dataesr/scientific-paragraphs-categorization huggingface dataset:
Hierarchical clustering was performed using hclust as part of the pheatmap package (v1. 0. 12) in R with correlation distance and average linkage. To identify and decide upon the number of lncRNA and mRNA clusters, the dendrograms were visually inspected using different cut-offs on the cutree_rows and cutree_cols functions of the pheatmap package. Cut-offs were manually selected to define the clusters depicted in Fig. 2a  (cutree_rows = 3 and cutree_cols = 3). Gene Set Enrichment analysis (GSEA). Gene Set Enrichment Analysis was carried out using either the 50 Hallmark pathway gene sets  60  (h. all. v7. 0. symbols. gmt), or "C5", ontology gene sets  61, 62  (c5. all. v7. 0. symbols. gmt) from MSigDB  63. Enrichment was calculated using hypergeometric test (the R function phyper) of the mRNAs in each cluster, against all genes in a gene set.
The average expression of the set of standard genes in a sample reflected lymphocyte infiltration. The xCell algorithm  28  was used to obtain a fibroblast score for SCAN-B samples with log2 (TPM + 1) values as input. For TCGA, xCell scores were downloaded from https://xcell. ucsf. edu/xCell_TCGA_RSEM. txt. lncRNA expression modeled with generalized linear models. Generalized linear modeling (glm function in R) was used to model lncRNA expression as a function of ESR1 mRNA expression, fibroblast infiltration, and lymphocyte infiltration to estimate which variable(s) explained most each lncRNA expression. Resulting coefficient of such modelling are used in subsequent analysis to estimate the impact of each variable in lncRNA clusters. RNA-seq from breast cancer cell lines. Gene expression from cell lines representing different breast cancer molecular subtypes: MCF7 and ZR751 (luminal A), MB361 and UACC812 (luminal B), AU565 and SKBR3 (HER2), MB469 and HCC1937 (basal), MB231 and MB436 (Claudin-low), and MCF10A and 76NF2V (Normal breast), each 4 replicates (GSE96860 29 ,) was obtained from the Recount3 project 65 (v 1.
2. 6) using the recount::getTPM function in R. 911 of the 919 lncRNAs defined in the clustering analysis (Fig. 2a ) were available and used to identify differentially expressed lncRNAs in each subtype compared to all other subtypes (wilcox test) using the FindAllMarkers function of the Seurat package (v4. 1. 0) in R. Single cell RNA-seq from breast cancer patients. Count matrix of single cell RNA-seq  30  were analyzed using the Seurat package (v3. 2. 1) in R to obtain UMAP. In brief, count matrix were already filtered for dying cells by the authors. It was further normalized and scaled regressing out potential confounding factors (number of UMIs, number of gene detected in cell. percentage of mitochondrial RNA). After scaling, variably expressed genes were used to construct principal components (PCs) and PCs covering the highest variance in the dataset were selected based on elbow and Jackstraw plots to build the UMAP.
Clusters were calculated by the FindClusters function with a resolution between 0. 8 and 1. 8, and visualized using the UMAP dimensional reduction method. Nine main cell types were identified on these UMAP based on the authors annotations. The main cell types identified are normal epithelial, cancer epithelial, myeloid, T, B, endothelial cells, plasmablasts, CAF and perivascular-like -fibroblasts. lncRNA promoter annotation. lncRNA promoters were defined as Transcription Start Site (transcription_start_site), positions obtained from Ensembl (v. 93) using BioMart 66 (biomaRt_2. 45. 6, host = 'http://Jul2018. archive. ensembl. org') -200 bp (upstream of TSS) and +100 bp downstream, and by increasing the upstream window to -300, -500, and -1000. lncRNA transcripts with independent promoters were obtained using bedtools subtract, with the -A flag, of all lncRNA promoters from a background file containing a window spanning 200 bp (300 bp, 500 bp, and 1000 bp for expanding window sizes) upstream of protein coding gene start positions, to gene end position (BEDtools 67 , v2.
