dataesr/scientific-paragraphs-categorization huggingface dataset:
In the Neolamarckia cadambia RNA-Seq experiment  [12] , roots were treated with 400 μM +Al for 1, 3, and 7 days, while controls were grown without Al3 +. The experiment made in 20 RNA-Seq libraries, each defined by one of four time sets and two conditions: untreated roots at 0, 1, 3, and 7 days old (AL0, AL1, AL3, AL7) and treated roots at 1, 3, and 7 days old (AL1t, AL3t, AL7t). We analyzed the RNA-Seq raw data using FastQC v0. 11. 8 tool  [63]  and removed low quality reads and adapters with the AdapterRemoval v2. 3. 0 software  [50]. Then, we mapped the data against the respective genome using HISAT2 v2. 2. 0  [27]  with default parameters. Finally, we used the featureCounts v2. 0. 0 tool  [30]  to count and normalize the transcripts. We carried out a coexpression analysis with the Clust v1.
Genome-wide prediction of metabolic gene clusters in the Rubiaceae family The surge in large-scale transcriptomic and genomic datasets has opened new dimensions in plant comparative genomics. This work demonstrates the application of omics techniques and bioinformatics tools for discovering metabolic gene clusters. Our analysis of MGCs across eight Rubiaceae species plus Solanum lycopersicum allowed us to predict a total of 2372 metabolic gene clusters using two pipelines. In Fig. 2 , we show the distribution of these genes among clusters and species. Using the PlantClusterFinder pipeline, we identified a total of 1931 metabolic gene clusters containing 31,392 genes, with detailed results in Table  3  and supplementary Table  S1. We identified an average of 214 MGCs per species, with the lowest number occurring in L.
The average number of genes per MGC was 13. The authors from the genome assembly of N. cadamba study  (Zhao et al. , 2021 ) also predicted MGCs using PlantiSMASH and 622 genes identified in MGCs by their study were identified in our analysis. These pipelines are based on different methodologies and algorithms, leading to substantial discrepancies in the MGCs they predicted. PlantClusterFinder identified 41. 7% of the MGCs that were predicted by PlantiSMASH, but only 7. 7% of MGCs detected by PlantClusterFinder were detected again by PlantiSMASH. These differences were previously reported  [11, 49]  and underscore the importance of considering multiple methods in MGC discovery and the inherent complexities in these types of analyses. We compared the identified MGCs found in Rubiaceae species were homologous to MGCs from other plants from the curated database within the "Minimum Information about Biosynthetic Gene clusters'' (MiBIG) repository  [57].
The protein domain family for cytochrome P450 (PF00067) was the most frequently detected, followed by the UDP-glucuronosyl and UDPglucosyl transferase (PF00201), as well as the 2OG-Fe(II) oxygenase superfamily (PF03171). In addition to the protein domain search, we performed a search for Metacyc plant pathways for each gene within a Metabolic Gene Cluster (MGC) that was predicted by PlantiSMASH. We chose to do this because the PlantClusterFinder (PCF) pipeline employs this methodology (see supplementary Table  S3  for additional information). The most frequently identified pathway was the Secologanin and Strictosidine biosynthesis pathway (PWY-5290). Following closely were the Sesaminol Glucoside/lignan biosynthesis pathway (PWY-7139), the Quercetin Glucoside/flavonoid biosynthesis (PWY-7129), and the flavonoid biosynthesis pathway (PWY1F-FLAVSYN).
jasminoides and Coffea canephora containing crocin biosynthetic genes, in which specific duplications of the genome can explain the synthesis of this compound in Gardenia and the absence in Coffea. One of these syntenic regions contains a tandem array of genes encoding the UDP-glycosyltransferase family (UGT) (PF00201. 21) in Gardenia, on chromosome 9, which has an orthologous relationship with UGTs identified on chromosome 2 of C. canephora  [65]. We identified a MGC in the chromosome 2 of C. canephora, CMG CC02_cl15_sac, predicted to be involved in saccharide biosynthesis, which corresponds to this region analyzed by Xu et al. [65]. C. canephora MGC was identified in our analysis because, in addition to UGTs, this region of the genome has a gene from the cytochrome P450 family (PFAM domain PF00067.
