersen & Nissen 2005). From the study of  Lewis et al. (2011)  based on Pfam domains, we tested ITS against the seven genes that could theoretically amplify a wide range of fungal taxa with a single primer pair, namely, phosphoglycerate kinase (PGK, PF00162), DNA topoisomerase I (TOPI, PF02919), Lipin/ Ned1/Smp2 (LNS2, PF08235), Indole-3-glycerol phosphate synthase (IGPS, PF00218), Phosphoribosylaminoimidazole carboxylase PurE domain (PurE, PF00731), Peptide methionine sulphoxi de reductase (Msr, PF01625) and Vacuolar ATPase (vATP, PF01992).
Fungal cultures and specimens For the barcode primer search by the complete genome approach of  Robert et al. (2011) , axenic cultures preserved and deposited in several biological resource centres, private collections and fungaria were used as sources for DNA. Tested fungal cultures are circumscribed according to their higher level taxonomic affiliation, main taxa (species level), quantity per 'dataset' source and corresponding collaborator in Table  1. A detailed taxon list for each 'dataset' is available upon request. Cultures were either activated from lyophilised or cryopreserved material and inoculated on various media, such as oatmeal (OA) and 2 % malt extract (MEA, Oxoid) agars, prepared according to  Crous et al. (2009). Alternatively, preserved cultures were directly used for DNA isolation from lyophilised or cryopreserved stocks.
The selected 'datasets' covered several important taxa within the Agaricomycotina, Pezizomycotina, Pucciniomycotina, Saccharomycotina and Ustilaginomycotina. In total 1 931 fungal strains and fungarium specimens representing > 1 500 fungal species (exact taxonomic status for some strains was not precisely determined 'spp. ') were selected for benchmarking newly designed against published primers. For the barcode primer search by the Pfam domain approach of  Lewis et al. (2011) , fungal cultures were obtained from a number of laboratories at the Eastern Cereal and Oilseed Research Centre, ECORC, of Agriculture and Agri-Food Canada, CEF, Ottawa. They were supplied as fungal cultures from which DNA was extracted or as already purified DNA samples.
8. 0 (www. bio-aware. com) (Vu et al. 2012). Edited sequences were exported to and aligned with MAFFT v. 7. 0  (Katoh et al. 2005 ) and further corrected for indels and SNPs (single nucleotide polymorphisms) by replacing respective positions with ambiguity code letters. For the primers inferred from the complete proteome through the Pfam approach by  Lewis et al. (2011) , genomic DNA from fungal cultures was extracted using the OmniPrep™ Genomic DNA Extraction Kit (G-Biosciences, St. Louis, Missouri). Fungal tissue was ground into a fine powder in a mortar and pestle with liquid nitrogen and stored at -80 °C. Approximately 50 mg of ground tissue was placed in a 1. 7 mL microfuge tube, resuspended in 250 µL of lysis buffer and vortexed for several seconds. An additional 250 µL of lysis buffer was added to the resuspension and the tube was incubated for 15 min at 55-60 °C without the addition of Proteinase K.
The in silico pipeline for identifying suitable protein sequences for primer design is briefly described below. The approach was inspired by the CARMA algorithm for taxonomic identification of metagenomic sequences  (Krause et al. 2008). CARMA matches short environmental gene fragments to Pfam protein families and constructs a taxonomic profile for the sample. The objective is to assign translated input nucleotide sequences to Pfam accessions that can identify single copy gene regions in source organisms and then design degenerate primers from the alignment of these putatively orthologous sequences. This requires pre-processing the Pfam dataset, translating and assigning DNA sequences to Pfam groups, and adding sequences to the reference Pfam alignments. The CARMA pipeline was adapted for use in this project, although the original intent and our application are quite different.
Data were processed in two stages. The first stage translates input sequences and assigns them to Pfam accessions; the second stage prepares alignments and attempts primer design for the selected set of taxa. Adding additional or updating data for existing organisms requires repetition of the first stage. Restricting the analysis to a taxonomic subset of the data requires that the final stage be repeated for the selected organisms. The pipeline requires up-to-date versions of the Pfam-A and NCBI taxonomy datasets; if the Pfam dataset is updated, the first stage of processing must be repeated. The input datasets are placed in a single folder for processing, with each organism in a unique directory named by 'genus_species_strain'. The pipeline first processes source nucleotide sequences to identify Pfam domains within each sequence and creates clusters of conserved domains.
