dataesr/scientific-paragraphs-categorization huggingface dataset:
Plant material and phenotypes We analyzed data from the IITA cassava breeding program in Nigeria, including a fraction (689 clones, i. e. , genetically unique individuals, each of which is clonally propagated) of the Genetic Gain (GG) collection, which comprises 709 elite and historically important clones. With these we analyzed 2302 clones developed as the cycle 1 of IITA's Genomic Selection (GS) breeding program. In total, 3011 clones were used as source for phenotypes. For further details on the populations used, see Wolfe et al. 24, 43. We analyzed 5 traits: Dry matter content (DM), mean cassava mosaic disease severity (MCMDS), root number (RTNO), shoot weight (STWT) and fresh yield (FYLD). The phenotyping trials used in this study have been described before and all phenotype data is provided in the supplementary table  S2.
21. Briefly, LDAK5 relationship matrices control short-range LD by assigning marker weights. Markers residing in low LD regions will have higher weights and are assumed to contribute more than markers in high LD regions. The LDAK5 model also assumes that a SNP's heritability depends on its MAF, using an α value set to -0. 25 as suggested in  21. Finally, LDAK5 considers genotyping uncertainty as higher-quality observed markers should contribute more than poorly imputed markers. Genomic relationship matrices were calculated for the enhancer candidate partition and the rest of the genome partition. Separate analyses used Genomic relationship matrices based on the three random sets. Python scripts for these analyses can be accessed at the GitHub repository associated to this article. The model fit to calculate the variance components was specified in matrix notation as:  =  +.
+  +  +  Where Y represents a vector with raw phenotypic observations, β represents the intercept and X is a vector of ones. The random effects include a single intercept for each location-year combination in which phenotypic trials took place and where l ~ N(0, Iσ 2 l) where I is the identity matrix and σ 2 l is the associated variance component. The genetic variance components include g and h where g ~ N(0, GRMFσ 2 g ) and h ~ N(0, GRMRσ 2 h ). These two terms have known covariance structure calculated using LDAK5 for the focal (GRMF) or ROG (GRMR) partitions. The incidence matrices Zloc. year, and Z relate observations to the levels of trials and clones, respectively. Variance components were estimated using the 'emmremlMultikernel' function implemented in the 'EMMREML' R package  50. There is no standard significance test for contrasting the alternative enhancer candidate hypothesis to the null model random set hypothesis (Deniz Akdemir, pers.
In silico combinatorial restriction digestion Using BioEdit (http://www. mbio. ncsu. edu/bioedit/page2. html), we identified 54 restriction enzymes that would not cleave in any of 71 tal gene BamHI fragments from X. oryzae that were retrieved from GenBank. From this analysis, two restriction enzymes, ApaLI (GTGCAC) and SfoI (GGCGCC), were selected for further analyses. Genome sequences of nine X. oryzae strains were then used for in silico combinatorial restriction digestion using Microsoft Office software (Microsoft Word and Microsoft Excel) (Supplemental Table  1 ). First, each genome sequence was converted into a consecutive list of BamHI fragments in Word. Upon conversion from text to table format, all virtual DNA fragments were transferred to an Excel spreadsheet. In Excel, fragment sizes and absence/ presence of ApalI and SfoI sites were scored using appropriate formulas.
DNA fragments were sorted by the absence/presence of the two restriction sites and the size of virtual BamHI fragments. tal generelated BamHI fragments were identified by TBLASTN searches. Finally, data were re-analyzed assuming isolation of 2-5 kb DNA fragments prior to cloning. Assuming that only DNA fragments flanked by two BamHI overhangs would be cloned, but none of the fragments that contain only one BamHI overhang (which could nevertheless occur by head-to-tail ligation of two fragments flanked by one BamHI overhang and one overhang from the other two enzymes), one would expect to obtain between 10% and 20% of tal gene BamHI fragments without size fractionation and between 50% and 90% of tal gene BamHI fragments with size fractionation (2-5 kb DNA fragments). However, our simulation revealed that this procedure would miss to isolate tal-gene related DNA fragments from truncated tal genes (two in PXO86, one in KACC10331, one in BAI11), which nevertheless are of functional relevance  [5, 6].
