dataesr/scientific-paragraphs-categorization huggingface dataset:
Second, we performed RT-qPCR analyses using a subset of genes to validate the assumption of the global shift in expression. In a first step, to identify non-DE and DE genes from the RNA-Seq dataset, based on our assumption of a global shift, we used the classical normalization method from DESeq2 R package (see Experimental Procedures). According to our assumption, non-DE genes Comment citer ce document : Pirrello, J. , Deluche, C. , Frangne, N. , Gevaudant, F. , Maza, E. , Djari, A. , Bourge, M. , Renaudin, J. -P. , Brown, Bowler, C. , Zouine, M. , Chevalier, C. , Gonzalez, N. (  2018 ). Transcriptome profiling of sorted endoreduplicated nuclei from tomato fruits: how global shift in expression ascribed to DNA ploidy influences RNA-Seq data normalization and interpretation. Plant Journal, 93.
On the other hand in PS, NIR reflectances are considered in the same way as genomic or endophenotypic regressors, at the genotypic level rather than the individual level, which allows making predictions in any environment without having any environment specific NIRS. In PS we suppose that once NIR reflectances are analyzed in one experiment (collections of seed, a nursery, a trial or a controlled experiment) they could be used as regressors or to estimate a kinship matrix to make predictions in any other experiment, as long as relevant phenotypic data are available to calibrate the statistical model, like in GS with molecular markers. There are several advantages to this approach. One can obtain NIRS for any plant or animal species at a lower cost than genotyping and potentially without particular treatment of the samples prior to the analysis such as DNA or RNA extraction.
At each site, a randomized complete block design was used with a single tree per block and six replicates per genotype. Growth data collected in each design clearly indicated that the Italian site was more favorable than the French site  (Guet et al. 2015; Gebreselassie et al. 2017). NIRS data: Wheat NIRS data were obtained on flag leaves and harvested grains from the two treatments of the drought trial in Clermont-Ferrand (France) in 2015/2016. For each variety in each treatment, twenty flag leaves were sampled on one plot at 200 degree days after flowering. The samples were oven dried at 60°for 48 h. Leaves were milled (Falaise miller, SARL Falaise, France), and the powder was analyzed with a FOSS NIRS 6500 (FOSS NIRSystems, Silver Spring, MD) and its corresponding softwares (ISIscan TM and WINisi TM 4.
20). For each variety in each treatment, 200 g of grains harvested at one plot were analyzed with a FOSS NIRS XDS (FOSS NIRSystems, Silver Spring, MD) and its corresponding softwares (ISIscan TM and WINisi TM 4. 20). For leaf powder and grain, absorbance was measured from 400 to 2500 nm with a step of 2 nm. 5 varieties were removed from the dataset because their leaf absorbance was abnormal because of a technical problem, resulting in a final panel of 223 varieties. The resulting spectra were loaded into R software (R Core Team 2018) to be pretreated using custom R code. They were normalized (centered and scaled) and their first derivative was computed using a Savitzky-Golay filter  (Savitzky and Golay 1964)  with a window size of 37 data points (74 nm) implemented in the R package signal (Signal Developers 2014).
3. 5). For each sample, the measurement consisted of an average of 64 scans done while rotating the cups over the 10,000 cm 21 -4,000 cm 21 range with a resolution of 8 cm-1 and a zero-filling factor of 4, resulting in absorbance data every 2 cm 21 : The resulting spectra were loaded into R software (R Core Team 2018) to be processed using custom R code. They were first restricted to the 8000 cm 21 -4000 cm 21 range because the most distant part of the spectra (8000 cm 21 -10,000 cm 21 ) appeared to be quite noisy. Then, the restricted spectra were normalized (centered and scaled), and their first derivative was computed using a Savitzky-Golay filter  (Savitzky and Golay 1964)  with a window size of 37 data points (74 cm 21 ) implemented in the R package signal (Signal Developers 2014). Finally, these normalized and derived spectra were averaged by genotype at each site.
SNP data: Wheat The 228 wheat varieties were genotyped with the TaBW280K high-throughput genotyping array described in  Rimbert et al. (2018). This array was designed to cover both genic and intergenic regions of the three subgenomes. Markers with a minor allele frequency below 1%, or with a heterozygosity or missing rate above 5% were removed. Groups of identical markers were identified, and for each of them only one marker was kept. Eventually, we obtained 84,259 SNPs, either polymorphic high resolution or off-target variants, with an average missing data rate of 0. 83%. Missing values were imputed as the marker frequency.
