dataesr/scientific-paragraphs-categorization huggingface dataset:
Transcription of endogenized parvovirus sequences in A. pisum was also suggested previously, as expressed sequence tags exhibiting similarities to parvovirus-related DNA were reported in the NCBI public databases  (Liu et al. , 2011b). However, no experimental confirmation of mRNA accumulation of these parvovirus-related DNAs was reported. Although we clearly detected polyadenylated RNA originating from DLSs in M. persicae, the question of their translation remains. Transcription, and potentially translation, of these sequences could explain the low pathogenicity of DNV in aphids. To date, there are only two DNVs described in aphids: MpDNV, which is highly specific to M. persicae  (van Munster et al. , 2003a) , and DplDNV, identified in D. plantaginea  (Ryabov et al. , 2009). Interestingly, these aphid DNVs do not harm their host, in contrast to other DNVs which are highly pathogenic to their insect hosts  (Barreau et al.
METHODS Insect rearing and virus clearing. An M. persicae (Sulzer) clone from Colmar (France) was reared on pepper (Capsicum anuum) at 20 uC with a 16 h photoperiod. This clone was infected with MpDNV. The same clone was cured of MpDNV by transferring individual larvae directly after birth onto beets (Beta vulgaris) to avoid contamination by contact. The absence of MpDNV was monitored by PCR  (van Munster et al. , 2003a) , and one aphid colony cured of MpDNV was maintained on B. vulgaris. DLS detection in the M. persicae and A. pisum genomes. To screen for DLSs in the M. persicae genome, BLASTN and tBLASTn searches were performed online against the AphidBase database (http://www. aphidbase. com/aphidbase/), using M. persicae clone G006 (originating from the USA;  Ramsey et al. , 2007)  as a reference genome (scaffolds).
In a similar manner, BLAST searches were performed on the updated assembly of the A. pisum genome (assembly v. 2) also available in AphidBase. A. pisum sequences showing similarity to subfamily Densovirinae representatives were also identified and extracted for phylogenetic analysis. Integration of DLSs in M. persicae clones was confirmed by amplifying virus-like sequences using specific primer pairs designed with the Primer3 program  (Rozen & Skaletsky, 2000)  and with binding sites in both flanking host and virus insertion sequence (Table  S4 , Fig. 2 ). Total genomic DNA was extracted from 30 aphids, which were ground in liquid nitrogen using the QIAamp DNA Mini kit protocol (Qiagen). One microlitre of DNA (20-30 ng ml 21 ) was used for PCRs containing the specific primers and performed in a thermal cycler (Eppendorf) using standard protocols.
PCR products were analysed by 0. 7 or 2% agarose gel electrophoresis depending on amplicon size and visualized under UV after ethidium bromide staining. Alignment and phylogenetic reconstruction. Phylogenetic trees were reconstructed using the predicted amino acid sequences of DLSs. Protein sequences of 100 aa corresponding to a region of the DLS sharing similarity with NS1 of parvoviruses were aligned with the corresponding sequences of known DNVs including subtypes of the subfamily Densovirinae using the MUSCLE program configured for high accuracy  (Edgar, 2004). The DNV sequences used in the alignment were: AaDNV (GenBank accession no. NC_004285. 1), IHHNV (NC_002190. 2), BmDNV5 (NC_004287. 1), FchDV-1 (NC_014357. 1), JcDNV, (NC_004284. 1), MpDNV (NC_005040. 1), DplDNV (EU851411. 1), Periplaneta fuliginosa densovirus (NC_000936.
1), GmDNV (NC_004286. 1) and HaDNV (JQ894784. 1). Phylogenetic relationships among DLS integrations and DNVs were assessed using the Phylogeny. fr platform  (Dereeper et al. , 2008). The phylogenetic tree was reconstructed using the maximum-likelihood method implemented in the PhyML program (v. 3. 1/3. 0 aLRT). The JTT 1 G model  (Jones et al. , 1992) , a commonly used probabilistic amino acid replacement matrix at the 85% identity level allowing heterogeneity across sites, was selected. Reliability for the internal branch was assessed using the aLRT test (SH-Like). Node support was estimated using bootstrap resampling procedures (1000 trials). Trees were edited using TREEDYN (v. 198. 3) (Chevenet et al. , 2006). Detection of TEs in the M. persicae genome. A 30 000 bp region encompassing each DLS was scanned for adjacent TEs or repetitive sequences using CENSOR (Repbase; http://www.
