dataesr/scientific-paragraphs-categorization huggingface dataset:
Additionally, exon-intron junctions were deduced from the mapping positions of the reads. Junctions supported by more than 3 reads were incorporated as evidence in Augustus. Assembled transcripts were aligned to the assembly and clustered in novel transcript models using PASA  43. A high quality full-length non-redundant subset from the ab initio transcript models from PASA was used as a training set for Augustus. The proteins of the Fabaceae family in Uniprot and TrEMBL, and the transcripts annotated in the soybean, common bean, and M. truncatula genomes were aligned to the masked genome using Exonerate  44. Gene models were predicted by GeneID  45  and SNAP  46  in the masked version of the assembly, and by Augustus in the unmasked version of the assembly. Augustus builds the gene models to be compatible with the information from the alignments, the transcript models and junctions deduced from the RNA-Seq data, and annotated repeated regions and transposons.
Additionally, alternative transcript models of a gene were reported for those incompatible with the provided alignments and transcript information. Finally, RNA-Seq reads were mapped again using the guidance of the generated annotation by Tophat, a new set of transcripts assembled by Cufflinks and alternative splicing incorporated in the annotation by PASA. The annotated features were stored in an EnsEMBL database to allow visualisation and exportation. The functional annotation of the proteome was done with an in-house pipeline (AnnotF) that compares the results of Blast2GO 47 and InterProSCAN  48. Clustering was based on eggNOG clusters. Genes within pathways were compared with RAxML 8. 0. 22  49  (100 bootstrap replications). The proteomes of four Fabaceae species and A. thaliana were aligned, and single gene clusters filtered and concatenated after removing gaps using HAL  50.
A phylogenetic tree based on these data was built with MEGA6 51 using Maximum-likelihood and 100 bootstrap replications. Divergence times in the phylogenetic tree were calculated with the RelTime method 52 in MEGA6 using the divergence date between common bean and soybean as reference  6. The gene density was calculated by dividing the total number of genes by the total length of each genome. Additionally, we calculated the same value for each interval of 10 Kb along the genome. The distribution of these values is reported as "mean number of genes in 10 Kb intervals". Syntenic blocks were identified with MUMMER  53  , analysed with SyMap 54 , using the default parameters, but with the requirement of 30 gene pairs to call a syntenic block, and plotted with Circos  55. The syntenic gene pairs were aligned with MAFFT v 7  56  and the alignments used to calculate the Kimura rates with MEGA6 in order to estimate the nucleotide divergence rates.
Acknowledgements This work was funded by an  Institute Programme Grant  to IBERS ( BB/J004405/1 ) from the  Biotechnology and Biological Sciences Research Council (BBSRC) , by the  ERANET Plant Genomics programme  ( ERAPG038A-TRANSLEG ) and by a Capacity, Capability Challenge Programme from TGAC. The R-script for the landscape plots of LD, the predicted r 2 and the average LD was kindly provided by  Lei Wang ,  Department of Molecular Biology and Genetics, Aarhus University, Denmark , and modifications of the R-scripts for red clover were provided by  Dr Nastasiya F. Grinberg ,  Manchester Institute of Biotechnology, Manchester University, UK.
S. Food and Drug Administration (FDA); this was a change from the original DHEW report that emphasized the importance of heritable germ cell damage. Moreover, database analyses suggested that potency measured using in vitro tests was not a reliable indicator of in vivo potency  [13, 14]  and the relative insensitivity of the in vivo tests  [15, 16]  discouraged their use outside of a battery of tests designed for hazard identification. As a result, regulatory testing and decision-making have until recently been based largely on qualitative outcomes of in vitro and/or in vivo genotoxicity tests, leading to dichotomous classification of an agent as genotoxic/mutagenic/clastogenic/aneugenic, or not, with cancer as the principal genetic disease of concern. More recently it has become recognized that qualitative outcomes of in vitro genotoxicity tests do not adequately correspond to the outcomes of rodent carcinogenicity bioassays or with in vivo genotoxicity.
Software packages designed for performing the calculations needed for BMD analysis include PROAST  [61] ) and the EPA's BMDS software  [62] ). Both are available for download and use without cost: (http://www. rivm. nl/en/Documents and publications/Scientific/Models/PROAST; http://www. epa. gov/ ncea/bmds/dwnldu. html). (iii) The threshold or breakpoint dose lower confidence bound (BPDL), determined using a bi-linear model, has also been used in human health risk assessment  [63]. In this approach, a bilinear dose response function is fit to the data: it is composed of two intersecting straight lines with the low-dose line having a zero slope. Examples of such models are those described by Lutz and Lutz  [64]  and Muggeo  [65]. A statistical lower bound on the point of intersection of the two lines is considered an estimate of the threshold dose (highest dose with no change in response, or BPD) and can be used as a PoD.
