dataesr/scientific-paragraphs-categorization huggingface dataset:
Ultimately, it will be necessary to demonstrate an association between gene expression/gene function and the trait in natural populations. MYB transcription factors comprise 14 out of the 43 members of the final dataset and are the only enriched For each gene in the final dataset (Gene ID column), the nature of the high-impact mutation, the predicted transcription factor family, and the closest Arabidopsis homolog gene symbol are listed. S2. © 2020 The Authors. The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd, The Plant Journal, (2020), doi: 10. 1111/tpj. 14962 class of genes. This is in line with the thought-provoking suggestion that MYBs may be preferred targets of pollination syndrome evolution  (Yuan et al. , 2013). MYB factors AN2 and MYB-FL and their homologs in Mimulus (and probably many other plant species) are key regulators of the biosynthesis of floral anthocyanins and UV-absorbing flavonols, respectively  (Quattrocchio et al.
The workflow 1) Identification of transcription factors expressed in the style. Global transcription factor assay-In order to identify putative transcription factors in the Petunia genome, publicly available sequences of Petunia proteins (ftp://ftp. solgenomics. net/genomes/ Petunia_axillaris/annotation/) were scanned against 57 Pfam  (Finn et al. , 2013)  domain signatures of known plant transcription factors, using the list from  (Rushton et al. , 2008)  as a reference with an E-value of the hit of <0. 01 as a filtering threshold. Additionally, as some of the discovered factors do not have a domain signature yet, a simple scan using BLAST+ v2. 6. 0  (Camacho et al. , 2009)  was performed for a number of such sequences to identify their homologs. The final transcription factor dataset contains 2284 protein IDs.
The replicas are independent biological replicas from different plants. Libraries were prepared using Illumina TruSeq PE Cluster Kit v3 and RNA material was sequenced in the Lausanne Genomic Technologies Facility (Lausanne, Switzerland) on an Illumina HiSeq 2500 platform (Illumina, Foster City, CA, USA) in a single-end mode with a read length of 100 bp. All raw sequencing data were submitted as BioProject PRJNA533335 to NCBI. RNAseq data analysis-Raw read data were quality-checked using FastQC software v. 0. 11. 2 with a subsequent filtering of the reads based on the final length (at least 60 bp) and the quality score of the bases in the read (at least Q25), along with removing the Illumina adaptor sequences, performed by the fastq-mcf tool, which is a part of the ea-utils package v. 1. 1.
2 (Aronesty, 2013). Filtered reads were then mapped to the P. axillaris reference draft genome v. 1. 6. 2 using STAR v. 2. 5. 0b (Dobin et al. , 2013)  in a basic two-pass mode with a custom edit of the P. axillaris annotation (original accessible at https://solgenomics. net/organism/Petunia_a xillaris/genome) as a reference for splice junctions with --sjdbOverhang 99 and ignoring reads that map more than 20 times in total. The resulting readmaps along with the same custom annotation served as a base for read counting performed by the htseq-count tool v. 0. 6. (Anders et al. , 2015). The calculations were performed on UBELIX (http://www. id. unibe. ch/hpc), the HPC cluster at the University of Bern. The count data obtained from htseqcount were processed using the DESeq2 v. 1. 12. 4  (Love et al.
, 2014)  package in the R v. 3. 3. 2 computing environment (R Development Core Team, 2016). In order to exclude non-and lowly expressed genes from the analysis, after running the DESeq normalization process the data were filtered with the HTSfilter (v. 1. 12. 0) (Rau et al. , 2013 ) package (resulting s = 27. 9). Samples were clustered using the R PoiClaClu package v. 1. 0. 2 (Witten, 2011). During the data preparation step a measure against possible pollen contamination of the long stage samples was includeda group of genes annotated as pollen-specific was found exhibiting a highly similar pattern of expression (average correlation coefficient per gene within the group from 0. 95 to 0. 98) with counts above filtering threshold present only at the latest stage of the experiment in all studied species.