29. 2), remaining transcripts were regarded as overlapping promoters. Overlapping protein coding genes were identified using the bedtools intersect command with the same input as described above, and nearest protein coding gene to independent lncRNAs were identified by bedtools nearest using the default parameters with lncRNA promoter regions (-200/ + 100) and protein coding genes start-stop coordinates. ATAC-seq data from TCGA-BRCA. Normalized ATAC-seq peak signals (log2((count + 5)PM)-qn) for 74 TCGA breast tumors  68  were downloaded from the Xena browser  54  (https://xenabrowser. net/datapages/). lncRNA promoter positions (-200/ + 100) were intersected with the peak positions using bedtools intersect. To test for differential open regions between ER positive and negative tumors, the average normalized counts of the peaks containing lncRNA promoters were calculated per tumor sample and a Wilcoxon rank-sum test was applied to test for statistical significance using R.
e. TFBSs) stored in the updated version of the UniBind database as of 20. 10. 2020  32. These TFBSs were obtained by combining both experimental (through ChIP-seq) and computational (through position weight matrices from JASPAR  69  ) evidence of direct TF-DNA interactions (see ref. 32  for more details). Note that a TF can have multiple sets of TFBSs derived from different ChIP-seq experiments. The enrichment of UniBind TFBS sets in regions surrounding lncRNA promoters against a universe considering all lncRNA promoter regions (window sizes as described above, Ensembl. 93) with the UniBind enrichment tool (https://unibind. uio. no/ enrichment/, source code available at https://bitbucket. org/CBGR/unibind_ enrichment/; input R data with TFBS information available on zenodo at https:// doi.
org/10. 5281/zenodo. 4452896). Specifically, the enrichment is computed using the LOLA R package (version 1. 12. 0)  70  DNA methylation lncRNA expression correlation analysis. Within each data set (OSLO2 and TCGA), CpGs with an interquartile range (IQR) > 0. 1 were selected. Considering only CpGs and lncRNAs present in both data sets resulted in 143 631 CpGs and 1027 lncRNAs, and analysis was restricted to lncRNAs and CpGs on the same chromosome (total number of tests n = 7130824). To test the correlation between the level of DNA methylation of CpGs and lncRNA expression (log2 expression (OSLO2) or log2 (TPM + 1) (TCGA)), the Spearman correlation statistics was applied (function cor. test with method = "spearman" in R). An association was considered statistically significant if a Bonferroni-corrected p-value was <0.
05. Only significant correlations with the same direction (sign) were kept. We assessed enrichment of all CpGs with negative correlation to lncRNA expression to each of the 13 chromatin states described above using hypergeometric tests (the R function phyper) with all Illumina Infinium HumanMethylation450 BeadChip CpGs as background (n = 436 506). P-values were corrected using the Benjamini-Hochberg (BH) procedure  59. ChIA-PET Pol2 data and ChIP-seq peaks. ChIA-PET Pol2 loop data from the MCF7 cell line was retrieved from ENCODE, accession number ENCSR000CAA  71. We investigated overlaps between ChIA-PET Pol2 loops and CpGs with negative correlation to lncRNA expression (Supplementary Data 6). A CpG-lncRNA pair was considered to be in a ChIA-PET loop if the CpG and the lncRNA TSS were found in two different feet of the same loop.
lncRNA TSS positions were lifted to hg19 coordinates using the UCSC liftOver tool before intersecting with the loop coordinates using BEDtools intersect. Similarly, lncRNA-mRNA pairs were considered to be in a loop if the lncRNA (gene body coordinates) and mRNA (gene body coordinates) were found in two different feet of the same loop. For the specific analyses of MCF7 TF ChIP-seq data sets, we retrieved ENCODE ChIP-seq peak regions from the ReMap 2018 72 database (ENCSR000BST. GATA3. MCF7, ERP000783. ESR1. MCF7, and GSE72249. FOXA1. MCF7).
