dataesr/scientific-paragraphs-categorization huggingface dataset:
Data were acquired with the EZChrom Elite (Agilent Technologies, Santa Clara, California) software, exported as. dat converted into. asc and processed with CHROMuLAN v0. 79 (PiKRON Ltd. ). Data were calibrated against a low molecular weight standards mix (LMW Gel Filtration calibration kits, GE Healthcare, Buckinghamshire) with the following standards: Conalbumin (75 kDa), Ovalbumin (43 kDa), Ribonuclease A (13. 7 kDa), Aprotinin (6. 5 kDa) and Angiotensine (1. 1 kDa). Elution times were converted into molecular weight masses according to the calibration curve (Supplementary information 1).
Data analysis Results were expressed as the average of three replicates with the associated standard deviations. (-)FT-ICR-MS data were handled with  DataAnalysis (v. 4. 3, Bruker Daltonik GmbH). Calibrated data were filtered to keep only m/z peaks with a signal to noise (S/N) ratio above 10 and an absolute intensity higher than 2. 0x10 6. Peaks alignment was made by Matrix Generator software (v. 0. 4, Helmholtz-Zentrum Muenchen) with a mass accuracy window of 1 ppm  (Lucio, 2009). Peaks with intensity equal to 0 in more than 80% of samples were removed from the analysis. Finally, the in-house software NetCalc 2015 (v. 1. 1a, Helmholtz-Zentrum Muenchen) was used to annotate peaks  (Tziotis et al. , 2011). 46% of the initially aligned peaks were annotated by NetCalc and used for this study.
Van Krevelen diagrams, which plot the H/C against the O/C ratio of annotated metabolites were generated by an Excel file, providing instantaneous chemical pictures of the metabolites diversity  (Brockman et al. , 2018; Kim et al. , 2003). The OligoNet webserver was also used to annotate potential peptides with a max error of 1 ppm  (Liu et al. , 2017). The UHPLC-Q-ToF-MS data were calibrated internally by 1/4 diluted tuning mix with  DataAnalysis (v. 4. 3, Bruker Daltonik GmbH). Calibrated features were filtered to retain those with signal/noise ratio (S/N) higher than 30 and an absolute intensity of at least 1000. Before features extraction, the spectral background noise was removed. The extracted features were aligned with a R script with a m/z tolerance of 2 ppm and retention time tolerance of 0.
2 seconds. Parent ions and fragments were submitted to different databases with the MassTRIX interface (http://masstrix. org) (Suhre & Schmitt-Kopplin, 2008) , Metlin (https://metlin. scripps. edu) and YMDB 2. 0 (http://www. ymdb. ca) (Ramirez-Gaona et al. , 2017). According to the precision of UHPLC-Q-ToF-MS and (-)FT-ICR-MS, an error of 3 ppm and 1 ppm was chosen respectively for the annotation of the metabolites  (Roullier-Gall et al. , 2014; Roullier-gall et al. , 2015). The combination of these databases enabled covering the widest range of metabolites found in biological systems, and notably in yeast.
Fragments were submitted to metabolomic databases, and annotation of the parent's ions are summarized in Table  1. Following the new prescriptions of Sumner and collaborators, the identification point (which gives the identification confidence level) of each metabolite is 5. 5 (High resolution retention time, accurate mass of parent ion, molecular formula based upon accurate m/z and isotope pattern and accurate tandem mass spectrum)  (Sumner et al. , 2014). Most of these compounds were already described in yeast metabolic pathways, but never so far as compounds which could be potentially extracted from IDY in winemaking conditions. Most interestingly, glutathione and its precursor (glutamyl-cysteine) were found in the three extracts, but consistently with the enrichment process, i. e. with a relative abundance following the Gplus > G > N trend.
Metabolites fragmented by UHPLC-Q-ToF-MS/MS in the MWSF for the three IDYs, and their corresponding chemical family. Main fragments used to identify features from databases are listed in the discriminant fragments column allowing a high confidence level. Relative abundance of metabolites between IDYs is expressed as percentage of the biggest area. The identification is the combination of composition elucidation by (-)FT-ICR-MS (when available) and structural elucidation by UHPLC-Q-ToF-MS /MS.
ACKNOWLEDGEMENTS The authors acknowledge the  Regional Council of Bourgogne -Franche-Comté , the  "Fonds Européen de DEveloppement Régional (FEDER) " and  Lallemand SAS (31, Blagnac) , for financial support. They also would like to thank  Dr. Eveline Bartowsky  for a careful reading of the manuscript by a native English speaker. The authors wish to confirm that there are no conflicts of interest associated with this publication.
