dataesr/scientific-paragraphs-categorization huggingface dataset:
For Barra et al. 's data set, we used the supplemental Table  S1 , retrieved the updated protein Uniprot Accession entries using Uniprot ID mapper (http://www. uniprot. org/uploadlists/) and proceeded as described for 3611 proteins present in both Barra et al. 's data set and ours. R scripts related to the analysis can be found online ͑https:// github. com/mlocardpaulet/Duguet_TregTconv_2016 and associated scripts and tables͒. Proliferation Analysis by Cell Trace Violet Staining-For proliferation assays, Treg populations (CD4 ϩ CD62L high CD25 bright ) were sorted based on the expression of the endogenous CD25 and CD62L markers to exclude contamination by activated Tconv cells. This popula-tion contains more than 99% of Foxp3ϩ T cells (supplemental Fig. S4 ). Tconv, CD4 ϩ CD62L high CD25 Ϫ naive T cells, were purified from wild type (WT) mice and were cultured in 96-well round-bottomed plates in the presence of CD4 ϩ CD62L high CD25 bright Treg cells purified either from WT mice or Themis1-Tg mice.
Data Analysis-Unless otherwise specified, data are expressed as mean Ϯ s. e. m. The GraphPad Instat statistical package was used for statistical analyses (GraphPad Software, Inc. , La Jolla, CA). Results were compared using Mann-Whitney test. Results were considered statistically significant when the p value were Ͻ0. 05. *: p Ͻ 0. 05; **: p Ͻ 0. 01; ***: p Ͻ 0. 001.
S2A ). The highest number of protein identification per run was obtained with a single run analysis of total cell extracts, using a 50 cm reversed-phase column packed in house and a long LC gradient (480min on a LTQ-Velos Orbitrap) to improve chromatographic separation of peptides. This protocol was thus followed for a total of 27 analytical runs (7 biological replicates and up to 3 technical replicates, supplemental Fig. S2B ) with a gradient length adapted to the sequencing speed of the instrument (i. e. 480 min on the LTQ-Velos Orbitrap and 300min on the Q-Exactive). Resulting raw files were simultaneously processed through MaxQuant, which performed realignment and LFQ normalization of MS intensity signals for all identified proteins. Database search with Andromeda (1% FDR validation at peptide and protein level) led to the identification of 4345 unique protein groups identified in the whole, including 3998 proteins identified with a minimum of two unique peptides.
Among these, we retained for further statistical analysis 3974 proteins which were quantified in a minimum of 3 independent experiments in the Tconv or the Treg samples. This data set was controlled for high reproducibility, with Pearson correlation coefficients on log2(LFQ) protein intensities typically in the range 0. 92-0. 98 for runs performed on different samples of the same cellular type and CVs calculated across biological replicates lower than 10% for 99% of the proteins (median of CVs around 2%), showing good reproducibility of the label-free measurement (supplemental Fig. S6 ). An outline of the data processing steps and statistical analysis is shown in supplemental Fig. S3. The final list of quantified proteins, along with their statistics in the Treg/Tconv comparison, is shown in supplemental Table  S1 , and the entire data set including raw MS files and MaxQuant output files has been deposited to the Proteome-Xchange Consortium (17) via the PRIDE partner repository with the data set identifier PXD004436.
To avoid selection biases because of the different statistical methods used in each case, we first applied a simple selection criteria based on a 2-fold change between the Treg/Tconv subsets, and retrieved the proteins or mRNA quantified as variant in at least 2 of the 3 considered studies, i. e. this study (7 biological replicates), Barra et al. 's proteomic data set (4 biological replicates), and the transcriptomic data set (3 biological replicates). This first selection procedure retrieved 138 gene products showing consistent differences between Treg and Tconv in a minimum of 2 independant studies (supplemental Table  S3 ). The top 50% of these proteins/genes, based on their mean log2-transformed fold change across all studies, are shown in Fig. 4. Depending on the data processing method and statistics applied in each study, proteins/genes were reported or not as differentially expressed, as indicated in Fig.