Acknowledgements S. B. and M. R. A. are postdoctoral fellow funded by the  Norwegian cancer Society  (grant nr  190372-2017 ). The authors would like to acknowledge patients, clinicians, and hospital staff participating in the  SCAN-B study , the staff at the central  SCAN-B laboratory at Division of Oncology, Lund University , the Swedish national breast cancer quality registry (NKBC),  Regional Cancer Center South , and the  South Swedish Breast Cancer Group (SSBCG). This work was supported by the  Mrs. Berta Kamprad Foundation , the  Mats Paulsson Foundation , the  Biltema Foundation , the  Swedish Cancer Foundation, Governmental Funding of Clinical Research within National Health Service.
Using ParticleGrid • ParticleGrid can be installed via pip • Low dependency overhead: • C++17 enabled compiler • NumPy • Smooth integration with deep learning frameworks • Zero-copy transfers to data containers • Integrates into existing data pipelines Using ParticleGrid • The grid generation function coord_to_grid takes as input: • A set of of coordinates to transform • The dimensions of the extent (bounding box): • Width • Height • Depth • The size of the grid • The variance (amount of spread) Using ParticleGrid • Returns a NumPy array  • The public repo is located at: https://github. com/ParticleGrid/ParticleGrid.
Data Processing for Protein Identification and Quantification Raw files were searched using MaxQuant software (32), version 1. 5. 3. 8 and 1. 6. 6. 0 (for in-gel digestion) with Andromeda as a search engine  (33)  against an internal C. difficile database containing 3957 proteins  (26) , common known MS contaminants, and reversed sequences of all entries. Andromeda searches were performed choosing trypsin as specific enzyme with a maximum number of three missed cleavages. Possible modifications included carbamidomethylation (Cys, fixed), oxidation (Met, variable), Nter acetylation (variable), and phosphorylation (S, T, Y, variable). The mass tolerance in MS was set to 20 ppm for the first search, then 4. 5 ppm for the main search, and 20 ppm for the MS/MS. Maximum peptide charge was set to seven, and seven amino acids were required as minimum peptide length.
Experimental Design and Statistical Analysis of the Proteome and the Phosphoproteome C. difficile strains were cultured at 37 • C in TY for 16 h. Quantitative proteomes and phosphoproteomes were acquired from five biological replicates of five C. difficile strains, the 630Δerm strain (WT) used as a control, and the ΔprkC, CD2148::erm, Δstp, and double ΔprkC CD2148::erm mutant strains. Only phosphopeptides with a localization probability (LP) of their phosphorylation sites superior to 0. 75 in at least one replicate were kept to compare their abundances between biological conditions  (34). Intensities of proteins and phosphopeptides were normalized by condition using a median-centering function from DAPAR R package  (35) , and their missing values were imputed using the impute. mle function of the.
Clostridioides difficile Phosphoproteome Mol Cell Proteomics (2022) 21(11) 100428 3 imp4p R package  (36). This algorithm imputes values in a condition only when an intensity value has been quantified in at least one of the samples of the considered condition. The quantification profiles (modified peptide and parent unmodified protein quantified/not quantified in at least one sample of a condition) can be viewed in the "Absent/Present" columns of supplemental Table  S4. For the differential analysis of one condition versus the WT strain, two statistical tests were used. First, a moderated t test was performed; thanks to the limma R package  (37) , to determine whether a phosphorylated peptide is significantly differentially abundant between both conditions. Moreover, phosphorylated peptides quantified in one condition and not in the other were also considered differentially abundant.