Since most genotoxicity assays currently employed do not have the statistical sensitivity to detect less than a doubling of the spontaneous rate (i. e. , a 100% increase in the spontaneous rate), the choice of a BMR of 10% results in a PoD that is approximately 1/10 (or less for certain assays) of the detectable NOGEL, (4) The BMDL approach is very flexible, can be readily applied to a wide range of datasets, is minimally affected by dose spacing, and requires only three treatment levels. Dose spacing is more critical for determination of the NOGEL and BPD, and BPD analysis requires approximately six treatment groups, (5) The BMDL 10 and BMDL 1SD values are essentially always lower than the NOGEL and are therefore more conservative. Although a large number of data sets from different types of assays were evaluated, rigorous comparisons were limited to a relatively small sample of structural classes of genotoxicants (i.
database described by Gollapudi et al. [29]  and Johnson et al. [30]  and of R-based procedures for derivation of genetic toxicology PoDs (i. e. , drsmooth), and for support of open access of this publication, was provided by Health Canada under the Chemicals Management Plan Research Fund. We thank the ILSI/HESI Quantitative Working Group for sharing data and analyses collected and employed by that Working Group, and note that their analyses were a critical component of our deliberations. Appendix 1. List of abbreviations and definitions 1  BMC (Benchmark Concentration): The concentration of a substance that is associated with a specified low incidence of response of a biological effect, generally in the range of 1-10%. BMCL (Benchmark Concentration Low): A lower one-sided confidence limit on the BMC.
Acknowledgements We acknowledge support from the  National Centre for the Replacement, Refinement and Reduction of Animals in Research  ( NC3Rs , contract number  NC/K500033/1 ) for  Dr. Johnson. Funding  for the development of the G4 genetic toxicity dose-response DHEW:  U. S. Department of Health, Education, and Welfare. EFSA:  European Food Safety Authority. EMA:  European Medicines Agency. EMS: Ethyl Methanesulfonate. ENU: N-Nitroso-N-methylurea,  Ethyl Nitrosourea. FDA:  U. S. Food and Drug Administration. HHRA: Human Health Risk Assessment. ICH: International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use. ICH S2(R1): ICH guideline S2 (R1) on genotoxicity testing and data interpretation for pharmaceuticals intended for human use.
See also: http://www. epa. gov/ncea/bmds/bmds training/appendices/glossary. htm, Benchmark Dose Software (BMDS), Appendix C: Glossary of Terms, APPENDIX C, GLOSSARY OF TERMS; http://ofmpub. epa. gov/sor internet/registry/termreg/ searchandretrieve/glossariesandkeywordlists/search. do, Vocabulary Catalog List Detail -Integrated Risk Information System (IRIS) Glossary, Terms & Acronyms; Gollapudi et al. , EMM, 54, 8-18,.
The A 260 /A 280 ratio for the Repli-g amplified DNA was 1. 83. Gel electrophoresis indicates that the Repli-g amplification worked well, although it appears to have preferentially amplified the longer fragments in the reactions (> 2 kb; data not shown). The Cot >940 DNA was processed for 454 sequencing at GATC-Biotech (Koblenz, Germany) using the GS-FLX technology (Roche, Boulogne Billancourt, France) following established manufacturer's protocols  (Margulies et al. , 2005). Half an FLX run was performed. Sequence assembly was achieved using the Roche Diagnostic GS de novo Newbler Assembler, and repeated sequences were eliminated using Repeat Masker software (http:// www. repeatmasker. org/).
Microarray probe design eArray software (https://earray. chem. agilent. com/earray/; Agilent technologies, Massy, France) was used to design transcriptomics and CH3 microarray probes corresponding to 60-mer oligomers based on the 42 004 predicted open reading frames (ORFs) and 6231 contigs, respectively, and on public sequence information from a large collection of sugar beet expressed sequence tags (ESTs; ~30 000) and the mitochondrial genome from the NCBI (http://www. ncbi. nlm. nih. gov/ nucest/?term=beta%20vulgaris). Sugar beet genes known to be involved in the floral transition were added, such as FLC  (Reeves et al. , 2007) , FT  (Pin et al. , 2010) , or CONSTANS-LIKE  (Chia et al. , 2008). Previous studies on sugar beet revealed 39 vernalization markers  (Hébrard et al. , 2013)  and 21 morphogenesis markers  (Maury et al.
