dataesr/scientific-paragraphs-categorization huggingface dataset:
Datasets used in the bioinformatics analysis The following datasets were downloaded from the supplementary material of the corresponding papers: 1) For mRNAs containing cis-regulatory motifs: (i) in silico identification: AREs  (Halees et al. , 2008) ; (ii) experimental determination: CPEs  (Piqueet al. , 2008) , G4-containing  (Huppert et al. , 2008)  and TOP mRNAs  (Thoreen et al. , 2012). G4-containing genes were restricted to those harboring a G4 in 5'UTR. 2) For RBP targets: (i) PARE in HeLa cells: SMG6 (mRNAs actually cleaved by SMG6)  (Schmidt et al. , 2015)  (ii) CLIP in HEK293 cells: HuR and TTP  (Mukherjee et al. , 2011; Mukherjee et al. , 2014) ; (iii) CLIP in HeLa cells: YTHDF2  (Wang et al. , 2014) ; (iv) RIP-CHIP in HeLa cells: PUM1  (Galgano et al. , 2008) ; (v) RIP-CHIP in mouse neurons: 4E-T  (Yang et al.
, 2014). In the case of HuR, the transcripts clipped only in 5'UTR/CDS/introns were removed, and the target list was restricted to transcripts clipped more than once. In the case of TTP, the transcripts clipped only in introns were removed. For other RBP targets (ATXN2, MOV10, IGF2BP1-3, PUM2, FMR1, FXR1-2, AGO1-4, YTHDF2), CLIP data from different laboratories were previously processed through the same pipeline in the CLIPdb 1. 0 database using the Piranha method  (Yang et al. , 2015). We retained those performed in epithelial cells (HeLa, HEK293, HEK293T). Moreover, when replicates were available, we selected the RNA-protein interactions detected in at least 50% of the replicates. Except for FXR1-2 targets, we determined whether protein-RNA interactions occurred in UTR or CDS by intersecting coordinates of the read peaks with the v19 gencode annotation, and we removed the transcripts clipped in their CDS.
Acknowledgements We thank  Marina Pinskaya  and  Marc Gabriel  for scientific discussions and technical assistance. We also thank  Virginie Magnone ,  Kevin Lebrigand  ( NGS platform, UCA Genomix ),  Sylvain Baulande ,  Patricia Legoix-Ne ,  Virginie Raynal  ( NGS platform, Institut Curie ),  Nathalie Campo  ( LMGM, Toulouse ) and  Regis Blaise (IBPS, Paris ).
Data availability RNA-Seq gene data have been deposited in SRA under accession codes E-MTAB-4091 for the polysome profiling after DDX6 silencing, E-MTAB-5577 for the transcriptome after PAT1B silencing, and E-MTAB-5477 for the PB transcriptome, all in HEK293 cells. RNA-Seq gene data have been deposited in GEO under accession codes GSE115471 and GSE114605 for the transcriptome after XRN1 silencing in HeLa and HCT116 cells, respectively. ENCODE datasets are available at https://www. encodeproject. org under accession codes ENCSR893EFU for the DDX6 eClip experiment, and ENCSR109IQO for the transcriptome after DDX6 silencing in K562 cells. All data generated or analyzed during this study are included in Supplementary file 1. The following datasets were generated:.
The labelled samples were hybridized to the Human HT-12 v4. 0 Expression BeadChip as per the manufacturer's protocol (47,231 probes; Illumina, California, USA). The BeadChips were scanned using the iScan microarray BeadStudio platform (Illumina, California, USA). Quality standards for hybridization, labelling, staining, background signal, and basal level of housekeeping gene expression for each chip were verified. After scanning on the Illumina iScan, the resulting images were background subtracted, quantile normalized and processed using the GenomeStudio software (Illumina, California, USA). Data files were deposited into the National Center for Biotechnology Information Gene Expression Omnibus (https://www. ncbi. nlm. nih. gov/geo/ query/acc. cgi?acc = GSE115645). An Illumina detection P-value of <0.