4. A panel of 30 genes were consistently found significantly regulated in all 3 data sets (supplemental Table  S3 ), both at mRNA and protein level (the later confirmed by both proteomic studies), representing highly reliable Treg/Tconv markers. These include FOXP3, Niban/FAM129A, GSTO-1 and Coronin-2A among the most overexpressed hits in the Treg, and Themis1, ITK, SATB1 and TCF7 among the most underexpressed hits in Treg. Fourteen proteins found significantly regulated in Barra et al. 's data set and in the transcriptomic study were not detected (such as IL-2RB, CD86) or did not pass the statistical criteria (SWAP-70) in our analysis. Conversely, a subset of 24 proteins were reported as significantly regulated in our study and the transcriptomic data set, but not in the data set of Barra et al.
Interestingly, our statistical analysis of 7 independent replicates allowed the identification of 23 additional differentially regulated proteins (corresponding to genes also regulated at mRNA level) that were not significantly regulated in Barra et al. data set. Some were identified with no quantity ratio reported in their study (like the Interferon regulatory factor 4/Irf4 or the Folate receptor 4/Folr4), whereas others were not identified at all (such as OX40/Tnfrsf4, Ikzf4/Eos) or didn't pass the p value criteria to be reported as regulated (such as Helios/IkZF2, Annexin A4/Anxa4 or Lysocardiolipin acyltransferase 1/Lclat1). It is worth noticing that a panel of proteins were consistently characterized as differentially expressed in the two proteomic studies, whereas not being previously reported in the typical Treg signature established based on previous microarray analysis  (15) , such as RLTPR, Nuclear factor NF-kappa-B p100 (Nfkb2) or c-Rel.
On the other hand, our proteomic data showed that the endogenous expression of Themis1 is maintained at low level in Treg compared with Tconv. This would be consistent with a recent report showing that TCR signals are naturally tuned down in Treg cells  (30). In the later study, the authors demonstrated that activation of several pathways and signaling nodes are broadly dampened upon TCR-mediated activation in Treg compared with Tconv. In an attempt to determine the origin of this attenuated signaling, they considered as possible candidates some inhibitory molecules known to be overexpressed in Treg cells, such as the T-cell surface glycopro-tein CD5, CTLA-4, Programmed cell death protein 1/Pdcd1 (also known as PD-1) or the Dual specificity protein phosphatase/Dusp4, but could not confirm their role in the negative regulation of TCR signaling in Treg cells.
Acknowledgments-We thank  Drs. Guillaume Gaud ,  Daniel Dunia ,  Anne Dejean  for their valuable inputs on the manuscript. Mr. Mehdi Benamar  for his technical help. We acknowledge the technical assistance provided by the people from the  Toulouse Proteomics Infrastructure ;  Fatima-Ezzahra L'Faqihi-Olive ,  Valerie Duplan-Eche , and  Anne-Laure Iscache  from the  CPTP Cytometry facility ;  Florence Capilla  and  Christine Salon  from  Inserm   US006  hitstopathological platform, and the personnel of  Inserm   US006  Anexplo/Crefre animal facility.
* This work was supported in part by the  Region Midi-Pyrenees, European funds (Fonds Europeens de Developpement Regional, FEDER) ,  Toulouse Me  ´tropole, the  French Ministry of Research  with the  Investissement d'Avenir Infrastructures Nationales en Biologie et Santeprogram (ProFI ,  Proteomics French Infrastructure  project,  ANR-10-INBS-08 ),  INSERM ,  Association Franc  ¸aise contre les Myopathies. FD was supported by grants  Region Midi-Pyre  ´nees and ARSEP. AS and AG are supported by  Centre National de la Recherche Scientifique. ʈ These authors contributed equally to this work. □ S This article contains supplemental material.
paleacea rather than M. polymorpha  (plastid, GenBank LC035012. 1; [Kisiel et al. , 2011] ). Organellar genome organization is identical, and previous evolutionary and functional conclusions are not compromised. Gene Numbers Annotation revealed 19,138 nuclear encoded protein-coding genes, with 5,385 alternative protein-coding transcripts, (> 90%) having EST support (Table  S1 ). To facilitate annotation, we obtained transcriptomes (see Table  S2 , STAR Methods, and Mendeley https://doi. org/10. 17632/zb7hwyj3hp. 1) representing various tissues of axenically grown plants, and from two ''wild'' samples in which gene expression was detected for some genes without evidence of expression in axenically grown plants. With the arbitrary level R 10-fold higher cut-off, sporophytic and antheridiophore (male reproductive) tissues possess more specialized transcriptomes than either sporeling or archegoniophore (female reproductive) tissues.