girinst. org/censor/ index. php) with a reference collection of sequence repeats  (Kohany et al. , 2006). BLASTX and tBLASTx queries on these flanking DLS regions against the non-redundant (nr) protein database at NCBI were also performed. As a control, repetitive sequences in DLS-free regions of scaffolds harbouring DLSs and on scaffolds where no DLSs could be detected were also analysed. Transcription of DLSs in M. persicae. Transcription of DLSs was analysed by real-time RT-PCR in fourth-instar individuals of M. persicae (clone Colmar). Total RNA was extracted from aphids cured of MpDNV (four biological replicates) ground in liquid nitrogen following the Qiagen RNeasy Plant Mini kit protocol (animal tissue protocol). On-column DNase treatment was carried out using an RNase-Free DNase set (Promega).
Synthesis of cDNA (from 2 mg RNA) was primed using oligo(dT) (Promega) and cDNA synthesis was done using the Moloney murine leukemia virus reverse transcriptase (Promega). The cDNA was then used as a template for real-time PCR amplification (primers listed in Table  S4 ) using an iTaq universal SYBR Green supermix (Bio-Rad Laboratories). Ribosomal proteins RLP7 and L27 were used as internal controls. The amplification reaction consisted of 10| SYBR Green supermix and 10 pmol forward and reverse primers (Table  S4 ) in a final volume of 20 ml. Three technical repeats were performed using a CFX96 Touch Real-Time PCR Detection System (Bio-Rad Laboratories). The mRNA synthesis was normalized with RLP7 and L27 using the 2 2DDCt method  (Livak & Schmittgen, 2001)  and analysed with Bio-RadCFX Manager software (Bio-Rad Laboratories).
ACKNOWLEDGEMENTS DNA sequence data were downloaded from AphidBase (http://www. aphidbase. com/aphidbase/). Funding for M. persicae clone G006 genomic sequencing was provided by  USDA-NIFA  award  2010-65105-20558  held by  Alex Wilson  ( Miami University, OH, USA ). Funding for M. persicae clone_O genomic sequencing was provided by  The Genome Analyses Centre (TGAC) Capacity and Capability Challenge program  project  CCC-15  held by  Saskia Hogenhout  and from the  Biotechnology and Biological Sciences Research Council (BBSRC)  ( BB/J004553/1 ) and the  John Innes Foundation. We are grateful to  Fabrice Legeai  and  Antony Bretaudeau  ( INRA, Rennes ) for their assistance in aphid genomic sequences analysis. We also thank  Denis Filloux  ( INRA, Montpellier, France ) for his help in database analysis,  Neta Luria  and  Aviv Dombrovsky  ( Volcani Center, Israel ) and  Pierre Gladieux  ( INRA, Montpellier, France ) for their fruitful discussions.
Selection of Clinical Samples 100 human sera positive for CHIKV qRT-PCR were randomly selected amongst  (1)  those sampled between week 52 of 2013 and week 5 of 2014 around the beginning of epidemic phase in the French Caribbean islands, and (2) those having a high viral load (mostly between 10 6 and 10 7 copies/ml, even if some lower viral loads were added to examine whether sampling bias with respect to viral load had occurred (Table  1 ). One third of these samples were from Guadeloupe and two thirds from Martinique. The consensus sequences of the 20 wholegenome samples were deposited in the European Nucleotide Archive, with the accession numbers indicated in Table  1 , and accessible at http://www. ebi. ac. uk/ena/data/view/ LN898093-LN898112.
For example, patient 1, amplicon 1 presented 2557 SNPs in the quality filtered reads along the 871 nucleotide sites sequenced, with the lowest frequencies at 0. 00001. ViVan statistical analysis further reduced these SNPs to 1188 with the lowest frequency at 0. 0001 for an average read coverage of 80,000X. We set an additional, conservative cut-off of a minimum of 3,000X coverage and 0. 001 frequency, bringing the total SNPs in this sample to 564. Average coverages were above 70,000X for all samples. All samples had similar profiles to the example given above, with no apparent outliers, with 95- 100% of all possible SNPs represented in quality filtered reads, 36-48% of SNPs in ViVan filtered data and 16-23% of SNPs above the conservative cut-off. Variants with a frequency above 0. 5 (50% of the total population) were considered consensus changes and were added to the CNR20235 reference sequence.