Acknowledgements This work was supported by a  National Health and Medical Research Council (NHMRC)  of  Australia Project Grant  [ NHMRC #540107 ] and an  NHMRC Program Grant  [ NHMRC #569940 ]. This work was also partially supported by an  OIS grant  from the  Victorian State Government  and a  NHMRC   Centre of Research Excellence Grant  [NHMRC #  1041056 ]. Professors Kingwell ,  Owen, Inouye and Dunstan are NHMRC Research Fellows.
This suggests that at least the Sir3p subunit of the SIR complex is not inherited and therefore does not contribute to the epigenetic inheritance of the silenced state in subtelomeric regions and silent mating type loci in these conditions. We propose that the sudden increase in Sir3p off rates upon release from growth arrest transiently destabilizes the SIR complex, which causes a temporary increase in gene expression at silent mating type loci and an increase in the number of cells that are unresponsive to α factor. Finally, our RNA-seq datasets normalized to S. pombe mRNA, show that gene expression is restored faster on most low and moderately transcribed genes upon exit from growth arrest suggesting that heterochromatin integrity is important for the regulation of gene expression programs in sub-optimal growth conditions.
Arrested cells were incubated with estradiol for 16hrs to induce the Cre mediated tag switch. Anti-HA (old Sir3p) and anti-T7 (new Sir3p) chromatin immuno-precipitations were performed in parallel at regular intervals before and after Cre activation. ChIP-seq datasets (normalized to average genome read density as described in Materials and Methods) of "old" and "new" Sir3p showed that Sir3p dynamics are similar at all heterochromatic loci, including silent mating type loci and subtelomeric regions (Figure  1  B-D and replicate experiment in Supplementary Figure  S1 ). Since Sir3p with the "old" tag stops being produced after the tag switch, we can determine Sir3p OFF rates during and after exit from stationary phase by measuring how "old" Sir3p enrichment changes after the tag switch from the slope of the linear fit of the decrease in "old" Sir3p enrichment at time points during stationary phase and during the first cell cycle after release.
While a triple HA tag substantially reduces Sir3p functionality, the triple T7 tag almost completely obliterates it with 77% and 97% of cells insensitive to α factor before and after stationary phase, respectively. The triple tag may either destabilize the mRNA or the protein or interfere with the ability of Sir3p to interact with other SIR complex subunits or with its ability to bind to chromatin. Increased mRNA instability resulting in lower amounts of tagged Sir3p in the cell is however probably not the cause of the increased insensitivity to α factor in tagged midlog cells since RNA-seq datasets normalized to an external S. pombe RNA spike-in (Figure  6 ) show that midlog SIR3 mRNA levels are comparable in all three strains. The decrease in SIR3 mRNA levels during the first cell cycle after exit from stationary phase does however explain the relative increase in α factor insensitivity during this period compared to midlog cells in all three strains (Figure  3F ).
The least affected genes are mostly involved in ribosome function and biogenesis (Table  S1 ). We also looked at subtelomeric genes whose expression is more likely to be directly affected by changing Sir3p levels. Consistent with our RT-qPCR results from Figure  5  and previous reports  (16) , the wt levels of the SIR complex do not control the expression of most subtelomeric genes because we found only 16 genes out of 132 in our RNA-seq datasets that were located within 15kbp of subtelomeric SIR nucleation sites, whose gene expression profiles in SIR mutants and Sir3p hypo-morphs were significantly different from WT cells (Δcorrelation(Sir3Δ-WT2) -0. 34, Figure  S4 ). As we mentioned above, external "spike-in" normalization of RNA-seq reads allows us to directly compare mRNA levels between different time points and different strains.
