dataesr/scientific-paragraphs-categorization huggingface dataset:
008G155900), antioxidant components (Potri. 003G066400, Potri. 006G141400, Potri. 011G140400) or elements of ribosome biogenesis (Potri. 014G135600 and Potri. 015G079600). A gene highly down-regulated (Potri. 010G069000) at 0. 5 h, 2 h, 72 h PB (and to a lesser extent 24 h PB) matched to a homolog of Arabidopsis PHOSPHATE1 (PHO1) encoding a Pi exporter  [28] , pointing, for the first time, to a role of Pi homeostasis in mechanoresponse. Transcriptional regulation of mechanoresponsive TFencoding genes Among the 2,663 mechanoresponsive genes, 271 corresponded to known or putative TF. As shown in Fig. 1 , the proportion of TF varied over the time-course, representing 12. 66% of the DEG at 0. 5 h PB but only 5. 90% at 24 h PB. These mechanoregulated TF were classified into 34 of the 58 TF families described in the PlantTF database  [29].
g. cluster 35). Clusters 5, 13, 23, 34, and 35 regrouped genes that were differentially expressed at more than two time-points across the time-series. Some interesting expression patterns are also shown by cluster 31 regrouping genes that were down-regulated at 0. 5 h PB then up-regulated at 72 h PB, and clusters 29, 30 and 33 regrouping genes showing a regulation throughout the time-course. To find out whether these co-expressed genes could be indicative of putative biological function, each cluster was analysed for enrichment of GO terms in the Biological Process (BP), Fig. 2  List of transcription factor families whose members are differentially expressed in response to bending. The PlantTFDB database  [29]  was used to identify the TF-encoding genes and their family among DEG. The family assignment rules based on the detection of pre-set domains in gene sequences identified 33 TF families (among the 58 different families defined for poplar in PlantTFDB).
When a TF family appeared less than 5 times in DEG, it was classified into the "other" category. The SIG family, not present in PlantTFDB, was revealed by Phytozome data Molecular Function (MF) and Cellular Component (CC) categories (Additional file 3). A clear picture delivered by this GO analysis is the early activation of responses linked to "defence" or "abiotic stress" shown by different GO enriched-terms found for clusters 1, 2, 3, 4, 6, 7, 8, 12 and 14. Three of the four clusters regrouping the earlier, transitory and most up-regulated genes showed an over-representation of terms related to transcription regulation or TF in both the BP and MF categories (clusters 2, 3, and 4). In contrast, cluster 10 is annotated with GO terms related to "negative regulation of transcription". This GO analysis also argues in favour of an early triggering of a "signal transduction" (cluster 5 and 12) involving protein phosphorylation (cluster 8 and 9) and positive regulation of the MAPK cascade (cluster 5).
Time-course expression analysis of candidate genes by qRT-PCR A RT-qPCRs experiment was performed with the dual aim of validating the reliability of our microarray experiment and refining the dynamics of the biological processes triggered in poplar after stem bending. Expression of 46 selected genes was analysed along a new timeseries made with samples collected at regular intervals between 0. 5 and 24 h PB (Fig. 4 ). Each gene was selected to be representative of a cluster on the basis of its expression profile and its putative biological function as determined by GO categorization (Additional file 3). Globally, except for one gene (PtaEB1B), the PCR data backs up the microarray analysis. Moreover, the refined time-series revealed an even greater complexity of the responses. Given the predominance of GO terms related to "defence response" in our data, we studied a variety of genes involved in this biological process, typically members of the WRKY, NAC, GRAS, C2H2 and ERF TF families.
Taken together, these data suggest that in trees, a transient non-wounding mechanical treatment also quickly activates defence-signalling pathways. Previous studies have argued that Ca 2+ may be involved in plant responses to mechanical stimuli  [16]. Accordingly, several genes encoding proteins related to calcium such as calmodulins, Calcineurin B-Like calcium sensor proteins (CBLs) and Autoinhibited Ca 2+ -ATPases (ACAs) were also deregulated in poplar stem tissues after bending. The These genes are differentially expressed 0. 5 h after a single bending and their regulation is not different whether one or two successive bendings are applied. Id_gene corresponds to the gene model found in the Phytozome database  [55]  for poplar or Arabidopsis (Ath). Log2 expression fold-change is shown for the different analyses: expression fold-change 0.