Preprocessing of Illumina reads and de novo transcriptome assembly The obtained raw data of each sample were initially cleaned by removing the adapters and filtering out the low-quality sequences (Q ≤ 5) and ambiguous reads sequences (the proportion of N > 5%) using Trimmomatic software  (Bolger et al. , 2014). Following quality check and normalization of data, for each species the clean data of all samples were merged and de novo assembled using Trinity  (Haas et al. , 2013)  with default parameters and an optimized k-mer length of 25 to generate a reference transcriptome. The program CD-HIT was then used with 98% of sequence identity and a word size of 10 (c = 98% n = 10) to eliminate redundant transcripts  (Fu et al. , 2012). Furthermore, the quality of the assembled transcriptomes was assessed by mapping the reads of individual samples to the assembled transcriptomes using bowtie2 v.
Identification of orthologs The reciprocal-best-BLAST-hits (RBH) has been considered the most robust and effective in identifying the orthologs genes between closely related species  (Osada et al. , 2008; Elmer et al. , 2010; Gao et al. , 2018). In our study, we applied this approach to identify orthologs between E. ivorense and E. suaveolens. Because the two studied species have diverged recently  (Duminil et al. , 2015) , we directly used nucleotide sequences to run BLAST search instead of peptide sequence in order to ensure accuracy of orthologs identification  (An et al. , 2016). First, the contigs sets of both species were searched against each other using BLASTN (package of ncbi-blast-2. 6. 0+) with a bit score ≥200 and an E-value < 1E-50  (Baldo et al. , 2011; Lee et al. , 2013). Then, to discard putative paralogs, each blast search was parsed to keep hits with at least 98% sequence identity, and a pair was considered orthologous if the best BLAST hit pointed reciprocally to each other.
Functional annotation All non-redundant transcripts were annotated by searching against the following databases: UniProtKB/Swiss-Prot, Pfam (version 31. 0) (Finn et al. , 2015) , Kyoto Encyclopedia of Genes and Genomes (KEGG: http://www. genome. jp/kegg/) (Kanehisa et al. , 2016) , and Non-supervised Orthologous Groups (eggNOG) (http://eggnog. embl. de/) (Huerta-Cepas et al. , 2015). Transdecoder 2. 0. 1 (http://transdecoder. sf. net) was first used to identify the protein coding sequences of at least 100 amino acids in length within the generated transcriptomes. The assembled transcripts and the protein coding sequences were then mapped against UniProtKB/Swiss-Prot database with an E-value cutoff of 1E-5 using Blastx and Blastp in the program ncbi-blast-2. 6. 0+, respectively. The protein coding sequences were also searched for conserved protein domains against the pfam database via HMMER v.
3. 1b2 (http://hmmer. org/) (Finn et al. , 2011). Using Trinotate software (https://trinotate. github. io/) (Haas et al. , 2013) , the obtained outputs were loaded into a SQLite database to generate an annotation report in order to perform Gene Ontology (GO) assignments for the annotated transcripts. GO functional classification was then carried out using the Web Gene Ontology Annotation Plot (WEGO: http://wego. genomics. org. cn) software  (Ye et al. , 2006). For KEGG pathway annotation, the assembled transcripts were mapped against KEGG database using online KASS server (http://www. genome. jp/tools/kaas/) (updated in March 2018) with the single-directional best hit information method  (Moriya et al. , 2007). Furthermore, the protein coding sequences were analyzed by eggNOG-Mapper in order to identify the Cluster of orthologous groups (COG)  (Huerta-Cepas et al.
Gene expression and exploratory analysis Gene expression level of the assembled transcripts of each sample was separately estimated using the FPKM (fragments per kilobase per million reads) metric by program RSEM (V 1. 2. 4), which allows for a quantification of the expression level of each transcript by assessing transcript abundance when mapping RNA-seq reads to the assembled transcriptome  (Li and Dewey, 2011). The clean reads of the analyzed samples were realigned to the final transcriptome assembly using Bowtie2 v. 2 with the aforementioned parameters in order to count the expression level of each transcript (transcript per million, TPM). To make a first visualization of the differences in gene expression among the different samples of the two species we conducted a Nonmetric Multidimensional Scaling (NMDS) ordination based on a Mahalanobis distance matrix between samples.