RNA preparation and microarray hybridization Total RNAs were independently isolated from shoot apical meristems after 9 weeks of vernalizing treatment in three bolting-sensitive (S1, S2, and S3) and three bolting-resistant (R1, R2, and R3) sugar beet genotypes using a Nucleospin ® RNA Plant kit (Macherey-Nagel, Hoerdt, France). One shoot apical meristem per genotype was used. To confirm the absence of genomic DNA in all the RNA preparations, a control PCR was performed on RNA. The DNA contamination and RNA integrity with RNA integrity number (RIN) measurement (RIN threshold of 8) were estimated by the use of a bioanalyzer 2100 (Agilent Technologies). Sugar beet total RNA was used to generate fluorescent cRNA (complementary RNA), according to Agilent Technologies' recommendations. Briefly, to be used as a positive control for labelling and data analysis, RNA spike mix, which is a mix of 10 polyadenylated RNAs synthesized in vitro from the E1A adenovirus gene, was obtained with the One-Color RNA Spike-In Kit (Agilent Technologies).
Data collection, normalization, and cluster analysis The single nucleotide polymorphism (SNP) collection was obtained in a previous study  (Adetunji et al. , 2014)  by genotyping elite sugar beet breeding lines (from SESVanderHave) and wild beet accessions. A G2505C scanner (Agilent Technologies) was used to scan microarray slides, and extraction of raw signal data was carried out using Feature Extraction 10. 7 software. The reproducibility and reliability of each single microarray were evaluated using Quality Control report data (Feature Extraction, Agilent Technologies). One-colour transcriptomics data were normalized per chip to the 75th percentile and per probe to medians and were transformed to log values. Feature Extraction flag criteria allowed data filtering: non-outlier population, not saturated; uniform and significant probe signal, probe signal above background.
A probe signal was considered as 'well above background' if gBGSubSignal was >2. 6 gBG_SD. A probe needed to pass all flag criteria for all replicates of at least one experimental condition (bolting-resistant or bolting-sensitive) to pass the filtering. A t-test was performed with MeV software  (Saeed et al. , 2003)  to determine if genes were differentially expressed between experimental conditions. Statistical tests were considered significant at *P<0. 05. Benjamini-Hochberg's correction was used for false-positive discovery. The second filtering criterion which was used is a 2-fold change in transcript levels among conditions. Average linkage hierarchical clustering analysis was applied using Pearson correlation distance. For the CH3 microarray, data normalization was performed by Feature Extraction software and used to correct cyanine bias with the linear method: the mean signal of probes selected for quality criteria (i.
e. signal above background, non-outlier, and whose ranks are similar between the two dyes) allowed the normalization of all probes values. The Cy5/Cy3 log ratio was then calculated for each probe. Data filtering was performed using Feature Extraction flag criteria: non-outlier population, not saturated; uniform and significant Cy3 and Cy5 probe signal, Cy3 and Cy5 probe signal above background. A probe signal was considered as 'well above background' if g/rBGSubSignal was >2. 6 g/rBG_ SD. A probe needed to pass all flag criteria for all replicates of at least one experimental condition (bolting-resistant or bolting-sensitive) to pass the filtering. A t-test was performed with MeV software  (Saeed et al. , 2003)  to determine if genes were differentially methylated between experimental conditions.
Bioinformatic analyses Reads were mapped on the sugar beet genome  (Dohm et al. , 2014;  The Beta Vulgaris Resource: bvseq. molgen. mpg. de) using CLC Genomics Workbench software (Qiagen). Contigs and ORFs were compared with the sugar beet genome using BLASTN. Differentially expressed and differentially methylated genes were annotated (i) by TBLATSX analyses using the Arabidopsis database from the National Center for Biotechnology Information (http://blast. ncbi. nlm. nih. gov/Blast. cgi); and (ii) by BLASTX analyses using The Beta Vulgaris Resource (bvseq. molgen. mpg. de). The Gene Ontology (GO) class enrichment was produced with Classification SuperViewer Tool w/ Bootstrap (http://bar. utoronto. ca/ntools/cgi-bin/ntools_classification_superviewer. cgi). These analyses were confirmed using Blast2Go (http:// www.
Quantitative PCR Approximately 500 ng of total RNA were reverse transcribed using SuperScript™ III First-Strand Synthesis SuperMix for qRT-PCR kit (Invitrogen). Primers were designed with QuantPrime software (http://www. quantprime. de/) and are listed in Supplementary Table  S1  at JXB online. Constitutively expressed genes encoding, respectively, a UBIQUITIN and an ATP SYNTHASE BETA SUBUNIT (accession nos BI544002 and BQ490219) were used as internal standards. To confirm the absence of genomic DNA in all the total RNA preparations, a control without reverse transcriptase during the cDNA synthesis was performed. PCRs were prepared using an automated system epMotion 5070 (Eppendorf). Amplification and analysis were performed using a Platinium SYBR Green qPCR Supermix-UDG kit (Eurogentec) on a Mastercycler Realplex (v1.