Poplar The poplar association population was genotyped with an Illumina Infinium BeadChip array (Faivre-Rampant et al. 2016) yielding 7,918 SNPs for 858 genotypes. Missing values were rare (0. 35%) and they were imputed with FImpute  (Sargolzaei et al. 2014). The data were restricted to the subset of 562 genotypes with SNP data and NIRS data at both sites. Within this set, SNPs with a minor allele frequency below 1% were discarded, yielding a final SNP dataset of 7,808 SNPs. Phenotypic data: Wheat The 228 wheat varieties were phenotyped for heading date (HD) and grain yield (GY) at the two environments in which the NIRS analysis was conducted (drought experiment in Clermont-Ferrand 2015/2016). The subpanel of 161 varieties was phenotyped for the same traits in six independent environments.
Poplar The poplar association population was evaluated at each of the two sites for the following traits on up to six replicates by genotype: height at 2 years at Orléans (HT-ORL), circumference at 1 m above ground at 2 years at both sites (CIRC-ORL and CIRC-SAV), bud flush at both sites (BF-ORL and BF-SAV) and bud set at both sites (BS-ORL and BS-SAV) as discrete scores for a given day of the year (see  Dillen et al. (2009)  and  Rohde et al. (2011)  for details on the scales used) and resistance to rust at Orléans (RUST-ORL) as a discrete score of susceptibility on the most affected leaf of the tree and on a 1 to 8 scale. Within each site, the phenotypic data were adjusted for micro-environmental effects using random effect block and/or spatial position when needed following a visual inspection of spatial effects with a variogram as implemented in the R package breedR  (Muñoz and Sanchez 2017).
2011). Genomic heritability was estimated for each wavelength within each environment (m) as follows: h 2 m ¼ ŝ2 u m ŝ2 u m þ ŝ2 e m ; (3) with ŝ2 um and ŝ2 em the REML estimates of s 2 um and s 2 em ; obtained with the Newton-Raphson algorithm implemented in the R package sommer (Covarrubias-Pazaran 2016). Following  Yamada et al. (1988) , the variance/covariance estimates from the previously defined bivariate mixed-model were used to compute estimates of genetic (ŝ 2 G ), genetic by environment (ŝ 2 G • E ) and residual (ŝ 2 e ) variances across sites as follows: ŝ2 G ¼ ŝu12 , ŝ2 G • E ¼ 1 2 ðŝ 2 u1 þ ŝ2 u2 Þ 2 ŝu12 ; and ŝ2 e ¼ 1 2 ðŝ 2 e1 þ ŝ2 e2 Þ: Association mapping on NIRS absorbance Association mapping was carried out along spectra considering the absorbance at a given wavelength as a bivariate trait (corresponding to the two environments) and using previous estimates of genetic and residual variances (EMMAX philosophy as previously proposed in the multi-trait mixed-model approach  (Korte et al.
The main difference between S1 and S2 was that in S1, we can expect NIRS to be more related to the phenotypic data than in S2. For both scenarios, 5-and 8-fold cross-validation procedures repeated 20 times were used for poplar and wheat, respectively. A larger fold-number was considered for wheat in comparison to poplar because the sample size in the wheat dataset (n = 223 in the panel, and n = 161 in the subpanel) was lower than the sample size in the poplar dataset (n = 562). We would like to emphasize that in both scenarios, the predicted set was only characterized by genotypic and NIRS (obtained in the reference site) data. In particular in scenario S2, the only information available from the predicted environment is the phenotypic data of the calibration set (Figure  S1 ). Predictive ability was computed as the Pearson correlation between the predictions and adjusted means.
The TBV and genomic or NIRS PV of these N individuals were then sampled from a multivariate normal distribution with means equal to 0, variances equal to 1 and covariance equal to the square root of reliability (R package mvtnorm  (Genz et al. 2018) ). The expected genetic gain was then computed as the difference between the average TBV of the 400 individuals having the best PV and the average TBV of the population (equal to 0). We selected 400 individuals because for many species, it is feasible to apply heavier phenotyping (multienvironment trials) on a few hundred individuals. We considered two situations; in the first situation, the expected genetic gain of GS and PS was computed for various genotyping and NIRS costs with a reliability set to 0. 4. In the second situation, the reliability of GS and PS varied between 0.
5 V per individual for wheat and poplar, respectively. Reliabilities were estimated as the square of predictive abilities estimated by cross-validation divided by the heritability of the adjusted means. For each combination of trait, scenario, and NIRS data considered (tissue, environment), the increase in expected genetic gain using PS instead of GS was computed with the best performing GS model as a reference. ability of SNP and NIRS through cross-validations have been deposited on github (https://github. com/visegura/PS). Supplemental material available at Figshare: https://doi. org/10. 25387/g3. 7243256.