Arrays were scanned with the GeneChip 3000-7G Scanner driven by the GeneChip Operating Software (GCOS). Raw data were imported into R software for analysis and normalized by the GC-RMA algorithm  [52]  available in the Bioconductor package. A two-group t-test assuming equal variance between groups was performed to determine which genes were differentially expressed in the following comparisons: (i) unbent vs. bent stems at each time-point of the timeseries, (ii) stems bent once and harvested 0. 5 h after the mechanical stimulation vs. stems bent twice, (iii) stems bent once and harvested 24 h after the mechanical stimulation vs. stems bent twice. To fit the assumption of equal variance of gene expression between groups, genes displaying extreme variances (too small or too large) were excluded.
The raw P-values were adjusted by the Benjamini-Hochberg procedure  [53] , which controls false discovery rate. A probeset was declared as representing differential expression of a gene if Benjamini-Hochberg P-value was < 0. 05. All the raw and normalized data are available through the Gene Expression Omnibus repository at the NCBI [GEO submission GSE44321;  [54] ]. In order to limit the tendency of this Benjamini-Hochberg correction to lead to false negatives, the data was pre-treated prior to statistical analysis. When a probeset presented a log-transformed intensity under the threshold of 4 for each repeated sample in one comparison, it was considered as unexpressed and these samples were removed from the analysis.
To update the Affymetrix probeset annotations to the third version of the Phytozome P. trichocarpa genome, a BLASTn search was performed between the probeset sequences and the third version of P. trichocarpa sequencing (ncbi-blast-2. 2. 28 -2013. 04. 01 version). 50,691 of the selected probesets were successfully associated to 31,121 different gene IDs among the 41,335 genes of the third version of P. trichocarpa genome. The Arabidopsis thaliana ortholog of each P. trichocarpa gene targeted by a probeset was retrieved in the Phytozome database of the third version of the P. trichocarpa genome (file: Ptrichocarpa_210_annotation_info. txt) as "best Arabidopsis TAIR10 hit symbol"  [55].
GO enrichment analysis To categorize the DEG based on their biological functions, GO enrichment was searched for each expression cluster. In the third version of the poplar genome, genes are associated to a narrow numbers of annotationse. g. PtaZFP2 (Potri. 001G235800) is annotated with 2 GO terms in the Phytozome database whereas its homologous A. thaliana gene has 10 GO annotations on Phytozome. Consequently, the GO enrichment of each cluster was performed with the term-enrichment tool from Amigo (1. 8) using the A. thaliana orthologs and the TAIR database as a background. The maximum-thresholds Pvalue was set at 0. 05 but most of the clusters presented P-values under 0. 01.
Acknowledgements The authors are grateful to  Christelle Boisselet  for her help on plant production and  Sylvaine Labernia  for her help on RNA extraction. We thank  Stéphanie Huguet  and  Sandrine Balzergue  ( URGV, Evry ) for assistance on the generation of Affymetrix microarray data and  Marie-Laure Magniette  ( URGV, EVRY ) for assistance with statistical analysis of the microarray data.
, 2017). Total RNA extraction RNA was extracted using a Nucleospin RNA Plant kit (Macherey Nagel) according to the manufacturer's instruction. Genomic DNA was removed from RNA samples by treatment with 6. 8 U of RNasefree DNase I (Qiagen, Cat. No 79254) followed by a clean-up step with an RNA clean up XS spin column (Macherey Nagel) according to the manufacturer's instruction. RNA was quantified using a NanoDrop ND-1000 (NanoDrop Technologies), and RNA quality was assessed with an Agilent 2100 Bioanalyzer. cDNA sequencing and de novo transcriptome assembly Total RNAs were extracted from P. ramosa seeds imbibed for 24 h, seeds conditioned for 7 d, germinated seeds (72 h after GR24 treatment), young tubercles, 'spider' stage tubercles, young flowering stems, and mature flowering stems. A Roche 454 normalized library was constructed with mRNAs isolated from each sample and mixed in equal proportion, and single read sequences were obtained with Roche GS-FLX+ 454 pyrosequencing systems equipped with oninstrument Data Analysis Software Modules v.
2. 3 (Life Science). cDNA library construction and sequencing were performed by Eurofins Genomics Company (Ebersberg, Germany). For de novo transcriptome assembly, sequences were treated using the Mira sequence assembler Version 3. 4 by Eurofins Genomics Company. Blast annotation of the translated sequences (BlastX) was performed using the BioEdit software v7. 2. 5 against Arabidopsis thaliana (TAIR 10) and Solanum lycopersicum (SlGDB, http:// www. plantgdb. org) libraries. The contigs were then assigned to the PlantTribes 2. 0 orthogroups  (Wall et al. , 2008). Gene Ontology (GO) terms were assigned for each unigene based on GO slim terms using the PANTHER algorithm (http://www. pantherdb. org) (Mi et al. , 2016).