Another statistical test was performed in a second step: a contrasted t test was performed to compare the variation in abundance of each modified peptide to the one of its parent unmodified protein using the limma R package  (37). This second test allows determining whether the fold change of a phosphorylated peptide between two conditions is significantly different from the one of its parent and unmodified protein (paragraph 3. 9 in Ref. (38) ). An adaptive Benjamini-Hochberg procedure was applied on the resulting p values; thanks to the adjust. p function of R package cp4p  (39) , and an FDR threshold of 1% was used to select peptides that evolve differently from their parent protein. Note that this second test can be performed only when there are quantified intensity values for both the nonmodified peptide and its parent protein.
Analysis of Protein Functions Proteins were classified into functional categories according to their annotated functions in the GeneBank database, publications, and by homology/functions according to the Gene Ontology  (45) , the Conserved Domain (https://www. ncbi. nlm. nih. gov/Structure/cdd/cdd. shtml), and the Kyoto Encyclopedia of Genes and Genomes Pathway (http://www. genome. jp/kegg-bin/show_organism?org_sco) databases. Enrichments of Gene Ontology terms, DAVID keywords, and Kyoto Encyclopedia of Genes and Genomes pathways were performed using hypergeometric tests; thanks to DAVID software  (46). The set of all identified proteins related to phosphosites was used as background for the statistical tests. A p value threshold of 1% was used to select enriched terms/keywords/pathways.
Phase Contrast and SNAP Labeling Microscopy For phase contrast microscopy, C. difficile strains were cultured for 5 h in TY (with antibiotics and inducers when needed) at 37 • C. Cells were visualized using Zeiss Axioskop microscope and analyzed using the ImageJ software. For SNAP labeling, strains were grown 3 h in TY. The expression of the SNAP Cd -PrkC, SNAP Cd -PrkCΔSGN, SNAP Cd -PrkCΔext, and FtsK-SNAP Cd fusions was induced with 50 ng/ml of ATc for 2 h. The TMR-Star substrate (New England Biolabs) was added at 250 nM, and the mixture was incubated for 30 min in the dark under anaerobiosis. Cells were then collected by centrifugation, washed, and resuspended in PBS. Cell suspension (3 μl) was mounted on 1. 7% agarose-coated glass slides. The images were taken with exposure times of 600 ms for autofluorescence and 800 ms for SNAP using a Nikon Eclipse TI-E microscope (100× objective) and captured with a CoolSNAP HQ2 Camera.
1B ). The biological functions of the phosphoproteins identified in C. difficile were classified based on their Gene Ontology terms. Most identified phosphoproteins were assigned to the cytoplasm, but 24% was also associated with the cytoplasmic membrane (Fig. 1C , top). As observed in another phosphoproteome of C. difficile  (22) , many phosphorylated peptides belong to proteins involved in translation, stress response including chaperones, cell division, envelope homeostasis, and carbon metabolism including enzymes of glycolysis and transporters belonging to the phosphotransferase system (PTS) (Fig. 1C , bottom). Known kinase substrates were also identified: (i) the anti-anti sigma factor RsbV on S57 as previously observed  (22, 51) , likely by the antisigma and kinase factor, RsbW, which was also phosphorylated on S89; (ii) the HPr protein of the PTS on S45 in agreement with data obtained in vivo  (22) , and in vitro using purified HPr and HPr-kinase proteins  (52)  and on S11 as found in B.
S1, B  and C ). The data were acquired from five biological replicates of five C. difficle strains. Phosphopeptides were enriched using our optimized protocol and analyzed via LC-MS/MS leading to a high reproducibility of results (Fig. 4A ). Combining the data obtained for the WT strain and all the mutants, we identified 2493 proteins and 1735 phosphopeptides including 1513 with an LP >0. 75 (Fig. 4B , supplemental Tables  S4  and S5 ). The quantity of a few proteins significantly changes between one of the mutant strains and the WT strain (Fig. 4A   S6 ) suggesting that PrkC and CD2148 play only a minor role in the control of protein production or degradation. However, the number of identified phosphopeptides differs between strains (Fig. 4B  and supplemental Table  S4 ). The potential targets of PrkC/CD2148 and STP should then show opposite behavior.