The consensus sequences obtained from the 20 wholegenome samples were deposited in the European Nucleotide Archive with accession numbers listed in Table  1. To align "total" and "unique" reads an in-house pipeline was used. The reads were trimmed to remove low-quality bases using fastq-mcf  [8]  and aligned with bwa-mem  [9]  to an artificial reference genome consisting of the two references genomes. For tissue culture passaged virus deep-sequencing, human sera were placed directly on Vero or C6/36 cells and supernatants were collected three days post-infection for C6/36 cells or at cell death for Vero cells. Viral RNA was extracted and analyzed as described above.
Phylogenetic Alignment and Analysis Full-length CHIKV sequences were aligned using the CLUSTAL W program  [10]. Once aligned, the program Model Generator  [11]  was used to identify the optimal evolutionary model that best described our sequence dataset. Akaike information criteria and hierarchical likelihood ratio test indicated that the GTR + Γ + I model best fit the sequence data. Maximum-likelihood phylogenetic trees were constructed under the GTR + Γ + I model using software from the PhyML program  [11]. As a measure of the robustness of each node, we used an approximate Likelihood Ratio Test (aLRT), which demonstrates that the branch studied provides a significant likelihood against the null hypothesis that involves collapsing that branch of the phylogenetic tree but leaving the rest of the tree topology identical.
Results To analyze the viral diversity present within human hosts infected during the current CHIKV outbreak in the Americas, we deep-sequenced 25 viral strains from sera of infected patients from Martinique and Guadeloupe (Table  1 ). The samples, consisting of approximately 70% from Martinique, were taken between weeks 52-2013 and week 5-2014 and represented the same proportion of patients who were diagnosed as CHIKV positive during this time. By using our deep sequencing data we assembled the consensus sequences obtained from each patient to determine the degree of genetic variability of these strains by phylogenetic approaches. These sequences were aligned with 63 full-length CHIKV strains isolated elsewhere, representing all major CHIKV lineages. Subsequently, maximum likelihood phylogenetic trees were constructed.
Our data also indicate that a considerable loss of diversity present within human samples occurs after even a single passage in cell culture, especially in C6/36 mosquito cells. Even a single amplification in cell culture passage, although often necessary for diagnostics and surveillance, may thus introduce artifacts or purify previously existing minority variants that were in the process of emerging in human samples. Phylogenetic analysis revealed that the strains collected in Guadeloupe and Martinique islands belong to the Asian genotype circulating in the Caribbean. Furthermore, the strains sequenced in this study show a closer phylogenetic relationship, which can be attributed to the short genetic distances depicted in their tree branches. We observed strains from Brazil (TR206 and AMA2798) and Mexico (InDRE_4CHIK and InDRE_51CHIK) to cluster with our cohort as well.
This work was supported by a  Institut Pasteur Programme Transversal de Recherche PTR 489  grant, the  French Government 's  Investissement d'Avenir program ,  Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases " (grant  ANR-10-LABX-62-IBEID ) and a  "Equipe FRM DEQ20150331759" grant  from the  French Fondation pour la Recherche Médicale  (KAS, MV). RC and SCW were supported by  National Institutes of Health  Contract  HHSN272201000040I/HHSN27200004/D04 , and by the  Institute for Human Infections and Immunity at the University of Texas Medical Branch. DWV was supported by the  Edmond J. Safra Center for Bioinformatics  and the  Center for Nanoscience and Nanotechnology at Tel-Aviv University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Acknowledgments: The research which led to these results received funding from the  European Union 's  Seventh Programme  ( GLAMUR  project). We also acknowledge all the participants of the  GLAMUR  project for the fruitful scientific exchanges that have directly contributed to this article, and the stakeholders of different wine chains (specially the wine producers) who have been involved in the different steps of this participatory work. We thank the two anonymous reviewers for their comments on a first version of this article, and  Laura Amalir  for her careful English editing of our paper.
The draft genome sequence of Frankia sp. strain Allo2 was generated at the Hubbard Genome Center (University of New Hampshire, Durham, NH, USA) using Illumina technology  (28)  techniques. A standard Illumina shotgun library was constructed and sequenced using the Illumina HiSeq2000 platform, which generated 26,647,886 reads (260-bp insert size), totaling 3,866. 4 Mbp. The Illumina sequence data were assembled using CLC Genomics Workbench version 8. 0. 1 and AllPaths-LG version r41043  (29). The final draft assembly for Frankia sp. Allo2 consisted of 133 contigs in 110 scaffolds with an N 50 contig size of 96. 9 kb. The final assembled genome contained a total sequence of 5,352,211 bp with a GϩC content of 70. 0% and is based on 3,120. 3 Mb of Illumina draft data, providing an average 583ϫ coverage of the genome.