We consequently determined apparent mRNA half-lives during stationary phase using the datasets from Figure  6. We detect a genome-wide drop in mRNA abundance in stationary phase, as would be expected if global transcription activity has largely stopped during that period. We measured apparent mRNA decay rates and half-lives directly from the decrease in mRNA levels from the midlog to the stationary phase, as shown in Figure  S5. Somewhat surprisingly and as opposed to decay rates in midlog cells  (18) , stationary phase mRNA decay rates are directly correlated with midlog mRNA levels meaning that the more an mRNA was abundant before stationary phase the shorter its half-life during stationary phase (Figure  S5 ). However, with the exception of Sir2Δ in which mRNA half-lives are globally ~50% longer, we did not observe any significant difference in mRNA half-lives between SIR3Δ and Sir3p hypo-morph strains and WT strains.
ChIP-seq and RNA-seq data analysis All analysis was done using in house Perl and R scripts available upon request. Sequences were aligned to S. Cerevisiae genome using BLAT (Kent Informatics, http://hgdownload. soe. ucsc. edu/admin/). Read count distribution was determined in 1bp windows and then normalized to 1 by dividing each base pair count with the genome-wide average base-pair count. For Sir3p binding to genes from Figure  4 , Input normalized read densities for each gene were aligned by the transcription start site. The median read density for each gene (from the tss to the end of the coding sequence) was determined for each transcript and the log2 of the median was determined for non-zero values. Medians with a value 0 were kept as 0 and signify that no sequences were detected for that gene in that particular dataset (time point).
Error bars are standard deviations from the average of two biological replicates. Figure  S4 : Gene expression dynamics after exit from stationary phase of subtelomeric genes responsive to Sir3 enrichment levels (top). The SSL2 gene is a negative control shown for comparison with Figure  5. We selected genes within 15kbp of subtelomeric Sir3p nucleation sites from the RNA-seq datasets in Figure  6  (middle panel) and kept the genes whose expression profiles were the most different between wt and Sir3D (∆correlation(Sir3∆-WT2) between -0. 34 and -1. 5) as shown in the bar graph on the bottom. The heat map shows midlog (M) and stationary (S) phase mRNA levels from the RNA-seq experiment from Figure  6. Genes are organized as in Figure  6A. mRNA decay rates (k d ) and half lives for each gene were determined from the decrease in mRNA levels midlog( mRNA M ) and stationary phases (mRNA S ) according to the equation shown in red below the heat map.
WT1) log phase cells or stationary phase cells in the first cell cycle after release in the presence of 0. 2µg/ml α-factor 60 and 420min after the beginning of the time course. Red arrows: budding cells; White arrows: shmoos. C; The bar graph shows the fraction of budding cells out of the total number of cells (budding and shmoo) for each strain and growth condition from B. The error bars are the standard deviation from the mean of all the replicates summarized in the table below the graph. D. same as C for the remaining set of strains from A that were not shown in B-C. E. α-factor test with the OE Sir3p strain which shmoos independently of Sir3p in the presence of α-factor because of the deletion of HML and MAT loci. F. Sir3 gene expression. Median Sir3 read density enrichment from the RNA-seq dataset from Figure 6 normalized to an external spike-in S.
Acknowledgments We thank  Fred van Leeuwen  and  Kevin Struhl  for yeast strains and plasmids. Thank you to  Marta Gut  and  Julie Blanc  from  CNAG (Barcelona, Spain ) and  Véronique Pantesco  ( IRMB, Montpellier, France ) for deep-sequencing services. We thank  Virginie Georget  ( MRI, Biocampus, Montpellier ) for her assistance with the microscope set-up. A previous version of this article is part of the doctoral thesis "Heterochromatin dynamics upon release from stationary phase in budding yeast" by  Hrvoje Galic. This work was supported by the  ERC-Consolidator  grant ( NChIP 647618 ) (MRL).