Atomic Force Microscopy (AFM) imaging. The AFM equipment used here is a Picoscan system from Molecular Imaging. It was primarily used in contact mode to record friction images (lateral deflection). In this case, AFM contact mode probes with a typical stiffness of approximately 0. 2 N/m were used (Contact-G tips from BudgetSensors). Scanning was performed at 1 line/sec with an image resolution of 512 × 512 pixels. Friction measurements were very effective in revealing chemical contrast. Unfortunately, the friction measurement could not be used quantitatively because the recorded data depend on many parameters related to the tip surface and geometry, applied force, scanning speed, etc. Phase images of block copolymer surfaces were recorded using resonant AFM. In this case, Si 3 N 4 tips with a typical resonance frequency of 285 kHz (stiffness 42 N/m, ACT tips from AppNano) were used.
XPS. XPS analysis was performed on a Gammadata Scienta (Uppsala, Sweden) SES 200-2 X-ray photoelectron spectrometer under ultra-high vacuum (P < 10 -9 mbar). The monochromatized AlKa source (1486. 6 eV) was operated at a power of 420 W (30 mA and 14 kV), and the spectra were acquired at a take-off angle (TOA) of 90° (angle between the sample surface and photoemission direction). During acquisition, the pass energy was set to 500 eV for wide scans and to 100 eV for high-resolution spectra. CASAXPS software (Casa Software Ltd, Teignmouth, UK, www. casaxps. com) was used for all peak fitting procedures, and the areas of each component were corrected according to classical Scofield sensitivity factors (Si2p: 0. 817, C1s: 1. 00 and O1s: 2. 93) and the transmission function of the spectrometer. All components on high-resolution spectra were referenced according to the CH x component at 285.
0 eV. The reproducibility of atomic quantification obtained on a Scienta spectrometer is estimated to be less than 10%. It is also worth noting that due to the low thickness of the films, there is no need for charge compensation in our system, and consequently, degradation occurring in the organic film during an XPS experiment is limited. Because the XPS experiments are time consuming, we could not reproduce each piece of data several times. Only the HTS-grafted surface was characterized several times because this chemistry was used in different applications. Block copolymer thin films. The diblock copolymer used in this study was PS-b-PMMA, (43000 and 21000 g/mol) provided by Polymer Source Inc. It was dissolved in toluene at a concentration of 5 mg/ml. This polymer self-assembles into cylinders of PMMA in a PS matrix with a period of 35 nm  34.
The versatility of the current DNA sequencing platforms and the development of portable, nanopore sequencers means that it has never been easier to collect genetic data for unknown sample ID. DNA barcoding and metabarcoding have become increasingly popular and barcode databases continue to grow at an impressive rate. However, the number of canonical genome assemblies (reference or draft) that are publically available is relatively tiny, hindering the more widespread use of genome scale DNA sequencing technology for accurate species identification and discovery. Here, we show that rapid raw-read reference datasets, or R4IDs for short, generated in a matter of hours on the Oxford Nanopore MinION, can bridge this gap and accelerate the generation of useable reference sequence data. By exploiting the long read length of this technology, shotgun genomic sequencing of a small portion of an organism's genome can act as a suitable reference database despite the low sequencing coverage.
2012; van Velzen et al. 2012; Mallo & Posada 2016; Liu et al. 2017). Broader, genome-scale identification methods hold a great deal of potential to bridge this gap. These approaches remove ambiguity from species-level identifications and produce information rich data that may be more applicable to species delimitation  (Coissac et al. 2016; Hollingsworth et al. 2016; Parker et al. 2017). Indeed, we have previously shown that field-based, genome scale sequencing is able to provide accurate species identification in closely related Arabidopsis species  (Parker et al. 2017 ). This study used carefully curated whole genome sequences of A. thaliana and A. lyrata as a reference database (against which newly generated long-read data generated in the field was matched) to explore the potential of this type of data for species identification.