The assembled Frankia sp. strain Allo2 genome was annotated via the Integrated Microbial Genomes (IMG) platform developed by the Joint Genome Institute, Walnut Creek, CA, USA  (30, 31)  and resulted in 4,224 candidate protein-encoding genes and 45 tRNA and 3 rRNA regions. Nucleotide sequence accession numbers. This whole-genome shotgun project has been deposited at DDBJ/EMBL/GenBank under the accession number JPHT00000000. The version described in this paper is the first version, JPHT01000000.
Partial funding was provided by the New Hampshire Agricultural Experiment Station. This is Scientific Contribution Number 2665. This work was supported by the USDA National Institute of Food and Agriculture Hatch 022821 (L. S. T. ), Agriculture and Food Research Initiative Grant 2015-67014-22849 from the USDA National Institute of Food and Agriculture (L. S. T. ), and the College of Life Science and Agriculture at the University of New Hampshire, Durham. M. N. was supported by an ARTS (Allocation de Recherche pour une Thèse au Sud) PhD grant from the Institut de recherche pour le développement (IRD). Sequencing was performed on an Illumina HiSeq2500 purchased with NSF MRI Grant DBI-1229361to W. K. T.
[15]. This subdivision matched with the present knowledge about grapevine usage (  and geographical origin (East vs West)  [12, [15] [16] [17] , while resulting in subgroups large enough for further sampling within each subgroup. Second, from the 2276 cultivars left, we selected 1190 non-or low-admixed cultivars, belonging to one of the three subgroups (wine East, WE; wine West, WW; table East, TE) with a membership higher than 80 % according to STRUCTURE results. Third, within each of the three subgroups of this set, we identified the founding individuals as the ancestral or most widely used genitors. This identification was based both on historical and ampelographic knowledge, and on SSR-based relatedness analysis  [18] , following Lacombe  [55]. We then complemented each subgroup up to 93 cultivars, using the Max Length Subtree procedure implemented in DARWin software  [56] , which allowed well-balanced maximization of the genetic distance between cultivars.
Wild panel A wild panel was also selected among the accessions of V. vinifera subsp. sylvestris available in the Vassal collection. After genotyping at 20 SSRs following Laucou et al  [51]  and careful exclusion of any possibly remaining inter-specific hybrids, 94 accessions (from eight different countries, mainly France), collected in a total of 48 locations, were selected to maximize both the number of geographical origins and the SSR genetic diversity using the Max Length Subtree procedure of DARWin software as described above for cultivars (Additional file 2). Due to loss of weak plants in the greenhouse, only 62 individuals from 34 locations finally composed the wild panel.
SNP discovery SNP discovery was performed in four genomic regions of ca. 2 Mb each (Table  1 ), harboring QTLs for agronomic traits: tannin content and composition on chromosome 8  [24] , downy mildew resistance on chromosomes 9 and 12  [59]  and berry weight on chromosome 17  [25]. Primer pairs were automatically designed in exons  [60]  to amplify one specific amplicon of 400-1400 bp per gene, using an automated pipeline combining SPADS v1. 0  [61]  and PRIMER3 v2. 3. 6  [62]  softwares (detailed procedure available upon request). Within each genomic region, 55-60 amplicons were selected to optimize sequencing (longest possible exon in one direction, absence of microsatellite and poly-T patterns). Small distances between neighbor genes were favored (Additional file 3) to ensure that such distances were sufficiently represented.
In addition, to estimate kinship between individuals, 169 amplicons regularly distributed over the whole genome were selected using a similar procedure. For the discovery panel with 30 accessions, a total of 399 amplicons were sequenced in one direction, using the high-throughput Sanger method described by Philippe et al. [63]. Raw sequence files (. ab1) were passed through a pipeline using PHRED and PHRAP  [64]. These sequences were then aligned together (not to a reference genome) and SNPs/indels were called, using PREGAP and GAP Shotgun Assembly (with Maximum number of pads = 100 and Maximum percentage of mismatches = 20) within the Staden v4 package  [65] , followed by manual curation (artifacts, lags). Final validated fasta files (. fas) are publicly available in the SNiPlay database  [66, 67]  (choose "Grapevine" as species, and "Nicolas_et_al_2016" as project).