Isolation of sugar beet single-copy sequences by Cot filtration and next-generation sequencing (Cot-SEQ) In order to obtain more gene sequence data for sugar beet, which has a 1223 Mbp genome  (Bennett, 1972)  containing >63% repetitive DNA  (Flavell et al. , 1974) , Cot filtration was used to isolate single/low-copy sequences which were subsequently sequenced using 454 technology (Supplementary Fig. S2  at JXB online). An average read length of 353 bp was obtained for the 466 386 reads, corresponding to 117 949 495 nucleotides (Supplementary Fig. S3 , Supplementary Table  S2 ). The 466 386 reads were assembled into 6231 contigs, including 2211 large contigs (>500 bases, 832 bases average size, 14 860 bases largest size), and covered 2 953 454 bases (Supplementary Table  S2 ). Large contigs have been deposited at DDBJ/EMBL/GenBank under the accession no.
JMBQ00000000. About 78% of the total reads remained as singletons. Bioinformatic analyses revealed that single-copy sequences represented 98. 6% of the covered bases while repeated sequences covered only 1. 4%, mostly with small RNAs and simple repeats (Supplementary Table  S3 ). Only 1. 0% of the contigs (Supplementary Table  S4 ) were mapped on the recent version of the reference sugar beet genome. Analysis of contigs identified thousands of potential ORFs that were blasted against public databases. These ORFs revealed very low sequence redundancy (1. 5%), and 3. 9% showed significant homology to previously annotated genes (Supplementary Tables  S3, S5 ). GO annotations indicate that those sequences with homology to known genes represent a wide range of biological processes (Supplementary Fig.
Trancriptomic microarray analysis of vernalized sugar beet genotypes with distinct bolting tolerance To explore transcriptomic variations between sugar beet genotypes with distinct bolting tolerance levels, a custom longoligonucleotide (60-mer) microarray (8 × 56k) was generated using the Agilent SurePrint ink-jet technology and hybridized with total RNA extracted from the shoot apical meristems of each genotype after 9 weeks of cold exposure (Supplementary Fig. S2  at JXB online). Transcriptomic data are available in the GEO database (GEO accession no. GSE55951). Of the 53 752 probes (29 094 from public databases and 24 658 from Cot-SEQ; Table  1 ), 22 448 (41. 8%) remained after the filtration and normalization steps (Supplementary Fig. S5  at JXB online), mainly from the public databases probes.
Genes with a lower relative expression level exhibited homogeneous expression levels between genotypes of the same group (R or S), while genes with a higher relative expression level exhibited variations among genotypes, allowing a clustering in agreement with their bolting tolerance (Fig. 2A ). TBLASTX analysis of the 169 DEGs revealed annotations for 125 of them using the Arabidopsis gene database (Supplementary Table  S7  at JXB online). Based on the TAIR percentage analysis, GO classification of these 125 sequences according to the 'Biological Process' component placed the largest proportion of genes in undefined categories such as 'Other metabolic processes' (31. 9%), 'Unknown biological process' (31. 9%), 'Other cellular processes' (27. 9%), but also in 'Response to abiotic and biotic stimulus' (24.
S8 ). A heatmap is shown in Fig. 3A  with the 111 sequences which all had a higher relative methylation level in S genotypes, allowing a clustering in agreement with the bolting tolerance of the six genotypes. From the 111 DMRs, TBLASTX analysis detected 50 with annotations in the Arabidopsis gene database (46 with known function and four uncharacterized; Supplementary Table  S8  at JXB online). Based on the TAIR percentage analysis, GO classification of these 50 sequences according to the 'Biological Process' component placed the largest proportions of genes in undefined categories such as 'Other metabolic processes' (18. 9%) and 'Other cellular processes' (17. 6%), but also in 'Developmental process' (8. 8%), 'Response to abiotic and biotic stimulus' (8. 1%), and 'Response to stress' (7.