A decision to remove all genes correlating with any of the members of the initial group with a positive coefficient of at least 0. 95 was made, excluding 488 genes from the analysis (22 005 genes remaining in the analysis). Counts were normalized using an rlog-transform in DESeq2 and for each gene mean counts were computed over the sample replicates. These averaged and normalized counts were used in the correlation analyses. © 2020 The Authors. The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd, The Plant Journal, (2020), doi: 10. 1111/tpj. 14962 2) Correlation of transcript levels with growth-associated traits. The values for various growth and mechanical properties of each experiment stage were correlated against the filtered mean normalized expression data to obtain PCCs.
341, for style growth rate 0. 346, for cell division rate 0. 408, and for cell wall elasticity 0. 466. 3) Allele-specific expression (ASE) in interspecific F 1 s. Allelic coverage for variant positions in P. axillaris x P. exserta F 1 and P. axillaris x P. parodii F 1 (Axs x Exs F 1 and Axs x Par F 1 ) read maps was detected with ASEReadCounter implemented in GATK  (DePristo et al. , 2011; Castel et al. , 2015). Variant positions were filtered using hard thresholds with variants having any of the DP < 10, QD < 2, MQ < 40, FS > 60, MQRankSum < À12. 5, and ReadPosRankSum < À8. 0 removed from further analysis. Clustered SNPs with more than three occurrences in a window of five were also omitted. Analysis of allelic imbalance was conducted in R software with the package MBASED v. 1. 2. 0  (Mayba et al.
, 2014)  using rounded read counts averaged over the biological replicates and one-sample mode, which identifies within-sample deviations using a nullhypothesis of equal allele expression. A correction for potential allele bias was implemented with the help of the package using the single-SNP genes with more than 30 reads detected for both alleles and low allelic disequilibrium (less than 1. 4-fold difference in read count, final rho estimated as 0. 00308). The results of the assay were controlled for the false discovery rate by applying the Benjamini-Hochberg corrections procedure  (Benjamini et al. , 2001). The R script used for RNAseq data processing, the ASE data, and the gene annotations can be found in Data S2-S5. 4) Analysis of functionally relevant SNPs in the coding region. In order to identify functional SNPs, variant maps obtained for ASE analysis were scanned using SNPeff v.
, 2016). DNA material was sequenced on an Illumina Hiseq3000 platform, producing 150 bp paired-end reads with 400 bp library insert size. Reads were quality-controlled using trimmomatic v. 0. 33  (Bolger et al. , 2014) ; the results were checked using FastQC software v. 0. 11. 2  (Leggett et al. , 2013). For the assembly, an optimal kmer value of 47 was estimated with the help of jellyfish v. 2. 1. 0 (Zimin et al. , 2013)  with the -m 21 parameter. Assembly of quality-controlled reads was performed by SOAPdenovo2 v. 2. 04. 240  (Luo et al. , 2012)  with parameters max_rd_len = 150, avg_ins = 400, reverse_seq = 0, rd_len_cutoff = 150, rank = 1, pair_num_cutoff = 4, and map_len = 32. A summary of the assembled draft genome can be seen in Table  2. In order to estimate the gene content in the assembly, P.
ACKNOWLEDGMENTS We thank  Holly Summers  and  Hagen Reinhardt  for help with sample collection, RNA extraction, and phenotyping, and  Christopher Ball  and  Jasmin Sekulovski  for expert care of the plants. Korinna © 2020 The Authors. The Plant Journal published by  Society for Experimental Biology  and  John Wiley & Sons Ltd , The Plant Journal, ( 2020 ), doi: 10. 1111/tpj. 14962Transcription factors controlling floral morphology in wild Petunia species 11 Esfeld and  Gina Cannarozzi  carefully read and commented on the manuscript. This work was supported by  SystemsX. ch,ERC Advanced  Grant  741354 ,  Swiss NSF  grant  31003A_182340 , and  University of Bern  core funding. Calculations were performed on UBELIX (http://www. id. unibe. ch/hpc), the  HPC cluster at the University of Bern.