9), an enzyme usually involved in the synthesis of matrix polysaccharides such as xyloglucan. The tomato aminotransferase is an ACC synthase paralog (Solyc04g077410, domain PF00155. 24), a key enzyme implicated in the synthesis of ethylene. We analyzed the expression of orthologs of two core genes of the tomato MGC in this gene cluster family (cellulose synthase and ACC synthase) and observed that they are not the most expressed of their respective gene families. We also observed that such orthologs are not coexpressed, however, this conservation suggests that the synthetic processes of polysaccharides and hormones are physically linked in the genome of several species of Rubiaceae. Future functional studies interrupting or overexpressing these genes would help in the final understanding of the function of this MGC.
We examined whether genes within MGCs shared coexpression modules. Consequently, we considered MGCs with core genes in the same coexpression module as high-confidence MGCs. In total, we identified 207 MGCs where at least three core metabolic genes were located in the same coexpression module. Of this total, 204 MGCs were also part of a gene cluster family, indicating conservation among other species in the study. The coexpression analysis for the C. arabica dataset yielded two coexpression modules, with a total of 11 MGCs showing coexpression and conservation in other species. These MGCs were considered highconfidence (supplementary Table  S6 ). For the C. canephora dataset, the coexpression analysis resulted in four coexpression modules, with 74 MGCs considered high-confidence (supplementary Table  S7 ).
The C. eugenioides dataset revealed four coexpression modules, with 9 MGCs considered high-confidence (supplementary Table  S8 ). In the G. jasminoides dataset, the coexpression analysis identified three coexpression modules, and 11 MGCs were deemed high-confidence (supplementary Table  S9 ). The O. pumila dataset yielded three modules in the coexpression analysis, with 17 MGCs considered high-confidence (supplementary Table  S10 ). Lastly, the coexpression analysis for the N. cadamba dataset resulted in three coexpression modules, and a total of 82 MGCs were considered high-confidence (supplementary Table  S11 ). In addition to this, our cross-species analysis demonstrated the power of using coexpression modules for MGC identification. Genes within a metabolic pathway are often coexpressed, and finding these coexpression modules can provide strong evidence for the functional relevance of the predicted MGCs.
In our study, we identified 207 high-confidence MGCs where at least three core metabolic genes were located in the same coexpression module. This approach not only enhances the confidence in MGC predictions but also provides a functional context to understand how these genes may work together in metabolic processes. In conclusion, our analysis has successfully elucidated the complex landscape of MGCs across multiple plant species, paving the way for more targeted and in-depth studies in the future. The identification of coexpression modules also highlights the relevance of such cross-species comparative methods in unravelling potential functional associations and underlying genetic influences in metabolic pathways. One limitation of our study was the small amount of public transcriptome datasets suitable for co-expression analyses in the Rubiaceae family.
Acknowledgments This research was supported by resources supplied by the  Center for Scientific Computing (NCC/GridUNESP)  of the  São Paulo State University (UNESP). The authors acknowledge the  Wageningen University Bioinformatics Group at Wageningen University  for providing computing resources that have contributed to the research results reported within this paper. The authors acknowledge the  IRD itrop HPC (South Green Platform)  at  IRD Montpellier  for providing HPC resources that have contributed to the research results reported within this paper.
Moreover, glycolytic enzymes were also reported to be involved in piRNA biogenesis  30. Using Aub iCLIP (individual-nucleotide resolution UV crosslinking and immunoprecipitation) datasets from early embryos  31  and cultured GSCs  18  , we identified a total of seven glycolytic mRNAs among mRNAs encoding the eleven glycolytic enzymes (Figure  1A ). This suggested that Aub could regulate glycolysis through direct interaction with glycolytic mRNAs. Here, we show that glycolysis is the main energetic pathway in GSCs and that high glycolysis is required for GSC self-renewal. Either reducing glycolysis by decreasing the levels of glycolytic enzymes, or increasing oxphos in GSCs promote GSC differentiation, preventing their maintenance. Strikingly, a switch in energy metabolism towards oxphos takes place in aub mutant GSCs.