Identification and enrichment analysis of the differentially expressed genes (DEGs) For each species, the differentially expressed genes (DEGs) were then screened by comparing gene expression levels between the three stages of stress (T0, T1 and T2) using the edgeR R package  (Robinson et al. , 2010). Here, we considered all transcripts, irrespective of whether orthologs were identified or not. False discovery rate (FDR) of 0. 05 and log2 (fold change) of 2 were set as the threshold for significantly differential expression. For the comparative analysis of DEGs between E. ivorense and E. suaveolens in response to drought, GO enrichment, which is commonly used for elucidating the biological functions of genes  (Young et al. , 2010) , was performed using singular enrichment analysis (SEA). We used the agriGO program  (Du et al.
Functional annotation of genes TransDecoder identified 27,910 (41. 38%) and 27,382 (41. 11%) contigs with coding sequences (ORFs) in E. ivorense and E. suaveolens, respectively. Moreover, 28,589 (42. 39%) and 26,386 (39. 61%) contigs had significant match with the UniProtKB/Swiss-Prot database (E-value threshold ≤1E -5) whereas 16,042 (57. 47%) and 14,743 (53. 84%) protein sequences contain protein domains of the Pfam database. Furthermore, the Gene Ontology (GO) annotation showed that the two species considerably differ in the number of annotated transcripts with a total of 10,865 (10. 6%) and 20,365 (21. 08%) transcripts annotated for E. ivorense and E. suaveolens, respectively. However, the three major GO annotation categories were represented by similar percentages in the two species (Fig.
3 ). In terms of GO categories, 26 GO terms belonged to "Biological process" among which "cellular process" (73. 7%; 67. 2%) and "metabolic process" (61. 5%; 56. 5%) were the most  represented GO terms, 14 GO terms belonged to "Molecular function" of which "binding" (60. 2%) and "catalytic activity" (53%) were the most represented GO terms, and 13 GO terms belonged to "Cellular component" where "cell" (87. 9%; 81%) and "cell part" (87. 9%; 81%) were found the most dominant GO terms. Notably, 24 GO terms showed significant gene number differences (P < 0. 001) between the two species (Additional file 1). After searching against the eggNOG database, a total of 25,730 and 25,302 protein sequences were assigned to 24 functional categories in E. ivorense and E. suaveolens, respectively. All the identified COG categories were found similarly distributed in both species.
, 2012). Our results showed that, compared to E. ivorense, E. suaveolens presented earlier signs of wilting, which suggest that the latter species displayed earlier morphological response and probably earlier physiological and molecular responses to drought. Our transcriptomes generated from Trinity assembly showed similar characteristics in both species, with N50 and the mean length of the contigs being comparable to those reported in many Fabaceae species (e. g. ,  Wegrzyn et al. , 2016; Wong et al. , 2011)  and very similar G/C content (41%), consistent with observations in other closely related species  (Herraiz et al. , 2016). On the other hand, although displaying similar distributions of GO terms and KEGG pathways, the two species differed greatly in the number of annotated transcripts in these two databases.
These findings suggest that one difference between E. ivorense and E. suaveolens could be the rapidness of drought perception and the modulation of the pathways involved in drought response. On the other hand, we noticed that E. suaveolens displayed more up-regulated genes than E. ivorense over the drought treatment. Assuming that a large part of these genes may be encoding for signaling and metabolic pathways but not unannotated using GO and KEGG databases from E. suaveolens transcriptome, we suggest that these sets of up-regulated genes could be involved in the response to drought at its different stages and may confer better drought tolerance to this species. This is in line with previous comparative studies performed in Oryza sativa (Poaceae)  (Lenka et al. , 2011)  and Trifolium pratense (Fabaceae)  (Yates et al.
displaying significant change in response to drought stress (P value ≤ 0. 05), the exclusive and shared DEGs were mapped to terms in the KEGG database (Additional file 4). After 2 weeks of stress, 28. 76% (21/73) and 66. 66% (174/258) of the specific DEGs identified in E. ivorense and E. suaveolens were associated with 20 and 64 KEGG pathways, respectively. In contrast to E. ivorense, in which only one gene encoding for 'Plant hormone signal transduction' was significantly enriched, 61 DEGs (48 down-regulated/13 up-regulated) were found greatly enriched in E. suaveolens. The down-regulated genes were mainly associated with eight metabolic pathways from which photosynthesis (12 DEGs) and glycolysis/gluconeogenesis (6 DEGs) and oxidative phosphorylation (6 DEGs) were the most represented, and three signaling pathways: plant hormone signal transduction (4 DEGs), AMPK signaling pathway (4 DGEs) and calcium signaling pathway (3 DEGs).
