dataesr/scientific-paragraphs-categorization huggingface dataset:
Beta mixture quantile dilation (BMIQ) was used to normalize between the two different probe designs (Infinium I and Infinium II)  29. Differentially methylated probes were identified using limma R package. Probes with a Benjamini-Hochberg adjusted p-value below 0. 1 and a methylation difference of at least 10% were defined as differentially methylated probes (DMPs). Probes were assigned to genes, CpG island annotations (CpG island, CGI shores, CGI shelves, and open sea) and gene regions (TSS1500, TSS200, 5′UTR, 1st exon, gene body and 3′UTR) based on the HumanMethylation450 v1. 2 Manifest file from Illumina. The online software tool EpiExplorer was used to identify enrichment of DMPs in different genomic regions (such as promoters, enhancers or chromatin marks)  30. Differentially methylated regions (DMRs) were identified using the DMRcate R package.
Bioinformatics analysis. Bioinformatics analyses were done using different softwares. For canonical pathway analysis, KEGG (Kyoto Encyclopedia of Genes and Genomes) (http://www. genome. jp/kegg/tool/map_ pathway2. html) 31,32 and Metacore software packages Metacore ™ (GeneGo, St Joseph, MO, USA; https://portal. genego. com) were used to identify significantly overrepresented pathways using. Enrichment statistics were calculated for these data sets assuming a hypergeometric distribution to identify significantly overrepresented pathways. Gene Ontology (GO), gene network by data-mining and transcription factor analysis was also done using Metacore ™. MicroRNA validated targets and target prediction was searched using the miRWalk database 33 that enables retrieval of genes predicted by different algorithms including Diana-microT, miRANDA, miRDB, miRWalk, RNAhybrid, PICTAR5, PITA, RNA22 and TargetScan.
Bioinformatic analyses were performed to identify biological functions of the 253 genes which expression was modulated by the mixture of epicatechin metabolites. First, gene networks built with the data-mining approach using Metacore software suggested that the flavanol metabolites change the expression of genes involved in different biological processes. Among the most significant networks, we identified a group of genes (CCR9, PLAU or NCAM1) involved in the regulation of chemotaxis but also networks regulating cell adhesion, cell junctions and cellular cytoskeleton organization. Among the genes identified in these networks are VE-cadherin, CDX1, SOS1, CLD11 and MAP2. These genes encode proteins involved in the control of cellular junctional complexes that control endothelial cell integrity and permeability.
To further refine biological functions of these differentially expressed genes in response to epicatechin metabolites, we performed the pathway analysis using KEGG and Metacore databases. Interestingly, similar to results obtained from network analysis, the most over-represented pathways were those involved in cell adhesion, leukocyte transendothelial migration, Ras signaling pathway, calcium signaling pathway, focal adhesion or gap junctions (Fig. 2 ). Among the genes identified in these pathways are PPP1R12B, SOS1, CLD11, CDH5, RASGRP3 and RAPGEF3. All these identified over-represented pathways are involved in regulation of adhesion and transmigration of leukocyte through the endothelium. We than used Transcription Regulation algorithms (Metacore software) to ascertain transcription factors which activity could be modulated by epicatechin metabolites and explain downstream gene expression changes.
Two miRNAs, hsa-let-7f and hsa-miR-221, were identified as modulated by the activation with TNFα and the pre-exposure to the mixture of metabolites, and interestingly these two miRNAs presented opposite expression patterns in the two conditions. To identify potential target genes of the differentially expressed miRNAs and consequently the cellular processes that could be affected, the miRWalk database was used. In total, 11899 potential targets genes were identified for all miRNAs with differential expression after TNFα treatment, and 7857 for the miRNAs which expression was affected by the mixture of three epicatechin metabolites. Comparison of the target genes identified for the two conditions showed that 6516 genes were in common. When we further compared the 253 differentially expressed genes with the 7857 potential target genes of differentially expressed miRNAs, both detected in HUVECs pre-exposed to the epicatechin mixture, we identified that 73 were in common.
This suggests that the expression of 30% of the differentially expressed genes in response to epicatechin metabolites could be regulated through modulation of miRNA expression. Bioinformatic analyses using KEGG database were performed to identify cellular processes in which the 7857 potential target genes of the 20 miRNAs differentially expressed after epicatechin metabolite treatment are involved in. Over 100 pathways were identified indicating that these miRNA could regulate multiple cellular processes. Comparison of the top 40 most over-presented pathways represented by the target genes of differentially expressed miRNAs with those identified with differentially expressed genes revealed that 26 were identified in common (Fig. 5 ). Interestingly, among these 26 pathways are pathways regulating of focal adhesion, cytoskeleton organization, cell adhesion, Ras signaling pathway or chemokine signaling pathway.