Final primer design Two different genome and proteome mining strategies were employed to identify the initial potential new barcode markers  (Lewis et al. 2011 , Robert et al. 2011 ) and different strategies were also used to develop them further and for validation. The selected gene regions by the complete genome approach of  Robert et al. (2011)  corresponding to a total of 54 alignments (available on request) were manually re-annotated using n-and x-BLAST and alignments resulting in identical annotations were realigned and redundant alignments discarded. These seed primers are given in Table  3. To design PCR primers, nonredundant alignments were imported into BioEdit v. 7. 0. 52 and visually screened for the most conserved sites to identify suitable primer binding sites. Priority was given to stable 3' prime ends with at least eight nucleotide binding sites and the least possible amount of degeneracy.
For several primers, 'N's were replaced with an inosine, 'I's, to improve binding stability and to reduce the quantity of primers in the actual synthesized wobble pool. Individual primer pairs are discussed in the results section. For the Pfam domain approach by  Lewis et al. (2011) , a nucleotide search was first conducted within NCBI for Interpro names to help with primer validation, using keywords found within the paper (e. g. , PF01625, PGK, IPR001576) and NCBI was searched for more recent accessions using trimmed amino acid sequences for each of the inferred protein regions as queries. The downloaded nucleotide sequences were added to the original data from  Lewis et al. (2011). A ClustalW alignment was performed individually on the supplied trimmed sequences and NCBI downloaded sequences.
Locus Original primer name Final primer name Sequence (5' to 3') PGK PF00162. 1120. M13-8-F PGK_480-F TGTAAAACGACGGCCAGTACGATATCCGAGTCGACTTCAAYGTCCC PF00162. 2081. M13-8-R PGK_480-R CAGGAAACAGCTATGACTCGAAGACACCRGGGGGACCGTTCCA PF00162. 1433. M13-8-F PGK_483-F TGTAAAACGACGGCCAGTACGATGAGAACYTGCGHTTCCACRYYGAGGAGGARGG PF00162. 1793. M13-8-R PGK_483-R CAGGAAACAGCTATGACCTTCTTGAAGGTGAARGCCAT PF00162A. 675. 1-F PGK_511-F GTYGSTGCYYTGCCMACCATCAA PF00162A. 1915. 1-R PGK_511-R ATCTTGTCRGMRACCTTRGCACC PF00162. 1127. 1-F PGK_533-F GTYGAYTTCAAYGTYCC PF00162. 2081. 1-R PGK_533-R ACACCDGGDGGRCCGTTCCA TOP1 PF02919. 2708. M13. 1-F TOP1_501-F TGTAAAACGACGGCCAGTACGATACTGCCAAGGTTTTCCGTACHTACAACGC PF02919. 3469. M13. 8-R TOP1_501-R CAGGAAACAGCTATGACCCAGTCCTCGTCAACWGACTTRATRGCCCA LNS2 PF08235. 1463. 8-F LNS2_468-F GGCCATGTGCTGAACATGATCGGHCGWGAYTGGAC PF08235.
All data were analysed and visualised with R statistical software (R Core Team 2014) with the 'lattice', 'vcd' and 'MASS' libraries, as well as related packages; the source code is available from  Sarkar (2008). Primer amplification efficiencies were visualised as 'barchart' and 'mosaicplot'. Gene maps with primer locations were created with the Qiagen CLC genomics suite (http://www. clcbio. com/products/clc-genomics-workbench/) primers designed by the complete genome approach by  Robert et al. (2011)  and with Geneious v. R6 (Biomatters http://www. geneious. com/) for the primers designed from Pfam domains  (Lewis et al. 2011). For sequences generated from primers designed by the complete genome approach by  Robert et al. (2011) , sequences were stored as bidirectional reads and edited as described above, using BioloMICS  (Vu et al.