5B pairs of paired-end Illumina RNA-seq reads, 24,803 transcript assemblies from $0. 35B single-end Illumina RNA-seq reads (derived from XVE:miR-E(z) lines  (Flores-Sandoval et al. , 2016)  and ectopic MpKNOX2 and MpBELL expression lines (Tom Dierschke and J. L. B. , unpublished data) to enrich for sporophyte expressed genes), 19,438 transcript assemblies from $3M 454 reads (Table  S1 ). All these transcript assemblies from RNA-seq reads were made using PERTRAN (S. Shu, unpublished data). 43,727 transcript assemblies were constructed using PASA  (Haas et al. , 2003)  from 33,863 Sanger ESTs  (Nagai et al. , 1999; Nishiyama et al. , 2000; Yamato et al. , 2007)  and all RNA-seq transcript assemblies above. Loci were determined by transcript assembly alignments and/or EXONERATE alignments of proteins from Arabidopsis thaliana, soybean, sorghum, rice, mimulus, grape, Chlamydomonas reinhardtii, Sphagnum fallax, Physcomitrella patens and Klebsormidium nitens, Swiss-Prot proteomes, and Pfam and Panther filtered ORFs from transcriptome assemblies of 4 Charophycean algae (Nitella, Spirogyra, Coleochaete, and Mesostigma) to repeat-soft-masked Marchantia polymorpha genome using RepeatMasker  (Smit et al.
PASA-improved gene model proteins were subject to protein homology analysis to above-mentioned proteomes to obtain C-score and protein coverage. C-score is a protein BLASTP score ratio to MBH (mutual best hit) BLASTP score and protein coverage is highest percentage of protein aligned to the best of homologs. PASA-improved transcripts were selected based on C-score, protein coverage, EST coverage, and its CDS overlapping with repeats. The transcripts were selected if its C-score is larger than or equal to 0. 5 and protein coverage larger than or equal to 0. 5, or it has EST coverage, but its CDS overlapping with repeats is less than 20%. For gene models, whose CDS overlaps with repeats for more than 20%, its C-score must be at least 0. 9 and homology coverage at least 70% to be selected. The selected gene models were subjected to Pfam analysis and gene models whose protein was more than 30% in Pfam TE domains were removed.
, 2013)  and mapped reads were processed through Cuffdiff  (Trapnell et al. , 2013). Sporeling FPKM normalized read counts were then loaded onto the FPKM matrix as sporeling datasets and enrichment analysis for sporelings was carried out by obtaining the ratio between the maximum value of any sporeling dataset over the value of any other tissue (see Table  M1  at Mendeley https://doi. org/10. 17632/zb7hwyj3hp. 1). Genes were defined as enriched in sporeling tissue if the ratio was > 10. Also, genes having infinity ratios (where all other tissues had 0 FPKM values) were assigned as enriched if the maximum expression value of the sporeling datasets was > 0. 3 FPKM, as to avoid extremely low counts from being assigned as enriched. This approach produced 366 genes enriched in sporeling tissue, which can be found in the supplementary enriched genes table.
Codon Analysis METHODOLOGY: Analyses of indices of codon usage across the transcriptome dataset were performed using codonw 1. 4. 4 software (J. Peden, http://codonw. sourceforge. net). Correlation analysis was carried out using a Spearman's rank correlation analysis tool (P. Wessa, Free Statistics Software, Office for Research Development and Education, version 1. 1. 23-r7, https://www. wessa. net/).