This comparison suggests that the pathways identified using both differentially expressed genes and target genes of miRNA are particularly involved in the regulation of cell adhesion and transendothelial migration. Since we identified the processes regulating monocyte adhesion and transendothelial migration as an interesting cellular target of epicatechin metabolites, miRWalk database was used to identify the miRNAs involved in the regulation of these processes. Accordingly, 247 miRNAs were identified as post-transcriptional regulators of genes involved in the leukocyte transendothelial migration pathway. Comparison of these miRNAs with the 20 miRNAs identified as modulated by the epicatechin metabolites in endothelial cells revealed that 12 of them are in common. This suggesting that over half of differentially expressed miRNAs are potentially involved in the regulation of endothelial cell function and permeability.
Together with miRwalk, Metacore software was used to identify potential target genes of miRNA which expression has been identified as modulated by epicatechin metabolites and to create gene networks for both mRNA and miRNA targets. Using this approach, several gene networks were obtained. Merging the three most significant networks resulted in a complex network containing 308 target genes (Supplemental Fig. S6 ). Interestingly, these 308 genes are involved in regulating focal adhesion, chemotaxis, cytoskeleton organization or cell adhesion. Taken together, results obtained using different bioinformatic approaches suggest that differentially expressed miRNA in response to the mixture of epicatechin metabolites are involved in the regulation of different cellular processes, particularly adhesion, cytoskeleton organization or focal adhesion.
For example miR-10a is involved in the regulation of inflammatory response of endothelial cells  58  , miR-1290 is involved in the regulation of the endothelial barrier  59  , miR-130b and 60 miR-361-5p can regulate angiogenesis  61  , whereas miR-365 modulates apoptosis  62. Taken together, our results suggest that EC metabolites can modulate the expression of miRNAs involved in the regulation of different endothelial cell functions, including monocyte adhesion and transendothelial migration. Upon linking differentially-expressed miRNAs with potential mRNA targets in the miRWalk database, we observed that nearly a third of the differentially expressed mRNAs are in common with the predicted miRNA target genes of differentially expressed miRNAs. This observation suggests that the EC metabolites can exert post-transcriptional fine-tuning of gene expression by modulating the expression of miRNAs.
Acknowledgements We thank the  Mars Inc. for providing us with metabolites. We also thank  Celine Boby  for conducting transcriptomic analyses at the  Metabolism Exploration Platform, INRA Auvergne-Rhone-Alpes Center. This work was funded by the  European Union  project " Flaviola ", grant number  226588 , within the  Seventh Framework Program  (http://www. flaviola. org), French Nutrition Society  (http://sf-nutrition. org),  CM1406   Epigenetic Chemical Biology (EPICHEM)  and  UA BOF/NOI  grants. The authors thank the  COST Action FA1403  " POSITIVe ", supported by  COST (European Cooperation in Science and Technology) , for the financial support for the short-term scientific mission of C. C. at  INRA  (D. M. ) during which the docking analysis was performed.
This method produces long, unbiased sequences that, in turn, facilitate subsequent assembly  18  , a critical step in plants due to the high proportion of repetitive sequences throughout their genomes  19. Most of the projects aimed at obtaining a draft or a complete plant genome were performed using large-insert based sequencing methods  20, 21  to allow estimation of the number of genes, and abundance of transposable elements and microsatellites. In the functional part of the genome in particular, the annotation of large-inserts can provide an arsenal of biological information to facilitate comparison against databases and, in addition, to determine the distribution of BAC inserts relative to related genomes in order to examine the degree of synteny between them and gain insights into evolutionary relationships  22, 23.