Microarray analysis Contigs obtained from de novo transcriptome assembly were used to design the Agilent-069670 Phelipanche_v1 chip. It was in situ synthesized by Agilent (see the manufacturer's web site at http://www. agilent. com/) and contained 105 086 oligoprobes of 60-mers as follows: 52 543 predicted sense and 52 543 complementary antisense sequences corresponding to the predicted coding genes. All probes were designed within the CDS and with a positive bias toward the 3′ end of the genes, and fixed on Agilent 4 X 180K microarray slides (Gene Expression Omnibus under the accession number GPL23512). Total RNA was extracted from 200 mg of germinated seeds treated or not with crude B. napus root exudates for 12 h and 24 h then deep frozen in liquid nitrogen. mRNA was amplified, labeled as described in  Celton et al.
(2014) , and co-hybridized according to the Agilent two-color microarray-based gene expression analysis protocol. For each comparison, two biological replicates were analyzed in dye-switch design as described in  Depuydt et al. (2009). All statistical analyses were performed using the R language (R Development Core Team, 2009) as described in  Celton et al. (2014). Transcriptome data have been submitted to the Gene Expression Omnibus as Series GSE99377. GO term enrichment of differentially expressed genes (DEGs) was carried out using the AGRIGO algorithm (http://bioinfo. cau. edu. cn/agriGO) (Du et al. , 2010)  against the GO assignment of P. ramosa de novo transcriptome assembly using Fisher's exact test corrected as described in  Benjamini and Hochberg (1995)  [false discovery rate (FDR) <0.
Detection and quantification of CKns by UPLC-ESI (+)-MS/MS in root exudates CK identification in the fractionated exudates was carried out by UPLC-ESI(+)-MS/MS according to  Svačinová et al. (2012)  with some modifications. An Acquity UPLC System (Waters) linked to a triple quadrupole mass spectrometer Xevo™ TQ-S (Waters MS Technologies) equipped with an electrospray interface (ESI) and the unique performance of a collision cell (ScanWave) was used. MS data were processed by MassLynx™ software with the TargetLynx™ program (version 4. 2, Waters). A 2 µl aliquot of samples (SPE extract or fractions) was injected onto a reversed-phase column (Acquity UPLC BEH C18, 1. 7 μm, 2. 1 × 100 mm, Waters). The flow rate was fixed at 0. 4 ml min -1 ; column temperature was 40 °C; sample temperature was 4 °C; and the following binary gradient (A, water-0.
High-throughput sequencing and de novo assembly of the P. ramosa transcriptome To obtain de novo assembly of the P. ramosa transcriptome, a GS-FLX+ 454 pyrosequencing approach (Life Science) was used. Total RNAs were extracted from various developmental stages from seeds to mature flowering shoots. A normalized cDNA library was constructed from mRNAs isolated from each sample and mixed in equal proportion. A total of 1 151 288 reads with an average length of 437 bp and a total length of 503 Mbp were generated. The assembly yielded 58 Mbp distributed in 53 511 unigenes (non-redundant sequences; Supplementary Table  S3 ). The unigenes had an average size of 952 bp and an N50 contig length of 1071 (L50=20 080). The coverage rate of the P. ramosa transcriptome was assessed by examining the proportion of conserved orthologous genes in the assembly data set.
Two sets of conserved genes were used, namely the UCOs containing 357 singlecopy genes highly conserved in A. thaliana, humans, mice, yeast, fruit flies, and Caenorhabditis elegans, and APVO sequences containing 959 single-copy genes from A. thaliana, Populus trichocarpa, Vitis vinifera, and Oryza sativa plant genomes  (Wall et al. , 2008; Duarte et al. , 2010) : 98% of UCOs (with a threshold blast e-value of 1 × 10 10 ) and 82% of APVO sequences were detected in P. ramosa transcriptome assembly, indicating the high coverage rates of P. ramosa transcripts (Supplementary Table  S3 ). Local alignment using the BLASTx algorithm (cut-off e-value 1 × 10 10 ) showed that 51% and 55% of P. ramosa contigs/unigenes have homologous sequences in the A. thaliana genome (TAIR 10) and in the S. lycopersicum (GeneBank 160) genome, respectively (Supplementary Table  S3 ).