Identification of Upstream ORFs The full transcript sequences, including splice variants, for A. trichopoda, A. thaliana, M. polymorpha, P. patens and S. moellendorffii were obtained from Phytozome v11. 0, TAIR 10 and ENSEMBLE databases. We predicted all ORFs in the forward strand for each sequence with EMBOSS utility ''getorf,'' detecting the longest ORF as the coding gene and filtering the 5 0 upstream uORFs if the size in aminoacids were > 9 using custom Perl scripts (available upon request), all uORFs must have an AUG and a stop codon within the 5 0 UTR. Information for initial and end positions, uORF size and uORF distance to ORF was recorded in text tables (see Table  S1 ). The minimum size was set to 10 amino acids, including the start and stop codon, this consideration was taken due to the experimental evidence from functional uORFs.
Whole and Segmental Genome Duplication Analysis The transcript with the longest ORF among transcriptional variants was collected from each gene. Paralog pairs were selected as reciprocal best hits by BLAST. The amino acid sequences of the pairs were aligned by  MUSCLE (v3. 8. 31; Edgar, 2004) , and their Ks and 4dTv values were calculated by the KaKs_Calculator2. 0 [Table  S4 ,  (Wang et al. , 2010) ]. Dot plot figures generated using BLASTZ  (Schwartz et al. , 2003)  and DAGchainer  (Haas et al. , 2004)  included in SynMap at CoGe website  (Lyons et al. , 2008).
repeatmasker. org/) and Tandem Repeats Finder (TRF) to build, refine, and classify consensus models of putative interspersed repeats  (Lerat, 2010). Long Terminal Retrotransposons (LTRs) were identified using the LTRharvest  (Ellinghaus et al. , 2008)  LTRdigest  (Steinbiss et al. , 2009)  pipeline. LTR retrotransposons were subjected to computational identification and manual curation to identify LTRs containing particular arrays of protein domains characteristics of retrotransposon proteins using HMMER (http://hmmer. org/) and Pfam (http://pfam. xfam. org/) database for domain search. Assignment of superfamilies was done based on the array of the Integrase (INT), and Reverse Transcriptase (RT) domains. Filtered LTRs were classified into Copia (GAG AP INT RT RH) and Gypsy (GAG AP RT RH INT) families.
This way we identified 264 full-length LTRs. Consensus libraries derived from both pipelines were employed to determine the repetitive content in the M. polymorpha genome using Repeatmasker and Repeatlandscape (https://github. com/caballero/RepeatLandscape; http://www. repeatmasker. org/genomicDatasets/ RMGenomicDatasets. html). Sex Chromosome Analysis X chromosome scaffolds were identified by aligning Kitashirakawa-2 (Kit-2) and Tak-1 reads to the genome assembly. The Kit-2 (female) and Tak-1 (male) varieties were selected to be of different sexes and X chromosome regions were identified as regions where the Tak-1 depth was $0 while the Kit-2 depth was unchanged. A total of 9 scaffolds, two of which (17, 18) are large, representing 4. 37 Mb of sequence were identified as X chromosome related (Table  S6 ).
Consistently, all available X-linked markers  (Fujisawa et al. , 2001; Yamato et al. , 2007)  were mapped onto the scaffolds. Since the sex chromosomes are diverged not only in sequence but also in function, the expression profiles of the sex chromosomal genes were examined. RNA-seq data from vegetative and reproductive tissues from each sex (see Table  S6 ) were mapped to the genomic sequence by tophat ver. 2. 1. 0, and gene expression profiles were estimated by cuffdiff ver. 2. 2. 1  (Trapnell et al. , 2012). A given gene is ''reproductive,'' when its expression level (FPKM/RPKM) is larger than 0. 1 in reproductive tissues, antheridiophore or antheridium in Tak-1 (male) and archegoniophore, archegonium or sporophyte in Tak-2 (female), and its vegetative to reproductive expression ratio is larger than 10.