, cooled on ice); and washed twice in buffer 1 [2× SSC, 0. 1% (w/v) SDS] (15 min. , 50 °C) and buffer 2 [0. 5× SSC, 0. 1% (w/v) SDS] (30 min. , 50 °C). Next, the hybridized membranes were placed in a film cassette for 24 h. ; radioactive signals were detected using a PhosphorImager TM and Storm 820 scanner (Amersham Biosciences) and analyzed using HDFR3 software, to identify the positive clones. Each positive clone was individually validated by PCR. In order to estimate insert sizes, the preserved cultures were scraped and a positive single colony of each BAC grown in a 96-well plate (overnight, 37 °C) containing 1200 µL of LB medium with chloramphenicol (12. 5 µg/mL) and glycerol (6%). DNAs were then isolated using a NucleoSpin ® 96 Flash (Macherey-Nagel) BAC DNA purifi- cation kit, digested with 5 U of FastDigest ™ NotI enzyme (Fermentas) and size-fractioned by PFGE (6 V.
DNA Sequencing and Assembly From Long Sequence Reads. Approximately 5 µg of each pool was used for the construction of a SMRT library based on the standard Pacific Biosciences (San Francisco, CA, USA) preparation protocol for 10-kb libraries. Each pool was sequenced in one SMRT Cell using P6 polymerase in combination with C4 chemistry, following the manufacturer's standard operating procedures and using the PacBio RS II long-read sequencer. Reads were assembled by a hierarchical genome assembly process (HGAP workflow)  26  , and using the v2. 2. 0 SMRT ® analysis software suite for HGAP implementation. Reads were first aligned by the PacBio long-read aligner or BLASR  27  against the complete genome of Escherichia coli, strain K12, substrain DH10B (GenBank: CP000948. 1). The E. coli reads, as well as low quality reads (minimum read length of 500 bp and minimum read quality of 0.
QV scores over 20 are from very good data with only 1% error probability. Finally, Quiver polishes the assembly for final consensus  26. Once the refined assembly was obtained, each BAC-insert sequence was individualized by matching the end sequences to the pool of assembled sequences using BLAST. Read coverage was assessed by aligning the raw reads on the assembled sequences with BLASR. Identification and Annotation of Repetitive Sequences. Eukaryotic genomes contain a substantial portion of repetitive elements which are organized into three main classes: dispersed repeats (mostly transposable elements and retrotransposed genes), local repeats (tandem repeats and simple sequence repeats or microsatellites) and segmental duplications (duplicated genomic fragments)  28. It is highly recommended to identify and mask repetitive regions before gene prediction.
Otherwise, unmasked repeats can produce spurious BLAST alignments, resulting in false evidence for gene annotations  29. The v2. 2 REPET package was used for de novo detection and annotation of transposable elements (TEs). The annotation process starts with self-alignment of the sequences by all-by-all comparison. Matching clusters are then identified based on the same cluster sequences in a given family. A consensus for each family is created, and each consensus is classified according to the structures and domains present. The last step entails annotating TE copies  30, 31. The resulting elements were then compared with sequences deposited in the Viridiplantae section of the Repbase repeat database  32. They were classified by PASTEC, a tool for classifying TEs by searching for structural features and similarities  33  and implementing the hierarchical classification system proposed by  34.
Repeat masking was subsequently performed with RepeatMasker Open-3. 0  35  using the library generated by the REPET and Repbase Viridiplantae dataset  32. MISA  36  was used to search for microsatellites based on microsatellite sequences with at least 10 nucleotides in the repeat for mono-, 5 for di -, and 3 for tri-, tetra-, penta-or hexanucleotides. Composite microsatellites were also identified. They are formed by multiple, adjacent, repetitive motifs. Hence, a microsatellite is considered composite if it has a maximum interruption of 10 bp between motifs  37, 38. Gene Prediction and Functional Annotation. Evidence-driven gene prediction was performed based on gene models of Arabidopsis thaliana and Theobroma cacao and using the following software: Augustus 39 , GlimmerHMM  40  , GeneMark.
hmm  41 , and SNAP  42. Ab initio gene finding was performed with the BRAKER pipeline  43. Protein homology detection and potential intron resolution were detected by Exonerate software  44  against the annotated genomes of Populus trichocarpa, Salix purpurea, Ricinus communis and Manihot esculenta, downloaded from the Phytozome website  45. These species are among the plant genomes with the highest number of top hits for P. edulis  15. Additionally, a P. edulis RNA-seq library (see details below) was used to support gene model predictions. PASA  46  was used to produce alignment assemblies based on overlapping transcript alignments from P. edulis RNA-seq data. The results were combined by EVidence Modeler software  47  , and PASA was used to update the EVidence Modeler consensus predictions, adding UTR annotations and models for alternatively spliced isoforms.