DATA STATEMENT The large-scale datasets including transcriptomes, genomes are freely available at NCBI and the Sol Genomics Network (solgenomics. net). The P. parodii data is housed under NCBI bioproject PRJNA533335 with the following accession numbers: JACAFL000000000 (assembly), SRX5714874 (nucleotide reads) and SRX5711291-SRX5711362 (RNASeq reads). The Petunia axillaris v4 annotations are available at: https://zenodo. org/record/3922967.
, 2020). These analyses depend on the inclusion of accurate explanatory-variable datasets and accurate process representation in models, which may not be the case. Quantification of the effect of iCO2 on carbon storage in terrestrial ecosystems remains elusive. As with BP responses, studies of forest inventories show higher C veg responses (β app = 0. 3-2, mean = 0. 85) than studies of eCO2 experiments (β app = 0. 22-0. 39) (Fig. 3 ). However, the highest values come from two analyses: one that includes global forest regrowth (β app = 1. 9; Pan et al. , 2011) and younger (c. 50-100 years old) temperate forests (β app = 2±1;  McMahon et al. , 2011). Exclusion of these higher change studies results in a narrower range (β app = 0. 3-0. 85, mean = 0. 57). This exclusion narrows the difference between responses inferred from iCO2 and eCO2 studies, which is consistent with theory as relative stock changes are under-estimated more in short-term experiments than in inventory-type studies (Fig.
JBF contributed to this research at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration  with support from the  NASA   IDS program. VH acknowledges support from the  Earth Systems and Climate Change Hub  funded by the  Australian. FJ acknowledges support by the  Swiss NSF  (# 200020_172476 ). TFK was supported by the  NASA   Terrestrial Ecology Program IDS  Award  NNH17AE86I. PC and JP were supported by the  European Research Council Synergy  grant  ERC-2013-SyG-2013-610028 IMBALANCE-P. J. Pongratz  was supported by the  German Research Foundation 's  Emmy Noether Program. MEBS acknowledges support from the  Australian Research Council Centre of Excellence for Climate Extremes  ( CE170100023 ). CT was supported by a  Lawrence Fellow award  through  Lawrence Livermore National Laboratory (LLNL)  under contract  DE-AC52-07NA27344  with the  U.
TLR Genotyping Primers for TLR4, 5, and 7 coding regions were designed using the primer design feature based on Primer3 2. 3. 7  (Untergasser et al. 2012 ) in Geneious v. 11. 0 (Biomatters, http://www. geneious. com). Reference coding sequences for primer design were derived from the congeneric Ad elie penguin (P. adeliae) reference genome (GenBank accession JMFP01000000) and unpublished Gentoo penguin genomic data. TLR4 amplifications for samples from MO, CB, BI, COP, and JP were conducted in 12 ml volumes (9 ml Qiagen Taq PCR Master Mix, 2 ml 10 mM forward and reverse primer mix, and 1 ml of template DNA diluted 1:100). TLR5 and TLR7 amplifications for these samples were conducted in 25 ml volumes containing 5 ml 5Â Phusion High Fidelity (HF) Buffer (New England Biolabs, UK), 0. 5 ml 10 mM dNTPs, 1.
A total of six PCR cycles were used for enrichment, purified with sample purification beads, quantified using a Qubit fluorometer, and then sequenced to $20Â coverage with 150 paired-end reads using an Illumina HiSeq X platform at MedGenome. Sequences for each TLR coding region were assembled, edited, and aligned using Geneious v. 11. 0. For TLR4, which has multiple exons along the coding region, exon sequences were extracted and concatenated for further analysis. Heterozygous sites and single-nucleotide polymorphisms were detected by visually examining chromatograms. In cases of doubt, resequencing was accomplished so that only high-quality reads from multiple sequencing runs were called as single-nucleotide polymorphisms. All heterozygous sites also had homozygous individuals within the data set, and each gene had at least one haplotype homozygous across the full length of the gene.