We could show for wheat that for fixed material, NIRS collected on seeds, so before sowing, was efficient to run PS, which offers very interesting perspectives for this species. The studies on endophenotypic variations in maize  (Fu et al. 2012; Riedelsheimer et al. 2012; Guo et al. 2016; Schrag et al. 2018) , rice  (Xu et al. 2016 ) and wheat  (Ward et al. 2015 ) also demonstrated that the characterization of germinated seeds or seedlings was efficient to estimate kinships resulting in accurate predictions. These results are promising, but this needs to be tested for other species and on other datasets. The choice of the tissue on which to collect NIRS is also important in regard to the amount of work required. It is clear that it would be much easier to take NIRS on seeds in the lab than on individual leaves in the field.
ACKNOWLEDGMENTS The authors gratefully acknowledge the staff of the  INRA GBFOR experimental unit  for the establishment and management of the poplar experimental design in Orléans, the collection of wood samples in each site, and their contribution to phenotypic measurements on poplars in Orléans;  Alasia Franco Vivai  staff for management of the poplar experimental plantation in Savigliano, and  M. Sabatti  and  F. Fabbrini  for their contribution to phenotypic measurements on poplars in Savigliano. We acknowledge the staff of the  INRA Geno-Bois  platform for the preparation of samples and collection of  NIRS  Volume 8 December 2018 | The Concept of Phenomic Selection | 3969 on wood samples and the staff of EPGV and BioForA for their contribution to obtaining SNP data on poplar. We would like to thank  J.
Messaoud  for NIRS acquisition on wheat samples,  V. Allard ,  B. Adam  and  D. Cormier  for implementation of the rain-out shelter experiment ( Phéno3C ,  INRA Clermont-Ferrand) , and  E. Heumez  ( UE GCIE ) for the experiment in Estrées-Mons. We would also like to thank  A. Chateigner ,  L. Sanchez ,  G. Charmet ,  V. Allard ,  P. Martre ,  L. Inchboard  and  S. Bouchet  for useful discussions and comments on the manuscript. We are grateful to the  Genotoul bioinformatics platform Toulouse Midi-Pyrenees (Bioinfo Genotoul)  for providing computing resources. Establishment and management of the poplar experimental sites until harvests were carried out with financial support from the  NOVELTREE project  ( EU-FP7-211868 ). NIRS measurements on poplar wood samples were supported by the  SYBIOPOP  project funded by the  French National Research Agency  ( ANR-13-JSV6-0001 ).
Management of the wheat multi-environment trials was financially supported by the  French National Research National Agency  under Investment for the  Future  ( BreedWheat  project  ANR-10-BTBR-03 ) and by  FranceAgriMer. The Phéno3C platform was financially funded by the  French National Research National Agency  under the  Investment for the Future Phenome project  ( ANR-11-INBS-12 ) and by the  European Regional Development Fund  ( AV0011535 ). V. S. and R. R. designed the study, analyzed the data and wrote the paper with input from J-P. C. , P. F. R, E. P. , J. L. G. , and C. B.
In addition, in the rubber tree, C. cassiicola virulence profiles vary significantly depending on the cultivars  (Breton et al. , 2000; Atan and Hamid, 2003; Nghia et al. , 2008; Qi et al. , 2009; Déon et al. , 2014). The only C. cassiicola effector characterized so far is cassiicolin. This necrotrophic toxin was purified from the filtrate of the highly virulent isolate CCP, collected from diseased rubber trees in the Philippines  (Breton et al. , 2000; de Lamotte et al. , 2007). Cassiicolin is a 27 amino acid glycoprotein with six cysteines engaged in three disulphide bonds  (Barthe et al. , 2007). It is matured from a pre-protein encoded by the three-exon Cas gene (GenBank EF667973)  (Déon et al. , 2012a). Cas is transiently upregulated 24-48 h after inoculation of rubber tree leaves with isolate CCP, just before the first symptoms occur in a susceptible cultivar, suggesting a role during the first phase of infection.