In addition, protein signature recognition was performed using the InterProScan tool  52. Microsynteny Analysis. The 20 P. edulis BAC-inserts with the highest number of annotated genes were used for the identification of potential microsyntenic regions between P. edulis and Populus trichocarpa (Salicaceae), and P. edulis and Manihot esculenta (Euphorbiaceae), two related Malpighiales species with entirely sequenced and well-annotated genomes. P. edulis coding sequences were compared with these two genome sequences, available in the Phytozome database  45  using BLASTN. Based on the phylogenetic relationships among the Malpighiales species, we chose P. trichocarpa because it is the closest species to P. edulis. Taxonomically speaking, Passifloraceae appears as a sister group to Salicaceae. On the other hand, M.
Results BAC Selection, Sequencing and Assembly. A total of 66 BAC inserts were selected for complete sequencing based on our previous BAC-end sequencing results  15  , and 46 were selected using probes homologous to transcripts of P. edulis  53  (Supplementary Table  S1 ). Thus, in total, 112 BAC inserts from the P. edulis genomic library were sequenced. The sequencing process resulted in 571,565 high quality reads, ranging from 500 to 46,831 bp in length. Sequences were between 24,316 and 142,456 bp in length, corresponding to their respective band sizes resolved by PFGE. The high quality of the long reads (QV > 47) and high coverage of the contigs (on average 278×) are indications of the reliability of our data (Supplementary Table  S2 ), leading to the conclusion that all inserts were completely sequenced and assembled.
axonopodis infection, none of the functional categories related to plant defense were found to be overrepresented. However, protein signatures related to plant immunity and defense functions were identified. The serine/ threonine-protein kinase active site (32 genes), and the leucine-rich repeat domain, L domain-like (27 genes) were among the most represented signatures (Table  2 ). In total, automated searches for protein signatures recognized 1,383 signatures in 1,488 genes of P. edulis: 783 domains, 453 protein families, 125 sites and 22 replicates (Table  2 ). Most of these signatures (769) were taken from the Pfam database  54  , and the remainder from SuperFamily (239)  55  and Smart (223)  56.
This abundance was to be expected, since kinases belong to a superfamily of proteins with copies in the hundreds or thousands and are components of all cellular functions. These proteins use ATP γ-phosphate to phosphorylate serine and threonine or tyrosine residues from other proteins  57. Note that to date there is an enormous scarcity of information on Passiflora nuclear genes in databases. This means that obtaining gene-based probes for selecting new regions for whole sequencing is practically impossible. The structural and functional annotation of 1,883 genes provides a significant set of high quality gene sequences that can be used in many other studies on Passiflora (see Supplementary Table  S3 ). Transposable elements (TEs) are highly widespread in plant genomes, accounting for 14% of the Arabidopsis thaliana genome  58  , up to 80% of the maize genome  59  and 17.
Acknowledgements We would like to thank  GATC Biotech  (http://www. gatc-biotech. com) for providing DNA sequencing services, and  Mr. Steve Simmons  for proofreading the manuscript. This work was supported by the following Brazilian institutions:  Fundação de Amparo à Pesquisa do Estado de São Paulo  ( FAPESP , grant no. 2014/25215-2 , postdoctoral and doctoral fellowships awarded to CFM, grant no. 2013/11196-3  and LAC-S, grant no. 2017/04216-9 , respectively),  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq , scholarship awarded to ZPC) and  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior  (CAPES, scholarship awarded to ACER).