( WT1) log phase cells or stationary phase cells in the first cell cycle after release in the presence of 0. 2µg/ml α-factor 60 and 420min after the beginning of the time course. Red arrows: budding cells; White arrows: shmoos. C; The bar graph shows the fraction of budding cells out of the total number of cells (budding and shmoo) for each strain and growth condition from B. The error bars are the standard deviation from the mean of all the replicates summarized in the table below the graph. D. same as C for the remaining set of strains from A that were not shown in B-C. E. α-factor test with the OE Sir3p strain which shmoos independently of Sir3p in the presence of α-factor because of the deletion of HML and MAT loci. F. Sir3 gene expression. Median Sir3 read density enrichment from the RNA-seq dataset from Figure 6 normalized to an external spike-in S.
Statistical analysis For each experiment, at least three biological replicates were performed. Biological replicates are defined as replicates of the same experiment with flies being generated by different parent flies. For all experiments, we performed a Mann-Whitney test for statistical analysis. Statistical analysis was performed using GraphPad Prism version 7. 04 for Windows  (GraphPad Software, La Jolla California USA, www. graphpad. com). Results are presented as dot plots. Error bars represent s. d. from the mean. The sample size (n) and the P-value are reported in the figure legends (****p 0. 0001; ***p 0. 001; **p 0. 01; *p 0. 05; ns, not significant).
RNA-Seq analysis of bulk tumors Quality control of raw reads was done using FastQC. Reads were mapped to dmel6 reference genome with Subread aligner using default parameters  (Liao et al. , 2013). Read counts were calculated using Subread featureCount  (Liao et al. , 2014). Differential expression was computed using R package DESeq2  (Love et al. , 2014). To concentrate on genes that are expressed in tNBs and remove those that are specific to wild type neurons and glia of the VNC, we excluded from the bulk RNA-seq analysis all genes that are not expressed in at least 0,5% of the 5740 cells used for the single-cell RNA-seq experiment. Among the remaining 6660 genes, we selected as significantly enriched genes those with an adjusted p-value<0001. Gene Ontology and Reactome Pathway analysis was made using Panther (http://www.
pantherdb. org/). Sequencing data have been deposited in GEO under accession code GSE114562. Preparation of tNBs for single-cell mRNA-seq NB tumors were generated in pox n -Gal4, UAS-GFP UAS-pros RNAi2 , UAS-dicer2 flies. Fifty-six adult females (6-8 day-old) were killed in 70% ethanol and washed once in PBS. VNCs were dissected in PBS, collected in a RNase-free Protein LoBinding tube filled with ice-cold PBS, and incubated in a freshly prepared dissociation solution containing 0,4% bovine serum albumin (BSA), 1 mg/mL collagenase I and papain (Sigma Aldrich) in PBS for 75 min at 29˚C with lowest agitation. Tissues were then disrupted manually by pipetting up and down with a 200 mL tip. Dissociated cells were pelleted for 20 min at 300 g at 4˚C to remove the dissociation solution and to resuspend cells in ice-cold PBS + 0,4% BSA.
Single-cell mRNA sequencing Single cells were processed using the Single cell 3' Library, Gel beads and multiplex kit (10X Genomics, Pleasanton) as per the manufacter's protocol. Cells were partitioned into nanoliter-scale Gel Bead-In-Emulsions (GEMs) with the Chromium Single Cell Controller (10X genomics, Pleasanton), where all generated cDNA share a common 10x Barcode. Libraries were generated and sequenced from the cDNA and the10x Barcodes are used to associate individual reads back to the individual partitions. Analysis using molecular indexing information provides an absolute digital measurement of gene expression levels. Sequencing was performed using a NextSeq 500 Illumina device (1sample) containing transcript lengh of 57 bp. Sequencing data have been deposited in GEO under accession code GSE114986.
Data processing Single-cell mRNA seq data were analyzed using the 10x Genomics suite Cell Ranger 2. 0. 1 with default settings for de-multiplexing, aligning reads to the Drosophila genome (10X Genomics prebuilt dm6 reference genome) with STAR and counting unique molecular identifiers (UMIs) to build transcriptomic profiles of individual cells. This first level of analysis generates quality metrics (Q30, number of reads by sample. ), FASTQ files and filtered genes matrices.