Acknowledgements We thank  Wallonie-Bruxelles International (WBI)  for the provided Postdoctoral Fellowships attributed to M. N. The genetic analyses were funded by the  F. R. S. -FNRS  (grants n°J. 0292. 17Fand T. 0163. 13 )and the  Belgian Science Policy Office (Belspo , project  AFRIFORD ). Computational resources have been provided by the  Consortium des Équipements de Calcul Intensif (CÉCI) , funded by the  Fonds de la Recherche Scientifique de Belgique (F. R. S. -FNRS)  under Grant No. 2. 5020. 11. We would like also to thank  Dr. Marie Baucher  ( FNRS-FRS ) and an anonymous referee for their constructive comments on previous versions of the manuscript.
Sequencing and assembly using stitching The elite breeding line IT97K-499-35, developed at the International Institute of Tropical Agriculture (IITA, Nigeria), was used previously for the development of genome resources  (Timko et al. , 2008; Muñoz-Amatria ın et al. , 2017). Here, a fully homozygous (single haplotype; see Experimental procedures) stock was sequenced using Pac-Bio (Pacific Biosciences of California, Menlo Park, CA, USA) single-molecule real-time (SMRT) sequencing. In total, 56. 8 Gb of sequence data were generated (~91. 7 9 genome equivalent), with a read N50 of 14 595 bp. Preand post-filter read length and quality distribution are reported in Figures  S3-S6. Two Bionano Genomics (San Diego, CA, USA) optical maps  (Cao et al. , 2014)  were generated using nicking enzymes BspQI and BssSI (Tables  S1  and S2 ).
PacBio Quiver enables consensus accuracies on genome assemblies approaching or exceeding Q60 (one error per million bases) when the sequencing depth is above 60 9  (Chin et al. , 2013). All of the assembly statistics significantly improved compared with the eight individual assemblies (Table  S3 ). For instance, the N50 for the stitched assembly (10. 9 Mb) was almost double the highest N50 for any of the eight individual assemblies. Similarly, the longest contig for the stitched assembly increased by 4 Mb over the longest contig of any single assembly. Scaffolds were obtained by mapping the stitched and polished assembly to both optical maps using the Kansas State University pipeline  (Shelton et al. , 2015). Briefly, a total of 519. 4 Mb of sequence scaffold were generated with an N50 of 16.
Several sequence datasets that were independently generated were mapped onto the assembly using BWA-mem with default settings, namely: (i) about 168M 149-bp paired-end Illumina reads (98. 92% mapped of which 86. 7% were properly paired and 75. 53% had MAPQ of at least 30); (ii) about 129 000 contigs (500 bp or longer) of the wholegenome shotgun (WGS) assembly generated previously (Muñoz-Amatria ın et al. , 2017; 99;. 69% mapped of which 98. 69% had MAPQ > 30); (iii) about 178 000 BAC sequence assemblies generated previously (Muñoz-Amatria ın et al. , 2017; 99;. 95% mapped of which 68. 39% had MAPQ > 30); and (iv) about 157 000 transcripts  (Santos et al. , 2018; 99. 95% mapped of which 94. 74% had MAPQ > 30). All of these metrics indicate agreement with the pseudochromosomes. The original PacBio reads were also mapped onto the assembly using BLASR using default settings: 5.
A set of 368 diverse cowpea accessions, including 243 landraces and 97 breeding accessions for which iSelect data existed, was used to estimate the frequency of the inversion among germplasm accessions. A total of 33 accessions (9%) had the same SNP haplotype as the reference genome across the entire region, which presumably indicates the same orientation. Among those 33 accessions, only three were landraces (1. 2% of the landraces in the set), while the other 30 were breeding materials, including the reference genome. This suggests that the reference genome orientation of this region is rare among landraces and that its frequency has been increased among breeding lines. Also, a complete lack of recombination across this region is reflected in the genetic map derived from a cultivated 9 wild cross  (Lo et al.
Gene family changes in cowpea To identify genes that have significantly increased or decreased in copy number in cowpea, 18 543 families from the Legume Information System (https://legumeinfo. org/ search/phylotree and https://legumeinfo. org/data/public/Ge ne_families/) were analyzed. This set was constructed to capture genes originating at the legume taxonomic depth, based on orthology relationships and per-species synonymous-site rates for legume species and outgroup species. These families include 14 legume species, six of which are from the Phaseoleae tribe (soybean, common bean, adzuki bean, mung bean, pigeon pea and cowpea). Among the 185 gene families in the top percentile in terms of cowpea gene membership in the family relative to average The Plant Journal, (2019), 98, 767-782 membership per legume species, the families include several in the following superfamily groups: NBS-LRR disease resistance genes, various receptor-like protein kinases, defensins, ribosomal proteins, NADH-quinone oxidoreductase components (Data S7).