1 sequencing kit (Applied Biosystems) with the same PCR primers as sequencing primers. Published mtDNA HVR1 sequences for the samples from BI (GenBank accessions KJ646314-KJ646330, n ¼ 16) and COP (KJ646361-KJ646382, n ¼ 21) were included in the analysis for the relevant individuals. mtDNA data were not available for the individuals from two Antarctic sites. Though other individuals from those sites have sequence data available, we only included data from individuals sequenced for both TLRs and HVR1. Individual mtDNA fragments from all remaining sites (CR, MAR, COU, BR, MT, SIG, SP) were amplified using the primers tRNAGlu and AH530  (Roeder et al. 2002). These reactions included 0. 4 lM of each primer, 1. 5 mM 1Â of PCR reaction buffer, MgCl 2 , 200 lM of each dNTP, and 1 U of Taq Polymerase Platinum (Invitrogen) in a two-phase touchdown program  (Korbie and Mattick 2008) : 1) 10 min at 95 C, and 11 cycles of 95 C for 15 s; a touchdown with an annealing temperature of 60-50 C for 30 s, with one cycle per 1 C interval, and 72 C for 45 s; 2) 35 amplification cycles at 95 C for 15 s, 50 C for 30 s, and 72 C for 45 s; and a final extension period of 30 min at 72 C.
1992). Significance of overall and pairwise genetic distances was computed using 1,000,000 permutations. We used the SGoFþ method (Carvajal-Rodr ıguez and de Uña-Alvarez 2011) within the Myriads software (Carvajal-Rodr ıguez 2018) to correct for multiple tests. To test for isolation-by-distance, shortest distances by sea (during summer ice extent), in km, were computed between each sampling location, using Google Earth v7. 3. 2. 5776 (supplementary table S5B, Supplementary Material online), which were then related to pairwise F ST in a Mantel test, implemented in Arlequin.
Population Divergence and Phylogeography Phylogenetic reconstruction and estimates of divergence time were carried out using BEAST 2. 5. 2  (Bouckaert et al. 2019 ). The evolutionary model for mtDNA analysis was selected using jModelTest v. 2. 1. 10 (  Darriba et al. 2012) , testing 88 candidate models and selecting the best fit using the Bayes Information Criterion. All 88 models were within the 100% confidence interval, with HKYþG selected for further divergence analyses  (Hasegawa et al. 1985). A total of 15 Ad elie penguin (P. adeliae) and 15 Chinstrap penguin (P. antarcticus) mitochondrial HVR1 GenBank sequences  (Clucas et al. 2014) , aligned and cropped to the equivalent size of the Gentoo sequences to avoid bias, were included in the analysis as outgroups. The most recent common ancestor prior was set for the Pygoscelis genus at 7.
6 Ma  (Subramanian et al. 2013) , derived from the fossil calibration for P. grandis  (Walsh and Suarez 2006) , with a normal distribution, and SD (r) of 1. 3 Ma. A strict molecular clock with a starting prior of 1. 0 and a Yule speciation process for branching rates, with uniform priors for birth and clock rates of 1. 0 was applied. Four independent runs of 30 million MCMC chains were performed, logging parameters every 3,000 steps. The four independent runs were then combined using LogCombiner v. 2. 5. 2 and assessed for convergence within Tracer v. 1. 7. 1  (Rambaut et al. 2018). All parameters converged with ESS values >6,000. A maximum clade credibility tree was then generated using TreeAnnotator v. 2. 5. 2 (part of the BEAST software distribution) and visualized in FigTree v. 1. 4. 3.