About 124 and 44 phosphopeptides were specific for PrkC and CD2148, respectively, and 76 phosphopeptides were significantly less abundant in the three mutants (Figs. 5A and supplemental Fig. S4E ). The two kinases could function together for some targets in agreement with the involvement of PrkC to detect an efficient phosphorylation of CD2148 in vitro. Interestingly, we also observed that a few candidate targets specific of PrkC were more phosphorylated in the CD2148 mutant as observed for CwlA (supplemental Table  S8 )  (27). Among the peptides differentially phosphorylated in the STK mutants compared with the WT strain, we searched for conserved sequence motifs using ggseqlogo R package  (40). Although no specific signatures were detected, an enrichment for S was observed among the sites phosphorylated under the control of CD2148 (Fig.
The ggseqlogo R package was used. LP, localization probability; pT, phosphothreonine; pS, phosphoserine; STK, serine/ threonine kinase; STP, serine/threonine phosphatase. CD2148 (Table  1 ). These sites were more phosphorylated in the Δstp mutant (Fig. 6 ). This suggests that STKs control this pathway that seems to be important for colonization  (69).
After exposure of the strains to 50 • C for 15 min, the survival of the ΔprkC mutant was five fold reduced compared with the WT strain (data not shown). This result suggested that PrkC plays a role in the heat shock response. However, the protein(s) targeted directly or indirectly by this PASTA-STK responsible for this phenotype remain(s) to be identified. Three proteins (CD1634, CD1636, and CD1639) likely involved in tellurite resistance were also targets of phosphorylation under the control of STKs. We then tested the sensitivity of the different mutants to tellurite using a disk diffusion assay. We did not observe any difference in the growth inhibition area for the WT strain and the Δstp mutant. By contrast, a significant increased sensitivity to tellurite was observed for the ΔprkC, CD2148, and double mutants (supplemental Fig.
difficile physiology. The phosphoproteomic data suggest a broader role for PrkC with a more limited and   S3 ). B, Phos-Tag and non-Phos-Tag SDS-PAGE with the whole-cell lysates of different strains expressing ftsK-HA or ftsK T318A -HA under the control of the P tet promoter. The FtsK-HA protein was detected using an anti-HA-tag antibody. C, FtsK-SNAP localization. The expression of the P tet ftsK-SNAP fusion carried by a plasmid introduced into the WT strain was induced 2 h in the presence of 50 ng/ml of ATc. partially overlapping role for CD2148. Both kinases target directly or indirectly pathways required for metabolism, translation, protein folding, and stress response. By contrast, proteins involved in cell division and PG metabolism (Fig. 9 ) seem to be mainly phosphorylated under the control of the PASTA kinase, PrkC, as observed in most firmicutes (6).
DATA AVAILABILITY The MS proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral. proteomexchange. org) via the PRIDE partner repository with the dataset identifier PXD029827  (100). The list of phosphopeptides and the spectra for the optimization step can be viewed on this link: https://msviewer. ucsf. edu/ prospector/cgi-bin/mssearch. cgi?report_title=MS-Viewer& search_key=bymwsuikwx&search_name=msviewer. The global phosphoproteome data from the analysis of the different strains have also been loaded into MSviewer at this link: https://msviewer. ucsf. edu/prospector/cgi-bin/ mssearch. cgi?report_title=MS-Viewer&search_key=lhik1ga8 x8&search_name=msviewer. Spo0A phosphosites described in the article can be viewed on this link: https://msviewer. ucsf. edu/prospector/cgi-bin/mssearch.
Acknowledgments -We acknowledge  Paul J. Hensbergen  for the fruitful advice and communicating results prior to publication and to  Anne Galinier ,  Pascale Pescher , and  Bruno Dupuy  for helpful discussions. This work was funded by the  Institut Pasteur , the  Universit é Paris Cit  é, and grants from the  ITN Marie Curie, Clospore  (grant no. :  H2020-MSCA-ITN-2014 642068 ), and the  ANR DifKin  (grant no. :  ANR-17-CE15-0018-01 ). Funding and additional information -E. C. and T. G. G. are the recipients of an  ITN Marie Curie  and an  ANR  fellowship, respectively.