Five-thousand events were acquired in each measurement. The resulting histograms of relative DNA content (Figure  S1 ) comprised two major peaks representing nuclei in the G1 phase of the cell cycle. The ratio of G1 peak positions was used to calculate the amount of DNA of cowpea. Five different plants of IT97K-499-35 were analyzed, each three times on three different days, and the mean 2C DNA amount was calculated. Genome size was determined using the conversion factor 1 pg = 0. 978 Mbp  (Dolezel, 2003). To estimate the cowpea IT97K-499-35 genome size using k-mer distribution, 168 M 149 bp paired-end Illumina reads were processed for a total of about 50 billion bp. Figure  S2  shows the frequency distribution of 27-mers produced with KAT (https:// github. com/TGAC/KAT). The x-axis represents the 27-mer multiplicity, the y-axis represents the number of 27-mers with that multiplicity.
Bionano Genomics optical maps High-molecular-weight DNA was isolated by Amplicon Express (Pullman, WA, USA) from nuclei purified from young etiolated leaves (grown in the dark) of 100% homozygous, pure seeds of cowpea IT97K-499-35. The material was screened for homozygosity by genotyping with the Cowpea iSelect Consortium Array (Muñoz-Amatria ın et al. , 2017; Data S8). The nicking endonucleases Nt. BspQI and Nb. BssSI (New England BioLabs, Ipswich, MA, USA) were chosen to label DNA molecules at specific sequence motifs. The nicked DNA molecules were stained according to instructions of the IrysPrep Reagent Kit (Bionano Genomics) as per  Luo et al. (2017). The DNA sample was loaded onto the nanochannel array of an IrysChip (Bionano Genomics) and then imaged using the Irys system (Bionano Genomics).
0 (P/N 100-642-800). The standard MagBead sequencing protocol followed the DNA Sequencing Kit 4. 0 v2 (P/N 100-612-400), which is known as P6/C4 chemistry. PacBio RS II sequencing data were collected in 6-h movies and Stage Start was enabled to capture the longest subreads possible. Sequence quality control. First, CLARK and CLARK-S  (Ounit and Lonardi, 2016)  were used to identify possible contamination from unknown organisms. CLARK and CLARK-S are classification tools that use discriminative (spaced, in the case CLARK-S) k-mers to quickly determine the most likely origin of each input sequence (k = 21 and k = 31). The target database for CLARK/CLARK-S was comprised of: (i) a representative sample of ~5000 bacterial/viral genomes from NCBI RefSeq; (ii) human genome, Homo sapiens, assembly GRCh38; (iii) Illumina-based cowpea draft genome, V.
HINGE v0. 41  (Kamath et al. , 2017)  was also tested on this dataset, but at that time the tool required the entire alignment file (over 2 Tb) to fit in primary memory and we did not have the computational resources to handle it. CANU v1. 3 was run with different settings for the error correction stage on the entire dataset of ~6 M reads (two CANU runs were optimized for highly repetitive genomes). Falcon and Abruijn were run on 3. 54 M error-corrected reads produced by CANU (30. 62 Gbp, or 49. 4 9 genome equivalent). Each assembly took about 4-5 days on a 512-core Torque/PBS server hosted at UC Riverside. Removal of contaminants from the assemblies. To remove 'contaminated' contigs, two sets of reference genomes were created, termed the white list and the black list. Black-list genomes included possible contaminants, whereas white-listed genomes included organisms evolutionarily close to cowpea.
The polishing step took about 7 days on a 40-core server at UC Riverside. Scaffolding via optical maps. Scaffolds were obtained from the polished assembly via the Kansas State University (KSU) stitching pipeline  (Shelton et al. , 2015)  in multiple rounds. A tool called XMView (https://github. com/ucrbioinfo/XMView) developed in-house that enables the visual inspection of alignments of assembled contigs to two optical maps simultaneously, also displaying consensus genetic map coordinates for SNPs, was used to identify chimeric optical molecules that had to be excluded from the scaffolding step. The KSU stitching pipeline was iterated four times, alternating BspQI and BssSI (twice each map) at which point no conflicts remained. Pseudochromosome construction via anchoring to genetic maps. Pseudochromosomes were obtained by anchoring the scaffold sequences to SNP markers (BLAST of SNP design sequences, e À50 or less) in 10 genetic maps (Table  S4 ).