The present study highlights the role of EHS development in P. ramosa aggressiveness. Indeed, a strong increase in the number of broomrape attachments (tubercles) was observed when germinated seeds were pre-treated with B. napus root exudates to induce EHSs before placing them in contact with B. napus roots (Fig. 2 ). Similarly, a recent study in P. japonicum showed that plants displaying the haustorial hair defective (hhd) mutation fail to form haustorial hairs upon DMBQ treatment and form fewer functional haustoria when put in contact with host roots  (Cui et al. , 2016). The transcriptome assembly obtained in this work yielded 53 511 contigs, 51% and 55% of which have a BLASTx hit with the Arabidopsis TAIR 10 database and the S. lycopersicum (GeneBank 160) genome, respectively. Although this value is weaker than the one found in a recent study in P.
Acknowledgements VG and EB were financially supported by PhD grants from the  French Ministry of Education and Research  and from the  Region Pays de Loire  ( RFI Objectif Végétal project ), respectively. We thank the  ANAN platform of the SFR Quasav , the  IBiSA platform 'Biopolymers-Structural Biology' (INRA Research Unit 1268 BIA) , and the  LABERCA laboratory of the CORSAIRE facility  for access to microarray facilities, microscopy analyses, and UPLC-MS analyses, respectively. LS was supported by the  Ministry of Education, Youth and Sports of the Czech Republic  (grant  LO1204  from the  National Program of Sustainability ).
Data set S2. GO term enrichment analysis on the DEG set. Data deposition. Transcriptome sequence data have been deposited in the Parasitic Plant Genome Project  (Westwood et al. , 2012; Yang et al. , 2015 ; http://ppgp. huck. psu. edu/). Microarray data have been deposited in the Gene Expression Omnibus under the accession numbers GPL23512 and GSE99377.
Genetic and phenotypic characteristics of segregants For the QTL analysis, we generated populations of segregants by crossing the two wine strains of different cold tolerance for up to 13 generations (F13). This strategy allowed us to increase the resolution by reducing linkage between nearby QTLs. To study the genetic diversity of segregant populations, five differential SNPs of chromosome III in 30 segregants of F6 were genotyped. A recombination frequency of 0. 39 was assessed, and there was an average of 10 haplotypes per SNP (data not shown). The F13 segregants were screened for their low temperature adaptation by calculating the maximum growth rate of each segregant and the two parental strains (Fig. 2a ). Transgression levels, that is, the percentage of segregants that exceed the phenotypic range of their parents by at least 2 standard deviations  [24] , can provide insight into the genetics that underlies complex traits.
# Indicates stuck fermentation before T100. c Spot test between the control hybrid and the hemizygous deletion of the selected genes we used the sequence available in the Saccharomyces Genome Database (SGD: http://www. yeastgenome. org) to perform a large-scale RH analysis with the genes known to be present in subtelomeric regions (~20-30 Kb), and those that are not essential (Additional file 6: Table  S2 )  [35]. The haploid single gene deletion strains in the background of the BY4741 lab strain were crossed with the stable haploids of each parental wine strain by constructing hemizygous hybrid lab/wine strains (BY4741/P5 and BY4741/P24). The fermentation capacity of the resulting hemizygotes, which contained only the wine strain allele of each individual candidate gene, were tested on SM at 15 and 28 °C and compared to the corresponding capacity of the lab/wine hybrid with both parental alleles intact (Fig.
Sequencing The genome sequencing of the selected populations was performed by 5500xl SOLiD sequencing. Genomic libraries were prepared following the manufacturer's standard instructions. Emulsion PCRs were performed using the SOLiD™ EZ Bead™ Systems. Sequencing was carried out by 75 nt single-end read exact call chemistry (ECC) following the manufacturer's standard protocols. The whole-genome sequences are deposited in the Sequence Read Archive (SRA) database (http://www. ncbi. nlm. nih. gov/sra/) and are available with access number SRP048919. Breseq (v0. 27. 1)  [51]  pipeline was used to first align the reads of the reference S288c genome (using Bowtie2  [52] , and to identify SNPs and indels with a frequency cutoff of 0. 2. CNV detection was performed using CNV-seq  [53]  with a window size of 121 nt (with a minimum log2 fold of 0.