Bioinformatics and statistics Quantification of glycolytic mRNAs using RNA-seq. We used published datasets (GSE119862)  49  in which we selected RNA-seq performed in cultured GSCs and nos-Gal4/UASp-tkv M1 ovaries, which we analyzed using Salmon  65  (https://combine-lab. github. io/salmon). Transcript quantification was done using --validateMappings option with the raw data on Drosophila melanogaster genome (FlyBase release 6. 28). Each RNA-seq dataset was analyzed independently and glycolytic mRNAs were filtered using thresholds of minimum expression of 10 and 100 Transcripts Per Million (TPM) in all replicates. Construction of piRNA library from small RNA-seq in cultured GSCs. The small RNA-seq datasets in cultured GSCs (GSE119862)  49  were analyzed using a simplified homemade version of the pipeline piPipes  66   (https://bowtiebio.
piRNA targeting close to Aub binding sites in glycolytic mRNAs. Aub cross-link sites in cultured GSCs were obtained from published iCLIP experiments (GSE96751)  18  and iCLIP genomic coordinates were converted to the dm6 release using the online converter of FlyBase. Using our piRNA library, bowtie was configured with different settings to identify potential target sites on transcripts. Nine targeting conditions were defined as follows: 0 to 3 mismatches within the piRNA sequence using option -v set up from 0 to 3; targeting of a 5' seed of 16 nt without mismatch, 18 nt (A) The ten steps of glycolysis. Enzymes whose mRNAs were found in GFP-Aub iCLIP datasets  18, 31  are in red. Aub binding to glycolytic mRNAs is statistically significant, p=1. 0e-05 using the      Figure legends.
Acknowledgements We are grateful to  A. Arkov ,  T. Kai  and the  Bloomington Drosophila Stock Center  for providing Drosophila stocks. We thank  P. Y. Plaçais ,  T. Préat  and  J. R. Huynh  for their gifts of plasmids and R. Lopez-Alemany  for her gift of antibodies. We thank  M. Jault  for participating in initial experiments shown in Figure 2 and  P. P. Marie  for participating in experiments shown in Figures 6, S1 and S4. This work was supported by the  CNRS-University of Montpellier   UMR9002 ,  ANR  ( ANR-15-CE12-0019-01 ,  ANR-19-CE12-0031 ,  ANR-21-CE12-0035-01 ) and FRM. PPM held a salary from FRM. CE and JC held a salary from  ANR. CG held a salary from  ANR  and  MSDAvenir , and AR held a salary from  ANR  and  Fondation ARC.
(https://github. com/bowhan/piPipes). The raw data were first trimmed with Cutadapt 67 (https://github. com/marcelm/cutadapt) as follow -a TGGAATTCTCGGGTGCCAAGGAACTCCAGTCACnnnnnnATCTCGTATGCCGTCTTCTGCT TG -m 18 -M 30 --discard-untrimmed. The alignment on the Drosophila melanogaster genome (FlyBase release 6. 28) was performed using bowtie 68.
Second, we ran GLMMs  (Baayen et al. , 2008)  with the glmmTMB package  (Brooks et al. , 2017). For each of the two tasks, we prepared the dataset by entering one line for each trial and individual that was visible in the trial. The rst two full models assessed whether wolves and admixed wolves differed in their reaction to unfamiliar humans. We used a binomial distribution to model whether the probability of showing vigilance (M1) and the probability of spending time in human proximity (i. e. coming closer/remaining at the same distance to the human) in the rst ten seconds of the trial (M2) differed between wolves and admixed wolves. In both models we included admixture as test predictor (i. e. wolves vs. admixed wolves), and as controls the individual's rank (as rank may affect individuals' reaction to novelty; e.
g. ,  Boogert et   ) and trial number, with individual identity as random factor (to control for the lack of independencies in the dataset). In the human task, only wolves showed fearful and aggressive behaviour within the experimental context, and only admixed wolves explored humans. Therefore, we refrained from running models for these variables. The next four full models assessed whether wolves and admixed wolves differed in their reaction to novel objects. We used a binomial distribution to model whether the probability of showing vigilance in the rst ten seconds of the trial (M3), and the probability of showing fearful behaviour (M4) or exploring the novel object during the trial (M6) differed between wolves and admixed wolves. We further used a beta distribution to model whether the proportion of time spent in proximity to novel objects (M5) differed between wolves and admixed wolves.
ACKNOWLEDGMENTS We would like to thank  Katja Lammers  at the  Osnabrück Zoo ,  Fernando Di Fabrizio , president of  COGECSTRE  and Director of the  Riserva   Naturale Regionale Lago di Penne ,  Giorgia Romeo  at the  Regione Toscana ,  Donata Marruchi , Director of the  Parco Faunistico del Monte Amiata  for allowing us to collect data on the different study groups. We are sincerely grateful to  Damien Farine  and  Gabriel Ramos Fernandez  for generously sharing thoughts and ideas about social network analyses.