2012 ) at CBS. Various software packages, differing between laboratories, were employed to assemble consensus sequences. Sequences were visually corrected for sequencing artefacts and quality controlled within individually aligned datasets. Quality controlled data was uploaded to the BioloMICS database and pairwise distances for selected (symmetric/orthogonal matrices only) datasets calculated using the optimistic reverse pairwise alignment algorithm. Symmetrical datasets were assembled to include sequence data for at least: complete ITS1-5. 8S-ITS2, LSU, TEF1α (either from one of the newly designed TEF1α primer pairs, see Results), or the TEF1α AFTOL primer set EF1-983F / EF1-1567R, Table  2 , spanning almost the same section), TEF3, and the section spanning the 60S ribosomal protein L10 (L1).
The global dataset comprised 502 strains or specimens representing a fully symmetrical distance matrix with 5 gene partitions. Each dataset was analysed individually to create a single locus similarity matrix to generate an overview UPGMA tree. A second analysis used all loci in a concatenated matrix, followed by a multi-dimensional scaling analysis (MDS), visual-ised in n-dimensional space. All six similarity matrices (5 loci + 1 concatenated) were compared, to obtain pairwise cophenetic coefficients of correlation (Mantel test) between each similarity matrix  (Smouse et al. 1986). A correlation super matrix was subsequently created, which was numerically rescaled and another final MDS performed; the results were plotted in an n-dimensional space to compare the 'global' between-loci performance.
Eigenvalues for each axis were computed to assign weights to dimensions. Two examples, the Onygenales and Fusarium datasets were analysed individually as described above, with identical loci sampling, except for the Onygenales which was supplemented with 'γ-Actin' sequence data to provide examples on 'local gene optima'. For sequences generated from primers designed by the Pfam approach of  Lewis et al. (2011) , bidirectional reads were edited manually using Geneious to generate consensus sequences. Sequences were aligned through MAFFT within Geneious and processed through R to compare intra-and inter-species pairwise distances  (Schoch et al. 2014 ). TUB2 and TEF1 sequences of Penicillium and Fusarium strains, respectively, that were already available were added to the dataset as comparison for species resolution.
The aligned sequences of each marker were analysed individually with the 'ape' package  (Paradis et al. 2004 ) for R (R Core Team 2014) to generate raw pairwise distances for each marker and each pair of strains. The values were separated as intra-or inter-species distances and the function ggplot2  (Wickham 2009)  was used with R to generate a barcode gap plot for each gene by overlaying the distribution of the intra-and inter-species distances.
Trial III In the last experimental phase, primer testing was extended to species-specific DNA sets. These extracts were received from 10 collaborating taxonomic experts at CBS (Table  1 ) in sets of 96 extracts. This more extensive testing evaluated the capacity of sequences yielded by the selected primer pairs for their power to delimit closely related fungal species. The extracts represented relevant groups such as medically or phytopathologically relevant fungi, or were selected because reference datasets were available for comparison, or because the taxa represented complexes of species that were previously shown to be difficult to resolve. The performance of several primer pairs amplifying TEF3 (alignment groups 47, 49, 50 and 51) and newly designed primers for TEF1α (alignment groups 17, 28, 31, 33) was consistently excellent.
Amplification efficiencies Optimal visualisation of multivariate data matrices, showing relative proportions of 'within' and 'between' categories (i. e. datasets vs primers), was achieved with the 'mosaic plot' function using 'lattice' in R  (Sarkar 2008) , with our categorical variables encoded as typical 'survival data'. Each plot is sectioned in rows representing datasets and columns representing primer pairs separated by primers designed by the complete genome approach (Fig. 11a, b ) and primers designed by the Pfam approach (Fig. 11c, d ). Horizontal expansion of boxes indicates the ratio of PCR reactions within a 'dataset' or taxonomic group relative to the global quantity (proportion) of all other PCR values (outcome), and vertical expansion of boxes indicates the ratio of individuals within quantity (proportion) of PCR values (outcome) of a specific primer test (e.
g. ITS). Horizontal lines indicate value 'zero'. When a line with a dot is on the left, it equals 'zero' positives (= no amplification), and when the line with a dot is on the right, it indicates 'zero' negatives (= 100 % amplification). Darkly shaded boxes indicate proportion of amplifications, relative to no amplification, which are shown by pale grey shaded boxes. Mosaic plots outperform other plot types in visualising large quantities of data and provide a global overview on data proportions only. The frequencies of positive and negative amplification for each primer pair are shown in Fig. 11b  and d. The mosaic plot shows that the overall amplification testing between laboratories to test the primers designed through the complete genome approach  (Robert et al. 2011 ) was strongly        biased towards novel primer pairs, revealed by the many horizontal lines in some datasets corresponding to the ACT, TUB2, TEF1α (AFTOL primers), ITS, LSU, and RPB2 pairs (Fig.