Statistical analysis All the experiments were carried out at least in triplicate. Physiological data were analyzed with the Sigma Plot 12. 5 software, and the results were expressed as mean and standard deviation. To evaluate statistical significance, two tailed t-student tests were applied with a p-value of 0. 05. P-values were corrected for multiple testing by the Bonferroni test. Phenotypic data were fitted to the reparameterized Gompertz model by nonlinear least-squares fitting using the Gauss-Newton algorithm as implemented in the nls function in the R statistical software, v. 3. 0. Phenotype heritability H 2 was calculated as previously described  [60] , i. e. , H 2 = ((Var seg -Var env )/Var seg ) × 100, where Var env is the pooled variance among parental measurements and Var seg is the variance among phenotype values for segregants.
77 Â 10 À 5 to 4. 45 Â 10 À 3 ), consistent with the possibility that these SNPs may mark one or more risk variants independent of rs2447853. We also noted in the 1000G Phase 3, version 1 reference dataset an insertion/deletion variant that was highly correlated with these nine SNPs (rs3030832, r 2 ¼ 0. 96 to rs451360 in EUR) that had not been included in the imputation reference based off an earlier version (1000G Phase 1, version 3). We therefore re-imputed the pancreatic cancer GWAS with the newer 1000G reference set and observed an association signal similar in strength and significance to that of the other nine variants (rs3030832, P ¼ 8. 25 Â 10 À 10 , OR¼ 1. 28 95% CI 1. 18-1. 39; Supplementary Table  2 ) indicating that this indel variant should likewise be considered a candidate functional risk variant.
(a) Chromatin interaction analysis paired-end (ChIA-PET) sequencing data from the K562 chronic myeloid leukaemia cell line using an antibody against RNA polymerase II generated by the ENCODE project (https://www. encodeproject. org/) is shown. For each of the ten strongly associated variants, layered H3K4Me1, H3K4Me3, and H3K27Ac chromatin immunoprecipiation (ChIP-seq), DNAse I hypersensitivity sequencing (DNase) and transcription factor ChIP-seq (TF-ChIP-Seq) data from the ENCODE project are shown (b) as displayed by the UCSC Genome Browser (lower panels). Silencing the region harbouring rs36115365. To interrogate whether the putative gene regulatory region harbouring rs36115365 influences expression of TERT and/or CLPTM1L, siRNA mediated transcriptional gene silencing (TGS) (refs 30,31)  was used to target across this region to evaluate effects on gene expression.
Methods Studies. Subjects were drawn from GWAS studies of four cancers: pancreatic cancer: PanScan I and II (3,525    29. All participants provided informed written consent and all studies were reviewed and approved by institutional ethics review committees at the involved institutions. Participation of subjects in the PanScan GWAS was also reviewed by the NCI Special Studies Institutional Review Board. Each participating study obtained approval from its institutional review board (IRB) permitting data sharing in accordance with the NIH policy for Sharing of Data obtained in NIH-Supported or NIH-Conducted Genome Wide Association Studies. Analysis of melanoma GWAS was reviewed by The Northern and Yorkshire Research Ethics Committee; each participating study obtained informed consent from study participants, approval from its local IRB as previously described  29.
Within the pancreatic cancer GWAS data, all common 1000G variants (n ¼ 195, MAFZ0. 01) in Region 2 (defined as the genomic region between the two recombination hotspots at 1,306,281-1,367,281 in NCBI build Hg19) had imputation accuracy (INFO) scores above 0. 3 (the lowest quality score was 0. 48). The imputation quality for the set of nine Region 2 variants most significantly associated with pancreatic cancer risk was high in the PanScan GWAS studies, with quality scores (INFO) ranging from 0. 82 to 0. 96 (average 0. 92). Similar imputation quality scores were observed for these SNPs in the lung cancer, TGCT, and melanoma GWAS (INFO range 0. 82 to 0. 98; average 0. 94). In addition, imputation quality was high for all SNPs that were statistically correlated with rs36115365 in 1000 Genomes CEU data (r 2 40.
2). In PanScan, only a single such 1,000 Genomes variant had an imputation quality score (INFO) below 0. 8 (rs186156459; INFO ¼ 0. 79), suggesting that poor imputation quality did not lead to the exclusion of additional strong functional candidates from consideration. Similar imputation quality was likewise observed for the other cancer GWAS. For completeness we assessed the newer 1000G (Phase 3, October 2014) reference dataset and noted an insertion/deletion variant (rs3030832) that was highly correlated to rs36115365 (r 2 ¼ 0. 87 in EUR). We therefore re-imputed the pancreatic cancer GWAS dataset  11, 57  with the newer 1000G reference set to reassess the association signal across Region 2 (defined as the genomic region between the two recombination hotspots at 1,306,281-1,367,281 in NCBI build Hg19) including this variant.