Genome de Novo Assembly For the reference genome, Illumina genomic reads from two libraries were filtered and assembled with AllPathsLG  (Gnerre et al. , 2011). RNA-seq data for each genome were de novo assembled into consensus sequences using Rnnotator  (Martin et al. , 2010). The genome assembly was annotated using the JGI Annotation Pipeline and made available via the JGI fungal genome portal MycoCosm (jgi. doe. gov/fungi; Grigoriev et al. , 2014). The Whole Genome Shotgun project of the reference isolate CCP has been deposited at DDBJ/ENA/GenBank under the accession NSJI00000000. The version described in this paper is version NSJI01000000. For additional strains, after quality control, short reads were assembled in contigs using Velvet v. 1. 2. 10 (Zerbino and Birney, 2008)  with k-mers hash tables of 57 and automatic coverage cutoff.
Mapping back the short reads to the generated contigs validated assembly parameters, yielding >97% mapped and >90% properly paired reads with the BWA SAMPE algorithm  (Li and Durbin, 2009). Post-assembly/annotation analysis relied on a parallel implementation  (Tange, 2011)  hosted by an Ubuntu 12. 10 x64 server running on a 32-thread Intel Xeon Dell PowerEdge R720 server. The Whole Genome Shotgun project of the additional C. cassiicola strains (BioProject PRJNA428435) has been deposited at DDBJ/ENA/GenBank under the accessions POQW00000000 to POSE00000000. The versions described in this paper are versions POQW01000000 to POSE01000000.
Functional Annotation Transposable elements (TEs) and repeats were searched for in the C. cassiicola reference genome assembly by first using RepeatScout v1. 05 for de novo identification of simple repeats and then RepeatMasker open-4. 0. 5  (Smit et al. , 2013 (Smit et al. , -2015) )  with RepBase Update 20140131, RMLib 20140131 and Dfam v1. 3  (Wheeler et al. , 2013)  and NCBI rmblastn v. 2. 2. 27+  (Altschul et al. , 1990; Camacho et al. , 2009). Carbohydrate-active enzymes (CAZymes) were predicted in fungal genomes using predicted protein sequences as queries and CAZyme Analysis Toolkit  (Park et al. , 2010)  and the CAZy database  (Lombard et al. , 2014)  (http://www. cazy. org). Proteins were considered as putative CAZymes if detected by both the Blast (E = 1e-5) and Pfam methods. Lipases were identified in fungal genomes using predicted protein sequences as queries and hmm profiles from the Lipase Engineering Database v3 (http://www.
led. uni-stuttgart. de) and hmmsearch (HMMER 3. 1b1; http://hmmer. org/) with an E-value inclusion threshold set at 0. 01. Proteases were identified in fungal genomes using predicted protein sequences as queries and the MEROPS v9. 12 blast database  (Rawlings et al. , 2014)  (http://merops. sanger. ac. uk/) with Blastp (E = 1e-4). Genes putatively involved in the secondary metabolism were searched for: non-ribosomal peptide synthases (NRPSs), polyketide synthases (PKSs) and terpene synthases (TSs) were identified using predicted protein sequences as queries and a local distribution of antiSMASH v2. 0  (Blin et al. , 2013). Proteins were predicted as potentially secreted when the following two conditions were satisfied: (i) absence of a transmembrane domain identified by our TMHMM v2. 0c local server  (Sonnhammer et al.
Protein Clustering Clusters of homologous proteins were computed using the latest available standalone version of OrthoMCL 2. 0. 9  (Li et al. , 2003; Fischer et al. , 2011). Protein sequences were first filtered using the default complexity setting (orthomclFilterFasta <PATH> 10 20) yielding 578,863 proteins concatenated into a single blast database. All-versus-all blastp was then carried out with NCBI Blastp v2. 2. 30+ with the E-value filter set at 1e-5. MCL  (Enright et al. , 2002)  was then used with the inflation parameter set at 1. 5. We determined this value experimentally using a manually curated cluster of orthologous major intrinsic proteins (MIPs), X-intrinsic proteins (XIPs). Using inflation set at 1. 5, we obtained the least number of protein clusters while keeping XIPs distinct from other MIP families.
cassiicola isolates studied. After Gblocks selection, only the polymorphic residues were conserved in the alignment using SNP-sites  (Page et al. , 2016)  yielding 37 chimeric concatenated sequences of 300,213 residues for final maximum likelihood tree calculation using RAxML with LG substitution matrix and 1,000 bootstraps. Tree rendering was carried out using FigTree v1. 42 (http://tree. bio. ed. ac. uk/ software/figtree/).