(Bayer et al. 2019; Dolatabadian et al. 2020) , individual characterization of these candidates is still a major challenge and limiting factors exist in use of this information for more applied genetics and breeding. Although such studies have provided useful and extensive databases for reference, the primary current use is likely to be in interrogating candidate genes identified to underlie experimentally determined resistance QTL  (Cantila et al. 2022) , as merely the identification of a resistance gene candidate is not sufficient to know if this gene is in any way involved in any particular pathogen response. However, this method may increasingly gain utility as our understanding of gene and protein interactions, signaling and gene pathways improves, particularly in conjunction with transcriptomic approaches involved in the pathogen infection response  (Borhan et al.
2022). A first approach developed to reduce the number of candidates is to compare predicted RGAs with previous knowledge obtained from the genetic mapping of resistance loci to identify robust candidates: such an approach has been carried out already in B. oleracea and B. napus  (Bayer et al. 2019; Dolatabadian et al. 2020). Resistance gene analog prediction can also be compared or applied to transcriptomic data (e. g. Lowe et al. 2014; Becker et al. 2017 Becker et al. , 2019;; Hubbard et al. 2020; Song et al. 2021 ) and applied across accessions with known resistance responses and large databases such as BnPIR. This latter approach can be particularly useful, considering the high diversity in terms of gene content between accessions, to assess the conservation of a particular candidate resistance gene analog  (Song et al.
Cloning of avirulence genes Forward genetic approaches have been very successful in cloning avirulence genes in the L. maculans / B. napus pathosystem, with AvrLm1, the first AvrLm gene cloned in 2006  (Gout et al. 2006 ). This gene was previously genetically identified by Ansan-Melayah et al. (1995) using a map-based cloning strategy which allowed the identification of the genetic interval on a BAC (Bacterial Artificial Chromosome) contig. This cloning highlighted interesting characteristics of AvrLm1 and its genomic environment: located in the middle of a large TE-rich region, AvrLm1 itself encodes a small protein (205 amino-acids) with one cysteine residue and a peptide signal and has no homology with other sequences available in public databases. It was later shown that these characteristics (except for cysteine enrichment) were shared by all avirulence genes that were subsequently cloned (subsequently referred to as SSPs for Small Secreted Proteins), together with low or no expression during axenic growth and specific induction during the asymptomatic infection of B.
and the availability of datasets representing genotyping data from c. a. 1,700 accessions (Song et al. 2021 ) (BnPIR database: http://cbi. hzau. edu. cn/bnapus/). This scaling up of available genome data has been mirrored by an extensive effort to predict resistance gene candidates, first in the single sequenced accession 'Darmorbzh' with 425 NBS-LRR genes predicted in the first genome assembly (Chalhoub et al. 2014 ) and more recently based on a B. napus pangenome or on available Brassicaceae sequence data (Dolatabadian et al. 2020; Tirnaz et al. 2020). Dolatabadian et al. (2020)  predicted 1,749 RGAs across 50 accessions of B. napus, including almost 400 absent from the reference genome.
Brassica species, an issue long recognized by breeders and researchers. This is an ongoing problem, and although solutions such as a registered accession database have been suggested and may be implemented in time (see e. g. Yang et al. 2023) , to date most research groups simply reconfirm resistance gene presence via cotyledon testing with specific LEM1, LmFr1, LmR1, ClmR1, assigned to chromosome A07 of B. napus, are now assumed to all correspond to Rlm4 based on map position and phenotypic responses; Rouxel et al. 2003b; Rimmer 2006). The availability of reference genomes and the possibility to physically localize R and Avrlm genes cleared up a lot of initially confusing results from early linkage mapping studies, where the same gene was often repeatedly detected or where different genes were occasionally conflated (Borhan et al.