All types of accurate taxonomic identification from DNA sequence data are reliant on reference databases of DNA sequences obtained from specimens identified morphologically by experts. This is exemplified by the rigor with which the most high quality DNA barcode databases are constructed (IBOL)  (Hebert et al. 2016a ) and highlights the important role that taxonomist, taxon-specific expertise and collections based institutions (such as natural history museums and botanic gardens) have played and will continue to play in expanding the utility of DNA-based identification. Increasingly high throughput methods of producing reference DNA barcodes are being developed, but the rate at which reference genomic data is published is substantially slower. Currently, whole genome sequences are available in public repositories (INSDC) for only 2733 eukaryotic species and these are distributed unevenly across the tree of life -874 bilatarians, 274 plants and 1237 fungi.
Demonstrated applications have included epidemic monitoring in Guinea  (Quick et al. , 2016) , extremophile sequencing in Antarctica  (Michael et al. , 2017) , assembly of complete plant genomes  (Johnson et al. , 2017) , species ID and phylogenomics in the field  (Parker et al. , 2017). Sequencing reads from these platforms are orders-ofmagnitude longer than those generated by more established, sequencing-bysynthesis technologies. Few tools or techniques are currently optimised for this technology and these developments present opportunities to revisit existing orthodoxies, analyses and uses of DNA data. In this era of mobile genomic sequencing, can genome-scale reference datasets be produced at a rate that species-level ID from genome resequencing will become a practicable and widely applicable reality? High quality reference genomes are still challenging and time consuming to produce, despite the incredible pace at which new data can be generated.
This opens the possibility for rapid and simple generation of a genomic reference database to match the scale of barcode databases, with the added benefit that reference sequences can be generated in the field by expert taxonomists, conceivably at the points at which new species discoveries are made. Our approach to live identification by real-time DNA sequencing and analysis comprises three steps: a 'R4ID-training' step, in which samples of known origin are sequenced as low coverage whole-genome shotgun libraries and converted to a panel of BLAST databases (being optionally combined with public assemblies); a 'R4ID-resequencing' step, in which samples of unknown origin are resequenced, also as whole-genome shotgun libraries, and aligned in real-time to the R4IDs databases using BLASTN; and a 'R4IDvisualisation' step by which live results are displayed as a web application with a graphical user interface (GUI).
Sequencing and live ID In the week prior to the Kew Science Festival in 2016, we extracted genomic DNA used Qiagen DNEasy Plant Miniprep kits from five vascular plant species: Sorbus aria (Rosaceae); Beta patula (Amaranthaeae); Nepenthes alata (Nepenthaceae); Erycina echinata (Orchidaceae); and Silene uniflora (Caryophyllaceae). Sample details, ploidy level and estimated genome size (taken from the Kew C-values Database) are given in Table  1. Estimated genome sizes for these species, or congenerics, range from 0. 28pg to 1. 9pg. To prepare rapid, raw-read reference ID datasets ('R4IDs'), wholegenome shotgun libraries were prepared for R9 Oxford Nanopore MinION sequencing using rapid-family (SQK-RAD001) transposase-based kits and protocols, which require less than 20 minutes to prepare. Samples were sequenced for variable run times and basecalled online via Metrichor's EPI2ME platform.
This generated megabase-scale genomic data for each species (40. 4Mbp -71. 7Mbp; see Table  2 ), with estimated genomic coverage ratios ranging from approximately 3% (S. uniflora) to 24% (N. alata). N50 sizes of 1. 6-2. 7 Kbp and maximum read lengths from 66Kbp-144Kbp highlight the exponential distribution of read lengths. Raw reads from each species were compiled into a separate BLAST (NCBI-BLAST+; v2. 5. 0) nucleotide database. The following week, live at the Kew Science Festival, libraries were prepared from extracted DNA samples and resequenced, two per day. Sample selection was randomised without replacement by an assistant (AJH) such that the sample identity was blinded to the experimenters (JP and ASTP). Live basecalling was used to generate results in real-time. Reads were continuously processed by matching each new read to each of the five R4ID species BLAST databases in turn, retaining single best hits with e-value cutoff set at 1.