Selection Analyses Phylogenetic inference was carried out on phased sequence data for each TLR locus using RAxML-NG using a GTR substitution matrix  (Kozlov et al. 2019 ). To detect selection, maximum likelihood analysis of ratios of nonsynonymous to synonymous nucleotide substitutions (dN/dS; x) was performed with the codeml package of programs in PAML v. 4. 9  (Yang 1997 (Yang , 2007)). Various models were fitted to the multiple alignments: M1a (neutral model; two site classes: 0 < x 0 < 1 and x 1 ¼ 1); M2a (positive selection; three site classes: 0 < x 0 < 1, x 1 ¼ 1, and x 2 > 1); M7 (neutral model; values of x fit to a beta distribution where x > 1 disallowed); M8 (positive selection; similar to M7 but with an additional codon class of x > 1); and M8a (neutral model; similar to M8 but with a fixed codon class at x ¼ 1).
Acknowledgments The financial support for this study was provided by an  Oxford Clarendon Fund  scholarship for H. L. and from the  Biotechnology and Biological Sciences Research Council (BBSRC)  (Grant No. BB/M011224/1 ) for S. R. F. Sample collection was funded in part by  CONICYT   PIA ACT172065 GAB  and  Spanish Ministry of Science  projects  CGL2007-60369  and  CTM2015-64720. Logistic and field costs for sampling at the Crozet and Kerguelen archipelagos were supported by the  Institut Polaire Francais Paul-Emile Victor (IPEV: Program 137-C. L. B. and Program 354-F. B. , respectively) with additional support from the  Laboratoire International Associ e 647 "BioSensib" (CSM/CNRS-University of Strasbourg ). Open access funding was provided by  ALS. We appreciate the support of all of our sampling logistics providers, including  W.
Finally, we sequenced a newly isolated strain of cedratvirus (cedratvirus kamchatka). The datasets are listed in Supplementary Table  1. The sequence data obtained from the whole collection corresponds to an average coverage of 192-fold. We then aligned the SMRT reads to their corresponding reference genomes (see Supplementary Table  1 ) and computed IPDr at each genomic position (see Methods). These genomewide profiles were used to identify overrepresented sequence motifs at positions with high IPDr values. All the identified motifs were palindromic and prone to either N 4 -methyl-cytosine or N  6  methyl-adenine methylations (Fig. 1 ). As a control, we applied the same procedure to DNA samples of mollivirus sibericum and pithovirus sibericum subjected to whole genome amplifications (WGA), which in principle erase methylation marks  33.
As expected, no overrepresented methylated motif was detected in these controls, and the median IPDr of the motifs previously detected in the wild-type datasets were basal here (see Supplementary Fig. 1  and Fig. 1 ). In parallel, we analyzed the DNA MTases encoded in these genomes and predicted their target sequences based on their homology with characterized MTases (see Methods). All the MTases for which a target site could be predicted were putative type II MTases. In 15 out of the 19 cases (79%) where a MTase target could be predicted or a methylated motif detected, we found an agreement between the two (Fig. 1 ). It is worth mentioning that this result also highlights the reliability of MTases' targets predictions based on protein homology. target GATC sites. Our data confirm that GATC motifs were modified (underlined characters indicate methylated bases) with N 6 -methyl-adenines (Fig.
1 ). We then searched for the possible cognate REase in the genomic vicinity of mel_016 and identified the neighboring mel_015 gene as a candidate. Although the encoded protein does not exhibit a recognizable motif using standard domain search tools  39, 40  , a search against REbase, the database dedicated to R-M systems  41  , identified it as a probable GATC-targeting REase. Moreover, the mel_015 protein is similar (blast E-value = 2 × 10 -33 ) to the Paramecium bursaria Chlorella virus 1 (PBCV-1) CviAI REase known to target GATC sites. Melbournevirus thus encodes a complete R-M system. The N 6 -methyl-adenine modification is typical of prokaryotic MTases. Since melbournevirus is a eukaryotic virus, our finding immediately questioned the evolutionary history of its encoded R-M system. We thus reconstructed the phylogeny of the complete system, including the MTase and the REase (see Methods).