Transcriptomic and methylation microarray analysis of the sugar beet mitochondrial genome The sugar beet mitochondrial genome (368 kbp; GenBank accession no. BA000009) was used to design oligonucleotides for the custom transcriptomic (132 probes) and methylation (3688 probes) microarrays. After normalization, 106 transcriptomic probes and 3211 methylation probes were kept (Table  1 ) and mapped on the mitochondrial genomic map for R and S groups (Fig. 4 ). After filtration (t-test at P<0. 05), only two DEGs were identified: ORF104a and CYTOCHROME OXIDASE C subunit 2 (COX2). DNA methylation probes showed a succession of peaks all along the genome in both S and R genotypes, but R genotypes had a significantly higher relative methylation level compared with the S lines (at P<0. 001). The non-coding regions also had a significantly higher relative methylation level compared with the coding regions (at P<0.
Candidate sequences related to the genotypic bolting tolerance In order to identify new candidate genes for bolting tolerance, the 14 sequences found to be both differentially methylated and differentially expressed between R and S genotypes were selected (Table  3 ). TBLASTX analyses proposed significant annotations for nine of them: four are involved in biosynthesis of various compounds such as suberin and cutin polymers (FG345845, a HXXXD-TYPE ACYL-TRANSFERASE-LIKE PROTEIN), triterpenoids (FG343208, a BETA-AMYRIN SYNTHASE), carotenoids (BQ582907, a CAROTENOID EPSILON-RING HYDROXYLASE LUT1/CYP97C1), or ethylene (FG344953, a 1-AMINOCYCLOPROPANE-1-CARBOXYLATE OXIDASE-LIKE PROTEIN; Table  3 ); BQ586261, BQ489455, and BQ585496 encode a GERMIN-LIKE PROTEIN 5 (GLP5/PDGLP1), an ATPASE E1-E2, and an SPX DOMAIN CONTAINING PROTEIN 4 (SPX4), respectively, and are involved in early development, endoplasmic reticulum to Golgi vesicle-mediated transport, and response to phosphate starvation, respectively.
Epigenomic approaches in a crop plant Genomic resources for crop plants such as sugar beet are crucial to elucidate complex physiological processes such as vernalization and bolting. When this study was started, the only public resource for sugar beet genomic sequences was a collection of ~30 000 ESTs in the NCBI dbEST database. However, the sugar beet genome has been released recently, opening up new perspectives  (Dohm et al. , 2014). EST sequencing only provides data on the coding regions expressed in a given tissue or set of tissues. Therefore, Cot filtration, another reduced-representation sequencing technique without the bias for expressed sequences, was employed to obtain low-copy sequence regions including expressed genes, promoters, introns, and non-expressed genes  (Peterson, 2005).
0% mapped on sugar beet genome), the genetic distance between the genotype and the reference genome, or the quality of the actual version of the reference genome. In addition, fragments from the same gene have probably been re-assembled into several contigs, artificially increasing the number of ORFs used for probe design: 42 000 ORFs were predicted using the data, while only 27 421 protein-coding genes are predicted in the published genome assembly  (Dohm et al. , 2014). Therefore, a high proportion of predicted ORFs were 'unreal' and only 218 ORFs mapped on the genome, representing a very low proportion of potentially 'efficient' probes. In addition, 184 ORFs showed sequence homology to genes, but only 84 distinct genes were identified, revealing high redundancy. However, the aim of this study was to expand the sugar beet public database to enable development of the first custom sugar beet expression and methylation microarrays.
Acknowledgements A PhD grant (CHrd) was provided by  SES-VanderHave (Tienen, Belgium)  and  Association Nationale de la Recherche et de la Technologie  (France, convention CIFRE no. 0379/2008 ). The authors thank  Valérie Vidal  and  Véronique Vidal  from  IMAXIO/HELIXIO (Clermont-Ferrand, France ) for technical assistance regarding the microarrays. We acknowledge  Martine Decoville  from  Orléans University/CNRS (France)  for the use of sonication material. We thank  Clément Lafon-Placette ,  Claude Joseph , and  Franck Brignolas  from  Orléans University (France ) for their discussions and continuous help.
DEGs and DMRs correspond to sequences showing both significantly different signals (t-test analysis with P<0. 05 and P<0. 01, respectively) and fold change >2 between resistant and sensitive genotypes. long-oligonucleotide(60-mer)microarray(1×244k)was generated using the Agilent SurePrint ink-jet technology and hybridized with either total or methylated DNA (isolated by methylated DNA immunoprecipitation; see the Materials and methods for details) extracted from the shoot apical meristems of each genotype after 9 weeks of cold exposure (Supplementary Fig. S2at JXB online). Epigenetic data are available in the GEO database (GEO accession no. GSE56415). Of the 227 242 probes (190 539 from public databases and 36 703 from Cot-SEQ; Table1), 19 574 (8. 6%) remained after the filtration and normalization steps (Supplementary Fig.