0 and default gap / match parameters and word sizes. Cumulative BLAST alignment lengths ('length') and number of identities ('nidents') were used to infer the identity of the unknown sample, assuming that the longest cumulative aligned read lengths would correspond to the correct sample. A simple graphical user interface (GUI; Figure  1 ) was displayed over a local network and projected live to science festival visitors. Code for the ID process and web interface is available at http://github. com/lonelyjoeparker/real-time-phylogenomics. All five species' identities were correctly inferred, with the fastest sample load-to-correct guess time being approximately 15 minutes (Table  3 ). Further resequencing was continued in the laboratory each night to collect further data; total sequencing yields for sample resequencing approximately mirrored those for R4ID training-run sequencing (Table  4 ), with the exception of S.
Evaluation of empirical R4IDs To determine the expected performance of our five-way BLAST ID method at varying levels R4ID training DB and resequencing 'sampling', Python scripts were used to resample the R4ID and resequencing datasets, without replacement, at five levels each: 100, 500, 1000, 5000 and 10,000 reads -producing a total of 25 combinations. This was repeated for each of the five species and 20 replicates, for 625 separate BLAST searches per replicate, collecting alignment length, % identities and e-values for each hit. In every case, aggregate hit alignment length (the statistic used in the science festival live-sequencing GUI) favoured the correct species, regardless of R4ID / training run sampling or resequencing effort (see Figures  2  and 3 ). To analyse the expectation of correct true positive read identifications in more depth, BLAST outputs were parsed to collect the following information: total BLAST hits; one-and two-way true positive hits (reads matching only the correct database, or with longest aligned length to that database); one-and two-way false positive hits (reads matching only an incorrect species database, or with the longest match to an incorrect species); mean length bias (read-wise aligned length for correct species -next longest species aligned length); twoway hit correct percentage (number of two-way hits correctly classified if a length bias > 0 was used for classification); and two-way hit correct % with 50bp threshold (e.
g. reads only classified if length bias > 50bp. ) Scripts and R Markdown notebooks for these analyses are available online at https://github. com/lonelyjoeparker/oddjects-sandbox/tree/master/R4IDs. In order to assess the capacity for individual reads to provide accurate species ID, we calculated the per read accuracy (ratio of true positives to true & false positives; Figure  S1 ). This ratio exceeds 50% at most sampling levels, including as low as 100 reads, however, the pattern is principally driven by R4ID training effort, with 5,000 or more reads likely to be sufficient for >60% correct IDs per read. We also estimated the expected total number of positive reads as a function of total resequencing effort when the threshold for the aligned length bias statistic used for species assignment is set to greater than zero bp (less conservative; Figure  S2a ) or greater than 50bp (more conservative; Figure  5b ).
Code is available for the nanopore sequencing simulation at the above Github repository. The outcome of the simulated R4ID experiments are displayed in the same format as for the empirical experiment (Figures  4  and 5 ). As with the empirical and resampled data from wider phylogenetic comparisons, this group of closely-related species showed the expected aggregate length biases that were greater than zero even for comparatively low amounts of R4ID sequencing and sample resequencing effort (1000 or more reads in each case). Again, the R4IDs database sequencing effort was a principal driver of accuracy. Figure  S3  shows how true-and false-positive hit rates vary with number of reads sequenced for the R4ID database; once 10,000 reads have been sequenced, the true-positive hit rate (~0. 55) considerably exceeds the false-positive rate (<0.
2). Since the assembled genome lengths for these closely-related species vary from ~100Mbp to ~650Mbp, we examined how this affected the ID rate for each species (Figure  9 ). A greater number of hits were observed at lower sequencing sampling effort for A. thaliana (the smallest genome, at approximately 119Mbp) compared with C. sativa (the largest, at 641Mbp). This is unsurprising, since each R4ID database of a given size represents a lower fraction of the genome as the genome size increases. Our real-time analysis pipeline has been packaged up as deployable Docker containers, making it simple for anyone to generate R4IDs ('training'), evaluate resequenced data ('resequencing') and display results on a local web server ('visualisation'). These are available from Docker Hub at https://hub.