4G2 at 10mg/ml; mouse and rat IgG at 25mg/ml) and 100nM dasatinib (Sigma-Aldrich). 6x10 6 cells were then incubated for 2 hours at 25°C with 1. 5 ml tetramer in 100 ml RPMI medium. Labelled cells were acquired using an LSRII or a Fortessa flow cytometer (BD Biosciences, San Jose, CA) and the data analysed using FlowJo software (Tree Star, Ashland, OR). Doublets and dead cells were excluded from the analysis by using appropriate FSC/SSC gates.
Processing of TCRseq data Initially, reads were processed with the toolkit pRESTO  (53)  as follows. Using FilterSeq, reads with a quality higher than 20 were selected. Using MaskPrimers and PairSeq algorithms, the sequences corresponding to the Tcra constant region (AGCAGGTTCTGGGTTCTGGA) and indicating location of the UMI (CTTGGGGG) were searched for and indexed to the head of the paired reads. Using BuildConsensus, consensussequences of the reads with the same UMI were constructed. Next, the forward and reverse reads were aligned to assemble the Tcra sequences (AssemblePairs) and the UMI groups containing at least two reads were selected. The sequenced fragments from each selected UMI were aligned to the Tcra genomic region using the toolkit MiXCR  (54) , with the tools "align" and "assemble".
The aligned fragments were exported as data tables "clonotype-tables" using the tool "exportClones". Using VDJtools (55) these clonotype-tables were then processed to graph with customized scripts in R. For all graphics described below, the "clonotypes" were selected according to their differences in the amino-acid sequences of the V segments, CDR3, and J segments. The Chao1 and Shannon-Wiener diversity-measures of the total Tcra repertoires were calculated using the command CalcDiversityStats from VDJtools. The Morisita-Horn similarity measure of clonotypes represented at ≥ 5 UMIs in individual samples was determined using the R package "divo". "Public repertoires" were defined as the clonotypes present in all four replicates for each condition. Custom scripts used are available at https://github.
This work was financially supported by the Fondation pour la Recherche Médicale (to JvM, DEQ20160334920); the IdEx Toulouse (to PR); the Région Midi Pyrénées (to JvM, 15/06/ 12. 05); the Agence Nationale pour la Recherche (to PR, ANR-16-CE15-0015-01), and the Fondation ARC pour la Recherche sur le Cancer (to CA, DOC20180507201). Il2fl knock-in mice were generated by the Centre d'Immunophénomique (CIPHE, Marseille, France). CIPHE is supported by the Investissement d'Avenir program PHENOMIN (French National Infrastructure for Mouse Phenogenomics; ANR-10-INBS-07 to BM).
Acknowledgements We thank  Adrian Liston  and  Pamela Fink  for providing Rag-Gfp Foxp3-Thy1 a mutant mice and the  NIH  tetramer-facility for MOG ( 35-55 )/I-A b tetramers. We are very grateful to the following persons for excellent technical assistance:  Fatima L'Faqihi ,  Vale ́rie Duplan-Eche, Anne-Laure Iscache, Lidia De la Fuente, Paul Menu of the INFINITy Cytometry platform ;  Adrien Castinel  of the  GeT-PlaGE Genotoul ; and the personnel of the  Inserm   US006  ANEXPLO/Creffre animal and experimental histopathology facilities. We thank  F. Fiore  for supervising the construction of the Il2 fl mice and the members of the 'Integrative T cell Immunobiology team' and  Sylvie Guerder  for discussions and input in the project. JPMvM is grateful to the staff of the  Biochemistry Institute of the Lausanne University, Epalinges, Switzerland , for its hospitality.