11a ). Data corresponding to these pairs was mainly obtained from PCR experiments performed at CBS, and lack of data from collaborating laboratories can simply be explained by time and financial constraints. However, the results of these amplification experiments provide an important benchmark, even when inconsistently assessed, and valuable information on the universal performance of certain pairs. Promising results were obtained for several amplicons. The TEF1α region, with the AFTOL pair (EF1-983F/EF1-1567R), enjoyed relatively consistent amplification for different taxon sets, with exceptions being relatively poor results for the Onygenales and rock-inhabiting Teratosphaeriaceae (80 % average). Results for sections of the fungal rDNA operon, especially the complete ITS and partial LSU (D1/D2 domains), were high in efficiency for all taxon sets, 92 % and 91 %, respectively (Fig.
11b ). These results confirm the outstanding potential of ITS as primary universal fungal DNA barcode; it performs exceptionally well under standard laboratory conditions. No particular distinction was made between the possible forward primer combinations using ITS5/ITS1/ITS1-F. Similarly, LSU had very similar amplification efficiencies with LR0R/LR5, and augments the useful species-level signal ITS with increased phylogenetic information. The results for RPB2 (fRPB2-5f/fRPB2-7cR) were less encouraging; only five taxon sets were tested for a single primer combination and the global performance of RPB2 can only be partly judged from our data. Two very commonly known gene sections widely employed in fungal phylogenetics (Fig. 2 , 3), ACT and TUB2, had relatively consistent amplification efficiencies among datasets (mostly grey shaded boxes), showing that these pairs qualify well for universal amplification (84 % ACT and 86 % TUB2 on average, respectively).
Multi-dimensional scaling of sequence data Results of the Mantel test  (Smouse et al. 1986 ), comparing pairwise distance matrices of: i) the 'global 502 taxa dataset' (Fig. 13a , b, i); ii) 'Fusarium' (Fig. 13c, d  (Fig. 13e , f, h) by pairwise cophenetic coefficients of correlation, were plotted (numerically rescaled 0 -1) in n-dimensional space. Rescaled cophenetic coefficients for each dataset are shown in Table  8. Assessment of global performance employing a comparison of TEF1α distances matrices (with sequences obtained from pair EF1-1018F/EF1-1620R; AFTOL pair EF1-983/EF1-1567 combined), TEF3 (sequences obtained from pair EF3-3185F/EF3-3538R, EF3-3188F/EF3-3984R combined), 60S, ITS and LSU for 502 taxa, showed that the first two axes contributed more than 75 % of the total information and the first three axes over 90 %.
While 60S and ITS were rendered closely together and apart from LSU, neither TEF1α nor TEF3 were correlated closely to one of the rDNA matrices (Fig. 13a, b ). The most significant result from visualising the cophenetic coefficients of correlation is apparent in Fig. 13a  and i. The 3D plot and hierarchical clustering indicated that TEF1α correlated optimally with the overall concatenated (ALL) data matrix. Scoring of individual genes (matrices) is equivalent to distances as obvious from the dendrogram in Fig. 13i. The comparison of the 'Fusarium' matrices showed the same trend with the first three axes describing the vast majority of variance. Our results show that optimal correlation of the global dataset (ALL) was achieved with the 60S matrix (Fig. 13c, d, g ), and the universal barcode ITS correlated poorly with the concatenated (ALL) object.
The impact of the second dimension is obvious, because TEF3 is more distant from the concatenated matrix than ITS in the second but not the first dimension. Therefore, the weighted impact of dimensions causes the performance of TEF3 to appear inferior to ITS, presumably an artefact of distance over true character-based (cladistic/phylogenetic) inference  (Felsenstein 2003). This could also be related to comparisons of cophenetic coefficients employing the Mantel test  (Harmon & Glor 2010). Visualisation of the results from comparing data matrices obtained from the 'Onygenales' dataset contradicted those of the '502 taxon dataset'. We observed that the combination of two genes, ITS and TEF3, resulted in the ordination highly resembling the one of the concatenated (ALL) matrix. The impact of the third dimension scaled the LSU matrix more distantly than the two latter genes in Fig.