Statistical Analysis Data management and statistical analysis were carried out using R version 3. 2. 1 (R Core Team, 2015) (http://www. R-project. org/). PCA analyses used FactoMineR package  (Lê et al. , 2008). RNA-Seq expression was rendered using the heatmap. 2 function of the plots package  (Warnes et al. , 2016). Hierarchical clustering was computed using the R base package using Euclidean distances and the Ward method. Gene ontology enrichments were computed by a chi-squared test between observed and expected abundances and p-values were adjusted with a 0. 01 FDR under a dependent multiple hypothesis (GO abundances are not independent since they are graph-related)  (Benjamini and Yekutieli, 2001).
RNA-Seq Analysis RNA-Seq analysis was carried out using our custom pipeline  (Garavillon-Tournayre et al. , 2015)  running on the GenoToul computing cluster. Briefly, 125-bp pair-end shorts reads were first decontaminated by mapping against a database of potential contaminants, then finally mapped on annotated CCP transcripts using the Burrows-Wheeler alignment algorithm BWA-MEM  (Li and Durbin, 2009). Owing to the very small proportion of short reads that targeted fungal RNA (0. 05%), counts for most genes were very low. Transcripts with fewer than three counts in at least two of the eight biological replicates (25%) were filtered out, allowing 1,052 transcripts for differential analysis. Differential expression analysis was carried out using the DEseq2 package  (Love et al. , 2014)  with 0.
Lipases Lipases form an emerging field of research into virulence mechanisms in fungi. Lipases (triacylglycerol acylhydrolases E. C. 3. 1. 1. 3) hydrolyse carboxyl ester bonds on triacylglycerols, and belong to the class of α/β hydrolase fold enzymes. Notably, secreted lipases may act against host morpho-anatomical barriers (wax and cuticle). We identified 104 putative lipases in CCP, including 40 with secretion signals (27 SSPs and 13 LSPs) (Figure  2 , Table  S4 ). Compared with the other species, with a mean value of 127 lipases, CCP had one of the lowest numbers of lipase representatives, the maximum being observed for Glci with 271 lipases. Cutinases (E. C. 3. 1. 1. 74) are esterases that hydrolyse cutin polymers into cutin monomers. They can be annotated as members of the CE5 class of CAZymes or as lipases, since they also belong to the α/β hydrolase fold class of enzymes, and are reported as the abH36 superfamily in the Lipase Engineering Database (http://www.
The last cluster (black) is composed of the four species with the lowest numbers of total and effector genes in this selection (Figure  2 ), i. e. , the Ascomycetes Saccharomyces pombe (Scpo), Saccharomyces cerevisiae (Sace), and Tuber melanosporum (Tume), together with the Basidiomycetes Ustilago maydis (Usma). While the three Ascomycetes are non-pathogenic (saprotrophic or ectomycorrhizal), U. maydis is a plant pathogen of maize and teosinte, emphasizing that even species with a small genome and a small effector arsenal can be pathogenic, though with a narrow host range  (Kämper et al. , 2006). Of the 15 components of this PCA analysis, the first two dimensions covered over 62% of the total variance of the dataset. The first dimension, which accounts for 52% of the total variance, shows a large contribution from CAZymes and a smaller one from lipases and peptidases, while the second dimension shows the contribution of secondary metabolism genes.
We carried out an intraspecific PCA based on the composition in accessory effectors found among the 36 C. cassiicola isolates (Figure  6 ). Despite the high number of dimensions, the first two explained >32% of the total variance. The classification obtained was fully consistent with the genome-wide phylogeny depicted in Figure  5. We note that the phylogram and the accessory effector PCA cladogram were obtained by totally independent datasets: in the first case, the ∼12,000 core genes (found in all isolates in one copy), and in the second case, the accessory effector gene subset. This result suggests that new accessory effectors have been acquired mostly through evolution. However, isolated acquisitions by horizontal gene transfer cannot be excluded, though not detected here. Whatever the classification approach, no clear structure by geographic origin or host plant could be established.
PART 4: The Case of Cassiicolin The most emblematic and best characterized C. cassiicola effector to date is the cassiicolin toxin. The Cas1 gene (GenBank EF667973) was not predicted by the JGI annotation pipeline despite high RNA-Seq reading coverage (>3,900), possibly due to its short size and its intron-exon organization. We could nevertheless locate it on scaffold 130:2337-2848, minus strand. According to the JGI VISTA plot of scaffold 130 (4,131 bp), compared with the other genomes of our interspecific study, no nucleotide sequence homology could be found by blast against the entire NCBI nr and MycoCosm databases. This suggests that the genomic region bearing Cas1 was acquired recently, either through evolution or horizontal gene transfer from still unknown organisms. Interestingly, a fragment homologous to a MOLLY transposon from S.