PC analysis, UMAP clustering and differential expression Filtered gene matrices generated via Cell Ranger 2. 0. 1 was then processed with the R package Seurat v3. 0. 2, using the online tutorial as a guide (https://satijalab. org/seurat/v3. 0/pbmc3k_tutorial. html). We kept all cells that had between 200 and 4000 genes/cell. Cells with more than 5% mitochondrial gene expression were removed. Cell cycle genes were regressed out using the CellCycleScoring and ScaleData functions. PCA was then utilized to determine sources of variability. Twenty significant PCs were determined based on the JackStraw function. We used the PCHeatmap function to explore gene sets representing the main sources of heterogeneity in the dataset. Cells were then clustered with the FindClusters function a resolution parameter of 0.
Cell trajectory and pseudo time analysis by Single-cell mRNA sequencing Filtered gene matrices generated via Cell Ranger 2. 0. 1 was processed with the R package Monocle version v2. 10. 1. for pseudotime analysis  (Qiu et al. , 2017a; Qiu et al. , 2017b; Trapnell et al. , 2014). Graphics were generated through ggplot2 R library. Analysis was performed following online tutorial as a guide (http://cole-trapnell-lab. github. io/monocle-release/docs/#constructing-single-celltrajectories). UMI Counts were both normalized across cells using estimateSizeFactors function and gene expression variance was estimated using estimateDispersions functions to remove outlier genes using DESeq2 packages  (Love et al. , 2014). Semi-supervised ordering was performed with the classifyCells function. Only genes expressed in at least 10 cells were taken in account.
Marker genes were selected for subsequent analysis based on their normalized average expression and variability across cells using markerDiffTable function. The top 200 genes were used for ordering. Dimension reduction was performed with Monocle implementation of Discriminative Dimension Reduction Tree (DDRTree) algorithm  (Qi Mao et al. , 2017)  using reduceDimension function. Minimum spanning tree computation, cell state assignations and pseudo time reconstruction was performed using orderCells functions. differentialGeneTest and plot_pseudotime_heatmap functions were used to uncover the top 200 genes that change as a function of pseudotime. The BEAM function was used to identify genes that are differentially expressed between the three main branches of the trajectory (qval <1e-20). Code available upon request.
We find that the deterministic model perfectly fits the stochastic model (Figure  5A ), and reaches the same homeostatic state. Importantly, this is also a control that both algorithms were properly implemented. We also use this deterministic model to illustrate the influence of the initial proportions of C and S cells (Figure  5B ). sequencing was performed by the IGBMC GenomEast platform, a member of the 'France Genomique' consortium (ANR-10-INBS-0009)". We thank France-BioImaging/PICsL infrastructure (ANR-10-INSB-04-01). We thank S Kerridge for critical reading of the manuscript.
Acknowledgements We thank  I Davis ,  P Macdonald ,  DJ McKay ,  C Meignin M Noll ,  N Sokol  for flies and antibodies. We are grateful to  B Aigouy  for helping with the ' Tissue Analyser ' plugin,  C He  ´rouard -Molina  for performing cDNA librairies and sequencing at the IGBMC GenomEast platform,  A Saurin  for alignment of RNA-seq data, P  Outters at HalioDx (Marseille)  for generating the single-cell transcriptomic data,  F Hubert  and  J Oliver  for discussions on mathematical modeling,  JC Patel  for help with image analysis and  P Grenot at Ciphe  for help with FACS. We also acknowledge the  Bloomington Drosophila Stock Center  ( NIH P40OD018537 ), the  Vienna Drosophila RNAi Center (VDRC) ,  TRiP at Harvard Medical School  ( NIH/NIGMS   R01-GM084947 ),  Kyoto DGRC  and  NIG-Fly Stock Centers  for flies, and the  Developmental Studies Hybridoma Bank (DSHB)  for monoclonal antibodies.