13e  but was less pronounced in Fig. 13f. Second closest, and in addition to the two previous datasets, ACT was inferred as inferior to 60S and ITS when compared to the overall dataset. Both elongation factor matrices, TEF1α and TEF3, were inferior to ITS and were both distant from the overall concatenated matrix (ALL). Hierarchical clustering of individual matrices performance against the overall dataset is shown in Fig. 13h. In general, TEF1α performed the best among all '502' investigated taxa when comparing 5+1 gene datasets, which was biased by local optima. The best candidate genes for Fusarium (60S) and Onygenales (ITS and 60S) rendered one of our new candidates as an optimal 'local' barcode for these two sets. The pairwise comparison for each dataset and each gene is given in Table  8  by rescaled (0-1) cophenetic correlation coefficients, with key results reflected by MDS analysis (Fig.
DNA-barcoding: a conceptual overview In the early 1960s some milestone papers by  Edward & Cavalli-Sforza (1964) ,  Zuckerkandl & Pauling (1965)  and  Hennig (1965)  gave rise to the novel field now known as molecular phylogenetics  (Felsenstein 2003) , including some decades later 'DNA barcoding'. The goal of DNA barcoding is to apply high-throughput DNA sequencing technology to large-scale screening of one or a few reference genes to identify unknown specimens to species, and enhance discovery of new species  (Hebert et al. 2003a , b, Stoeckle 2003). Proponents of 'DNA-based taxonomy' envisage the development of comprehensive databases of reference sequences, centred on voucher specimens or living cultures that represent all described species, against which sequences from newly sampled individuals can be compared.
Given the long history of use of molecular markers for this purpose for identifying microbes (e. g. 16S rDNA for prokaryotes, Avise 2004), there is nothing fundamentally novel about DNA barcoding as applied in microbiology and mycology today, except for the increased scale and proposed standardisation. Standardisation by the selection of one or more reference genes is critical and stimulates large-scale phylogenetic analyses, but whether 'one gene fits it all' is still an open debate. Initial reactions to the coordinated DNA barcoding movement ranged from great enthusiasm, especially among ecologists Gene / Dataset Gene  (Janzen 2004) , to condemnation, largely from traditional taxonomists. The proponents emphasized its application to modern meta-barcoding studies, increasing the precision and efficiency of field studies of rare, diverse and difficult to identify taxa across all kingdoms of organisms, while DNA barcoding is not possible without large-scale and well curated sequence databases  (Schoch et al.
In our study we have particularly tested fungal taxa of economic or applied importance. We foresee that a high fidelity secondary DNA barcode would be included in a very large portion of ongoing taxonomic studies. Based on our results and anticipating community acceptance, we propose the use of primers EF1-1018F and EF1-1620R (corresponding to TEF1α) as secondary universal DNA barcode primer pair for the fungal kingdom. Given the results of this extensive four-year multi-lab project, we are convinced that fungal DNA barcoding with ITS and TEF1α for the identification of unknown or validation of 'primary-barcoded' specimens will increase the resolution and lead to the development of more complete, structured datasets. Global consensus might also speed up the discovery and description of novel taxa in a standardised way.
Acknowledgements Primer development and testing by partners in the  European Consortium of Microbial Resource Centres (EMbaRC)  was supported through funding of the European Community's  Seventh Framework Programme  (FP7, 2007-2013),  Research Infrastructures action , under grant agreement no. FP7-228310. Part of sequencing work in CBS was supported by  Fonds Economische Structuurversterking (FES) ,  Dutch Ministry of Education, Culture and Science  grant  BEK/BPR-2009/137964-U ). WM and VR were supported by research grant  NH&MRC  #  APP1031952. Genome mining at  CBS  and  AAFC , and primer development and testing at AAFC, were supported by grants from the A. P. Sloan Foundation Programme on the Microbiology of the Built Environment. We acknowledge the  Deanship of Scientific Research (DSR) ,  King Abdulaziz University , under grant No.