Phylogenetic Methods Protein and/or transcript sequences were collected using the Marchantia genome portal site (http://marchantia. info/genome/index. php/Main_Page) based on the sequence similarity, or from the GenBank databases at NCBI. In some case, domains were evaluated using NCBI Conserved Domain Database (https://www. ncbi. nlm. nih. gov/Structure/cdd/wrpsb. cgi) or PROSITE (http://prosite. expasy. org). Multiple sequence alignments were performed using the MUSCLE program  (Edgar, 2004)  contained in the Geneious ver. 7. 1. 9 package (https://www. geneious. com) with the default settings. Alternatively, amino acid sequences were manually aligned using Se-Alv2. 0a11. After removing alignment gaps manually or using the Strip Alignment Columns routine in the Geneious package, phylogenetic analyses were performed using RAxML ver.
8. 2. 8  (Stamatakis, 2014)  or MrBayes ver. 3. 2. 6 (Ronquist et al. , 2012)  programs. Alignments used to generate the phylogenetic trees are available upon request. Predicted protein sequences obtained from the sequenced genomes of land plants, charophytes, and Chlorophytes and from the transcriptomes of charophytes listed below. In some cases for which greater resolution with the charophycean algal grade was desired, additional algal sequences were interrogated  (Cooper and Delwiche, 2016). Finally, sequences in GenBank from additional algal or land plant taxa, as detailed in figure legends, were utilized for the examination of select gene families (Table  S11 ).
ACKNOWLEDGMENTS The work conducted by the  U. S. Department of Energy Joint Genome Institute  is supported by the  Office of Science of the U. S. Department of Energy  under Contract No. DE-AC02-05CH11231. Funding from  ARC   FF0561326 ,  DP130100177 ,  DP160100892  (J. L. B),  MEXT   KAKENHI Grant Numbers  ( 15K21758 ,  16H06279 ,  25113001 ,  25113009 [T. Kohchi], 22112514 ,  24112715 ,  24510272 [K. T. Y. ], 15H04391 ,  15H01233 ,  17H06472  [ K. Ishizaki ],  FWF  grant  P28320-B21  (F. B. ),  SNF  grant  310030B_160336  (U. G. ),  Gates Cambridge Trust  (C. R. B. ), Program to Disseminate Tenure Tracking System, MEXT, Japan (Y. I. ), the  Marsden Fund of New Zealand  grant  PAF1302  (K. D. ),  UC MEXUS Collaborative program  grant  2011-UCMEXUS-19941-44-OAC7  (M. A. A. -V, X. C. , and E.
jgi. doe. gov/pz/portal. html#!info?alias=Org_Mpolymorpha charophyte and Chlorophyte transcriptomes (Cooper and Delwiche, 2016) https://figshare. com/articles/Green_algal_ transcriptomes_for_phylogenetics_and_ comparative_genomics/1604778 NCBI Conserved Domain Database (Marchler-Bauer et al. , 2015) https://www. ncbi. nlm. nih. gov/Structure/cdd/ wrpsb. cgi PROSITE (Sigrist et al. , 2013) http://prosite. expasy. org Carbohydrate-Active enZYmes Database (Lombard et al. , 2014) http://www. cazy. org/ Software and Algorithms Newbler (v2. 6) (Margulies et al. , 2005) N/A Arachne v. 20071016 (Jaffe et al. , 2003) https://github. com/cseed/arachne-pnr Burrows-Wheeler Alignment tool (BWA) ( Li and Durbin, 2009) http://bio-bwa. sourceforge. net/ (Sequence Alignment/Map) SAMtools (Li et al. , 2009) http://samtools.
sourceforge. net/ (Continued on next page) Cell 171, 287-304. e1-e10, October 5, 2017 e1 Continued REAGENT or RESOURCE SOURCE IDENTIFIER Gepard (Krumsiek et al. , 2007) http://mips. gsf. de/services/analysis/gepard BLAT -the BLAST-like alignment tool (Kent, 2002) https://www. soe. ucsc. edu/?kent MIRA ver. 3. 2. 0 (Chevreux et al. , 1999) https://sourceforge. net/projects/ mira-assembler/ PERTRAN (S. Shu, unpublished data) N/A Program to Assemble Spliced Alignments (PASA) (Haas et al. , 2003) https://pasapipeline. github. io/ RepeatMasker (Smit et al. , 2013-2015) http://www. repeatmasker. org/ RepeatModeler (Smit and Hubley, 2008-2015) http://www. repeatmasker. org/ RepeatModeler. html FGENESH+ (Salamov and Solovyev, 2000) N/A GenomeScan (Yeh et al. , 2001) http://genes. mit. edu/genomescan/ BRAKER1 (Hoff et al.