Data availability Sequencing data have been deposited in GEO under accession codes GSE114986 and GSE114562. Codes generated for the numerical model are available at: http://doi. org/10. 17632/j2j9gmyb6m. 1. Codes used for single-cell RNA-seq analysis are available at: https://github. com/cedricmaurange/ Genovese-et-al. -2019 (copy archived at https://github. com/elifesciences-publications/Genovese-etal. -2019). The following datasets were generated:.
Q Exactive HF analysis Journal Pre-proof J o u r n a l P r e -p r o o f 10 The cleaved peptides (5 μL injection containing 400 ng of acidified peptide lysate) were resolved by reverse phase chromatography on an Ultimate 3000 RSLC nano system (Thermo Scientific) over a 120-min linear gradient, 100 min from 4% B to 25% B and 20 min from 25% B to 40% B (A: 0. 1% formic acid in MS-grade water; B: 80% acetonitrile, 0. 1% formic acid in MSgrade water). Eluted peptides were online desalted and then analyzed by a Q Exactive HF mass spectrometer (Thermo Scientific) operated in data dependent mode with a Top20 strategy (= up to 20 MS/MS cycles after a full scan), with full scans at a resolution of 60,000 and most intense peaks with charge state 2 or 3 being selected for fragmentation (normalized collision energy at 27%) and subsequent MS/MS (resolution 15,000) measurements.
Proteomic data interpretation The recorded MS/MS spectra were searched with MASCOT against the Paralichthys olivaceus full annotated proteome database  (Shao et al. , 2017)  which comprises 36,123 polypeptide sequences totaling  22,711,544 residues (GCF_001970005. 1 v1. 0, downloaded 2018-01-19). The following parameters were used: tryptic peptides with a maximum of 2 miscleavages during proteolytic digestion, a mas s tolerance of 5 ppm on the parent ion and 0. 02 Da on the MS/MS, fixed modification for carbamidomethylated Cys (+57. 0215) and variable modification for oxidized Met (+15. 9949) and deamidated Asn or Gln (+0. 9840). All peptide matches with a peptide score above its query identity threshold set at p < 0. 05 with the database and rank 1 were parsed using the IRMa 1. 31. 1c software  (Dupierris et al.
, 2009). A protein was considered validated when at least two different peptides were detected. Falsepositive identification of proteins were estimated using a reverse decoy database as below Journal Pre-proof J o u r n a l P r e -p r o o f 11 0. 1% with these parameters. The number of MS/MS spectra per protein (spectral counts) was determined for the five animals and the two technical replicates of each of the three sites. The protein abundances were compared for each Seine estuary sites to the Canche one. For each of these comparisons, the list of non-redundant proteins detected among the three corresponding datasets was established. The total spectral count of each polypeptide was used to rank the proteins from the highest to the lowest detection intensities. The statistical protein variations among the replicate samples of the three one-to-one comparisons were calculated using the T-Fold option of the PatternLab 2.
0 software  (Carvalho et al. , 2008). This module allows normalizing the spectral count datasets, calculating the average fold changes with statistics (t- For functional analyses, the sequences of the deregulated proteins were retrieved by using the "Batch Entrez module" available at www. ncbi. nlm. nih. gov. The four different subsets of proteins, corresponding to proteins up-and down-accumulated in fish encaged in the Rouen and Fosse Nord sites (as compared to fish encaged in Canche), as well as those deregulated following encagement in Canche (as compared to flounders fished in Canche), were then submitted to EggNOG 4. 5. 1  (Szklarczyk et al. , 2016)  and Blast2GO basic  (Götz et al. , 2008)  for functional re-annotation, Gene Ontology (GO) and Cluster of Orthologous Group (COG) analyses.