ACKNOWLEDGMENTS This work was supported by the  CNRS , the  University of Strasbourg , the " Agence Nationale de la Recherche "( BioconjUgi ;  ANR-19-CE07-0045  and  Conformabs ;  ANR-21-CE29-0009-01 ), the  French Proteomic Infrastructure  ( ProFI ;  ANR-10-INBS-08-03 ) and the  Interdisciplinary Thematic Institute IMS  ( Institut du Médicament Strasbourg ), as part of the  ITI 2021-2028  supported by  IdEx Unistra  ( ANR-10-IDEX-0002 ),  SFRI-STRAT'US  project ( ANR-20-SFRI-0012 ). R. B. and I. K. acknowledge the  European Union 's  Horizon 2020 Research and Innovation Program Marie Skłodowska Curie Action ITN  for funding their PhDs under Grant Agreement No. 859458. V. V. acknowledges the " Agence Nationale de la Recherche"  for funding her PhD ( BioconjUgi ;  ANR-19-CE07-0045 ). The authors would also like to thank  Prof.
This suggests that natural selection did not favor combinations of mutations that would lead to high antibody binding. It should be noted that in each curve on Figure  2c , four out of five dissociation constants are fixed. Thus, the inferred fitness for each data point in these one-dimensional cross sections of the fitness landscape does not represent its actual fitness. Further validation of the robustness of our model was undertaken by applying it to ten non-overlapping subsets of observed variants (Figure  2d ). The error bars illustrate minor variations in coefficients, indicating the model's stability. Moreover, the coefficients for datasets, both with and without the T478K mutation, were remarkably similar. Intriguingly, the effective concentrations derived from our model for antibody binding demonstrated remarkable consistency across LY-CoV016, LY-CoV555, and REGN10987.
To further demonstrate the predictive capacity of our model, we assessed its ability to forecast fitness of future variants. The biophysical model was fitted on the dataset comprising all registered variants prior to May 01, 2021, which included only 42 data points. This trained model was then deployed to predict fitness of subsequent variants that emerged between May 1, 2021, and May 1, 2022, a period in which 843 unknown variants arose. Importantly, our model exhibited a notable predictive power, accurately predicting the leap in fitness induced by the Omicron B. 1 variant and its neighboring variants, as depicted in Figure  3c , with an 𝑅 2 value of 0. 76 (see SI). This underscores the model's efficiency in predicting fitness of unseen variants, even when trained on relatively sparse data from the early stages of the pandemic.
[14]  or other non-epistatic models. To calculate the epistatic coefficients, we utilized a linear model in which the effects of mutation combinations summed to the 145 phenotype of a sequence (see "Methods: Epistasis analysis" for definitions and details). We then applied this model to fitness values inferred from our biophysical model across all 32,768 possible combinations of mutations. The performance of the model was evaluated (expressed as the coefficient of determination, 𝑅 2 ) for different orders of epistasis on a withheld test dataset, constituting 10% of the total data. A first-order model (comprising 16 coefficients) produced an 𝑅 2 value of 0. 96, while a second-order model (comprising 121 coefficients) achieved an 𝑅 2 of 0. 98. The high correlation values and satisfactory representation of the data suggest that a second-order model sufficiently captures the key dynamics, thereby alleviating the necessity for higher-order models.
These high correlation values are attributable to our model's ability to fit to real fitness coefficients obtained in a non-epistatic model from Obermeyer et al. [14]. Despite this, our biophysical model successfully captures epistatic effects as it is based on the dissociation constants of the RBD to antibodies and ACE2, which inherently exhibits epistatic behavior. Following the training of the second-order model across the complete dataset to derive final coefficients, we observed that most single mutations (reflected on the diagonal of the matrix in Figure  4c ) have a positive impact on fitness. Interestingly, one mutation, Y505H, exhibited a negative first-order coefficient, which is nonetheless offset by epistasis. We discovered that mutations with a pronounced effect on antibody escape-specifically K417N, G446S, E484A, and Q493R  [8] -individually confer high fitness.
RBD Binding Affinity We acquired the binding affinity data from the work of Moulana et al. [8, 9]  In their study, they systematically examined the interactions between all possible combinations of 15 RBD mutations (totaling 32,768 genotypes) and ACE2, as well as four monoclonal antibodies (LY-CoV016, LY-CoV555, REGN10987, and S309) via Tite-seq measurement. In situations where binding affinities in their dataset were too low to measure accurately, we have chosen to substitute these with a fixed value of 5. We stress that this choice of value is not expected to influence our study's outcomes. As indicated in Figure  2c , the antibody escape largely resides in the upper plateau region of fitness curve, thus, this preset value for variants with immune escape to antibodies does not have a substantial impact on the logistic regression results.