2 ). As expected, the noumeavirus genome is digested by DpnII but not by DpnI (Fig. 3 ). This demonstrates that noumeavirus DNA is not methylated at GATC sites and is thus susceptible to degradation by a co-infecting marseillevirus bearing a functional R-M system. To verify whether the Acanthamoeba castellanii host genome was sensitive to DNA degradation at GATC sites we performed digestion assays using the same enzymes. According to its sequence (GenBank accession AEYA00000000), the A. castellanii genome should be fragmented into 171,298 pieces of 273 nt on average. Surprisingly, the A. castellanii genome is cleaved by both enzymes (Fig. 3 ). This indicates that the host DNA contains a mixture of methylated and unmethylated GATC sites. However, the restriction profiles show that unmethylated positions are in larger proportion than methylated ones.
As previously discussed for pandoraviruses  30, 38  , the process of de novo gene creation seems to participate to the shaping of cedratviruses genomes. Interestingly, among the 51 genes unique to cedratvirus kamchatka only one has a clear predicted function: the ck412 DNA MTase. DNA methylation is widespread but unevenly distributed among giant viruses genomes. The SMRT sequencing of cedratvirus kamchatka and the other methylome datasets show that the various giant virus families exhibit distinct methylation features. Whereas the genomes of pithoviruses and Mimiviridae members are devoid of DNA modifications, those of molliviruses, pandoraviruses and cedratviruses clearly contain methylated nucleotides (Fig. 1 ). More specifically, cedratvirus kamchatka DNA is methylated at CTCGAG motifs (Fig.
1 ). Although the ck412 DNA MTase has a slightly different predicted target (CTSAG), it is probably responsible for the CTCGAG methylation as CTSAG motifs are not methylated (Fig. 1 ). Cedratvirus kamchatka ck366 gene encodes an additional predicted FkbM domain-containing MTase for which the predicted specific target, if any, is unknown. Importantly, we found no REase associated with the cedratvirus kamchatka predicted MTases. Pithovirus sibericum encodes two DNA MTases: pv_264 predicted to target the CTSAG motif and pv_113, an FkbM domain-containing MTase targeting an unknown site. Yet, pithovirus sibericum DNA exhibit no methylated sites (including CTSAG and CTCGAG) (Fig. 1 ). Surprisingly, the RNA-seq data from  20  shows that both transcripts are significantly expressed all along the replication cycle (see Supplementary Tables  3  and 4 ).
1 ). They clearly do not share a common origin, and appear partitioned in five main groups, interspersed among bacterial and occasional amoebal homologs. First, one observes that most viral MTases are either embedded within clusters of prokaryotic sequences (green, and red groups in Fig. 5 ) or constitute sister groups of prokaryotes (orange, purple and blue groups). Thus, as for Marseilleviridae members these MTases are most likely of bacterial origin. Of special interest, the tree also exhibits MTases encoded by different Acanthamoeba species (see GenBank accessions in Fig. 5 ), the main known hosts of giant viruses. For instance, the closest ml_135 mollivirus MTase homolog is found in A. polyphaga, suggesting a recent exchange between virus and host. The direction of the gene transfer cannot be determined from these data.
The aqueous phase was incubated with 2 volumes of precipitation buffer (5 g L -1 CTAB, 40 mM NaCl, pH 8) for 1 h at room temperature and centrifuged for 5 min at 16,000 × g. The pellet was then resuspended in 350 µL of 1. 2 M NaCl and 350 µL of chloroform was added and centrifuged at 16,000 × g for 10 min at 4 °C. The aqueous phase was mixed to 0. 6 volume of isopropanol, centrifuged for 10 min at room temperature and the pellet was washed with 500 µL 70% ethanol, centrifuged again and resuspended with nuclease-free water. The DNA was sequenced using the PacBio SMRT technology, resulting in 868 Mb of sequence data (76,825 reads). SMRT reads were filtered using the SMRTanalysis package version 2. 3. 0. We then used Flye 72 version 2. 4. 2 to perform the de novo genome assembly with the pacbio_raw and g = 450,000 parameters.