Phenotypic analyses The phenotypic representativeness of the association panel was assessed for five quantitative traits measured in the Vassal collection (mean values over 2 to 5 years): véraison and maturity dates (relative to the reference cv. Chasselas), vigor, berry and cluster weight at physiological maturity. Comparison between the association panel and the whole collection was performed using R packages 'sm' v2. 2-5. 4  [70]  for density plots, 'stats' v3. 0. 1  [71]  for non-parametric mean equality tests (Wilcoxon rank-sum test), and 'car' v2. 0-20  [72]  for Levene's variance equality tests. A principal component analysis (PCA) was performed with 'adegenet' v1. 4-1 R package  [73]. We also tested the effect of the association panel subgroup on each quantitative trait by analysis of variance (ANOVA) and Kruskal-Wallis rank sum test using the 'stats' R package, with the following model: Y ij = μ + S i + e ij , where Y ij is the phenotypic value of cultivar j belonging to subgroup i, μ the general mean, S i the subgroup effect and e ij the random effect.
Genetic diversity analyses To assess the genetic representativeness of the association panel, several statistics were computed from the most recent data representing Vassal diversity (genotypes at 20 SSRs for the 2195 cultivars listed in Additional file 4) using GenAlEx v6. 501  [74, 75]. For each SSR locus, the number of different alleles (Na), effective number of alleles Ne = 1/(1-Σp i 2 ) (where p i is the frequency of allele i), observed heterozygosity Ho and expected heterozygosity He = 1 -Σp i 2 were calculated. They were then averaged over the 20 SSRs (data for the association and wild panels are given in Additional file 5). To further assess differences in diversity between subgroups, Ho, He and MAF were calculated for each SNP locus. All genetic diversity analyses were also performed on the wild panel to allow comparison with the association (cultivated) panel.
Assessment of population structure and kinship To check the representativeness of the association panel for genetic structure based on SSR data, a PCA was performed, as implemented in 'adegenet' R package. GenA-lEx was used to measure pairwise genetic differentiation among subgroups with SSRs or SNPs, using Fst. Relatedness and the proportion of first degree relationships (parent-offspring + full-sib) were estimated with ML-Relate. Since genetic structure and kinship may be confounding factors in linkage disequilibrium and genome-wide association studies, corresponding matrices were calculated for the association and wild panels together, i. e. for a total of 334 individuals, based on a combined genotypic file including data for 20 SSRs  [51]  and 129 SNPs distributed on the genome (this study).
For LD correction by kinship, we used five different co-ancestry estimators, implemented in the CoCoa v1. 1 software  [76] : i) AIS (Alikeness In State  [77] ), the probability that the two alleles drawn at a random locus of each of two individuals are identical by state (IBS); ii) WAIS (Weighted Alikeness In State  [77] ), obtained from AIS by introducing two correction factors to account for the mean probability that two individuals have an IBS allele that is not identical by descent (IBD); iii) BNO  [78] , which uses a single correction factor for the same goal; iv) LOI  [79] , a modified correlation coefficient between mean allelic frequencies; v) MLE (Maximum Likelihood Estimator,  [80] ). For BNO and WAIS, either two or four unrelated groups were assumed, by distinguishing either between the wild and the association panels or between all subgroups (WE, WW, TE, Wild), respectively.
LD analysis Linkage disequilibrium was estimated in the four genomic regions between all SNPs with a MAF > 5 %. We used the classical r 2 estimate of correlation between genotypes and two recently developed estimates: one corrected by kinship (r 2 V ) applied to each cultivated subgroup and to the wild panel, and one corrected by both kinship and structure (r 2 VS ) applied to the whole association panel  [81]. These corrected estimates were calculated using the 'LDcorSV' v1. 3. 1 R package  [81]. The expected LD value within each region was modeled as a non-linear function of physical distance according to Hill and Weir  [82]  model. LD extent was defined as the physical distance corresponding to an expected LD value of 0. 2. The effects of MAF, Nei's diversity index and annotation features (coding vs non-coding, synonymous vs non-synonymous) on LD extent were tested with ANO-VAs using separate models (detailed in Additional file 7), which included the effects of subgroup and genomic region.
LD landscape within each genomic region was explored: i) through heatmap visualization ('LDheatmap' v0. 99-1 R package  [83] ), ii) by plotting mean r 2 V against physical position in a 300 Kb-sliding window, with a 10 Kb step, iii) by inspecting the IBS clustering of haplotypes estimated with the localized haplotype cluster model implemented in Beagle v4. 0 software  [84]  using ten iterations.