DATA AND SOFTWARE AVAILABILITY Genomic resources are available from web sites of both JGI (https://phytozome. jgi. doe. gov/pz/portal. html#!info? alias=Org_Mpolymorpha_er) and the Marchantia user community (http://marchantia. info/). Both sites provide similar services, such as a genome browser, BLAST and keyword searches, but there are some features unique to each. The site of JGI, for example, is a part of Phytozome and thus suitable for comparative genomics, and bulk data can be downloaded. On the other hand, the site by the user community is more oriented to supporting researchers who are using M. polymorpha. Its genome browser provides separate Supplemental Figures  (B) Searches for charophyte AUX/IAA-like sequences identified genes with IAA-like PB1 domains, but that were not associated with B3 domains.
In contrast, convincing YUCCA orthologs were not identifed in available charophycean algal databases -the origin of land plant YUC genes may have been via horizontal gene transfer from a microbe. The auxin receptor, TIR1, has its origin in a gene duplication followed by neofunctionalization into TIR1 and COI proteins that possess the ability to interact with auxin and AUX/IAA proteins (TIR1) or a jasmonate-related ligand and JAZ proteins (COI). Neofunctionalization of an AUX/IAA precursor via addition of domain II enabled the evolution of an AUX/IAA-TIR complex that interacts in the presence of auxin, thus assembling the auxin transcriptional signaling system. Acquisition of domain I by AUX/IAA genes enabled the repressive capability of AUX/IAA proteins. ARF transcription factors predated the evolution of the auxin-signaling system, suggesting they could retain ancestral functions independent from auxin.
The last four training sessions were used to establish consensus amongst the panellists with regard to the attributes and the wines' ratings on a 10 cm line scale anchored at none on the left-hand side and intense on the right-hand side. Although a 10 cm line scale was used, the results of the data analysis are expressed out of 100. The wines were evaluated by the panellists in triplicate on the same day. Panellists were forced to take breaks longer than 10 minutes between replicates. The sensory evaluation was conducted in off-white tasting booths located in a sensory laboratory. The sensory laboratory was quiet, odour free and had controlled air conditioning (20 ± 2°C). Twenty-five millilitres of each wine sample were presented in black ISO tasting glasses covered with petri dishes coded with pseudo random three-digit codes generated by an online research randomiser.
In order to limit biases, each panellist received the samples in a different order according to a balanced Williams Latin square design, computed using the design of experiments (DOE) function of the MX package of XLSTAT (Microsoft, Redmond, WA, USA). Data was captured on paper ballots, line scales were measured with rulers and data was captured using Microsoft Excel 2010 (Microsoft, Redmond, WA, USA). Panel performance was assessed using the workflow suggested by  Tomic et al. (2010). Repeatability (p*MSE plots and F-plots), discriminability and panel consensus (Tucker-1) were tested using PanelCheck 1. 4 (Nofima Mat, Tromsø, Norway). DA data was analysed by means of twoway ANOVA with panellists and wine samples as main effects. Wine sample effect was tested by the regular F test to identify significant (p < 0.
b Standards were presented fresh in amber glass bottles covered with foil. c All fresh products were purchased from local grocers, Checkers, Spar or Woolworths. Statistica 10 (Statsoft Inc. , www. statsoft. com), followed by a post-hoc Fisher's least significant difference (LSD) analysis, to determine significant differences in the chemical and sensory analyses (p < 0. 05). Principal component analysis (PCA) was performed with the same software for sensory data analysis.