Discussion We have shown that even partial genomic sequencing using long-read nanopore data provides an adequate training dataset for future resequencing. This demonstrated that numerous potential uses for DNA sequence data that are impeded by availability of reference sequences may now be exploited with the rapid generation of non-specific genomic sequencing. Intuitively, this may seem unsurprising, but our results clearly demonstrate how little data is needed for a successful sample ID. The sequencing effort is low both in terms of sample resequencing and more importantly in terms of the 'reference' genome sketch generated from a sample of known origin. In each case, our empirical, resampling, and simulation results (including closely-related species) show that as few as 10,000 reads are easily sufficient to achieve reliable sample ID with nanopore long-read sequencing.
At the time that the empirical data was collected (August 2016), 30-120 minutes' sequencing were required per species/sample. MinION platform yields have greatly improved since then, as has the data accuracy -Oxford Nanopore's informal 'Poreboard' competition currently stands at >11Gbp sequencing from a single flowcell, while the longest single mapped read now stands at 1. 3Mbp with 97% accuracy (M. Loose, pers. comm. ). In this context, training datasets for multispecies R4ID matching could easily be collected in a few hours' sequencing. To identify unknown samples by resequencing and comparison against previously-sequenced R4ID databases even more rapid. Our live sequencing at the Kew Science Festival 2016 managed 5 out of 5 correct identifications of blinded samples in as little as 15 minutes.
Recent improvements in platform throughput suggest that this time is likely to fall in the near future. We observed three main drivers of identification efficiency and accuracy. First, the level sequencing effort employed in the generation of the R4IDs training datasets. Second, the size of the target genome, since this will interact with sequencing effort to give the expected genome coverage fraction. Thirdly, the phylogenetic distance between candidate species: our expected hit fractions were lower, though still acceptable, in our in silico simulations on closely-related species. Our overall approach is well-validated by these empirical and in silico experiments. One possible reason for the success of this method may be the abundance of plastid and other organelle material present in extracted DNA.
Outline genomic mapping analyses using this data and data from our previous work  (Parker et al, 2017)  show a high prevalence of chloroplast genomic material with nearly complete plastids being assembled post hoc. This is unlikely to be the sole cause as whole plastid genome sequencing is demonstrably not a panacea for species identification, however, the combination of abundant plastid sequences and more limited matching to nuclear data may be an important factor. In addition to the phylogenetic distance mentioned larger phylogenetic distances in the empirical dataset, this detail may explains why the empirical data outperform the simulated data in key respects. Typical applications for this approach could include rapid digitisation of collections and biodiversity evaluations, particularly where applications and sampling opportunities are limited to on-site data collection (perhaps for CITES permit reasons) or time-limited (as in the case of transient and unexpected events, such as the Deepwater Horizon rig fire and subsequent contamination of the Gulf of Mexico (partially sampled post-hoc by  Mason et al.
Furthermore, the data from other barcoding approaches can easily be incorporated into R4ID databases, building upon the valuable and highly curated barcode datasets that are already available. Molecular systematics based on multiple loci also have clear advantages over barcode-based approaches, since they allow models which account for reticulate evolution and horizontal gene transfer  (Pryron, 2015; Mallo & Posada, 2016). In addition, it has the clear advantage that truly genomic data can be captured both in the training and resequencing phases, for future use. This is important, because it can reasonably be expected that low-coverage data sequenced for R4ID training or sample identification can yield useful downstream data for genomic, metagenomic or phylogenomic studies With improving read accuracy collecting these kinds of broad genomic sketches/skims could revolutionise the way in which we delimit species and gather population genetic data.
Acknowledgements This work was carried out at the Kew Science Festival 2016, with support from the  Kew Foundation. Oxford Nanopore  provided flowcells, reagents and technical support free-of-charge but have had no editorial input into downstream analyses, or preparation of this manuscript. We are grateful to Kew colleagues in the  Science Directorate  for assistance with sequencing logistics, and in the Horticultural Directorate for assistance with sample collection; and to  Gaetan Lee  and colleagues for assistance and funding for the  Kew Science Festival  exhibit.