IPR005162 [Domain]: Retrotransposon gag domain IPR011009 [Domain]: Protein kinase-like domain IPR000719 [Domain]: Protein kinase domain IPR027417 [Domain]: P-loop containing nucleoside triphosphate hydrolase IPR001878 [Domain]: Zinc finger, CCHC-type IPR011990 [Domain]: Tetratricopeptide-like helical domain IPR008271 [Active_Site]: Serine/threonine-protein kinase, active site IPR013083 [Domain]: Zinc finger, RING/FYVE/PHD-type IPR029058 [Domain]:  Alpha/Beta  hydrolase fold IPR017441 [Binding_Site]: Protein kinase, ATP binding site IPR016024 [Domain]: Armadillo-type fold IPR032675 [Domain]: Leucine-rich repeat domain, L domain-like IPR013320 [Domain]: Concanavalin A-like lectin/glucanase domain IPR009057 [Domain]: Homeodomain-like IPR002885 [Repeat]: Pentatricopeptide repeat IPR011989 [Domain]: Armadillo-like helical IPR016040 [Domain]: NAD(P)-binding domain IPR013242 [Domain]: Retroviral aspartyl protease IPR001841 [Domain]: Zinc finger, RING-type IPR017986 [Domain]: WD40-repeat-containing domain IPR012337 [Domain]: Ribonuclease H-like domain IPR015943 [Domain]: WD40/YVTN repeat-like-containing domain IPR001128 [Family]: Cytochrome  P450 IPR001611  [REPEAT] -Leucine-rich repeat IPR012677 [Domain]: Nucleotide-binding alpha-beta plait domain IPR001680 [Repeat]: WD40 repeat IPR001005 [Domain]: SANT/Myb domain IPR029044 [Domain]: Nucleotide-diphospho-sugar transferases IPR026960 [Domain]: Reverse transcriptase zinc-binding domain IPR017853 [Domain]: Glycoside hydrolase superfamily IPR000504 [Domain]: RNA recognition motif domain IPR013210 [Domain]: Leucine-rich repeat-containing N-terminal, plant-type IPR001245 [Domain]: Serine-threonine/tyrosine-protein kinase catalytic domain IPR018247 [Binding_Site]:  EF-Hand 1 , calcium-binding site IPR005135 [Domain]: Endonuclease/exonuclease/phosphatase IPR011598 [Domain]: Myc-type, basic helix-loop-helix (bHLH) domain IPR011992 [Domain]: EF-hand domain pair IPR002401 [Family]: Cytochrome P450, E-class, group I IPR005123 [Domain]: Oxoglutarate/iron-dependent dioxygenase IPR002048 [Domain]: EF-hand domain IPR012334 [Domain]: Pectin lyase fold IPR013781 [Domain]: Glycoside hydrolase, catalytic domain IPR011050 [Domain]: Pectin lyase fold/virulence factor IPR017930 [Domain]: Myb domain IPR017972 [Conserved_Site]:  Cytochrome P450 , conserved site IPR006121 [Domain]: Heavy metal-associated domain, HMA IPR001810 [Domain]: F-box domain IPR000620 [Domain]: EamA domain IPR012336 [Domain]: Thioredoxin-like fold IPR016140 [Domain]: Bifunctional inhibitor/plant lipid transfer protein/seed storage helical IPR025558 [Domain]: Domain of unknown function DUF4283.
1 mg of piRFP670-N1 (used as transfection control) and 200pmol of siRNA and, immediately resuspended in culture media and transferred to a 10 cm dish. After 48 hr, cells were trypsinized and 0. 25 million cells plated into glass coverslips and the rest analyzed on BD Accuri C6 Plus Flow Cytometer. Cloverexpression was assayed by fluorescence microscopy the next day that is 72 hr post-nucleofection. Data represent the mean percentages (±SD) of Cloverpositive cells (structured nuclear GFP signal) over the iRFP670-positive population from independent experiments performed in triplicates (n > 800 cells per condition). d KEY RESOURCES.
NHEJ assay The U2OS cell line with I-Sce1 reporter GFP-RFP cassette to measure NHEJ has been described  (Jacquet et al. , 2016). The cells were transfected with 200 nmol of the indicated siRNA using Lipofectamine RNAimax (Invitrogen) for 36 hr and infected with I-Sce1 adenovirus for 1 hr. Cells are harvested 48 hr after DSB induction and analyzed by FACS for GFP and RFP expression on a BD Accuri C6 Plus Flow Cytometer. Control cells produce 10% of RFP positive/GFP negative cells in the NHEJ reporter. The data presented are from biological triplicate experiments.
QUANTIFICATION AND STATISTICAL ANALYSIS ChIP-seq data processing H3K4me2, H4K20me1, H3K36me3, H3K79me2, H2Bub, H3K56ac, H4K16ac, H3K9me2, H4S1P, H3K36me2, and gH2AX samples were sequenced using Illumina HiSeq 2500 (single-end, 50 bp reads) at BGI (Beijing Genomics Institute, Hong Kong). FK2 samples were sequenced using Illumina HiSeq 2500 (single-end, 50 bp reads) at GATC biotech (Konstanz, Germany). XRCC4 at 1, 4, and 24 hr post 4OHT treatment, DNA ligase IV, H2BK120ac, H2AZ, H1, MacroH2A, H3K9me3, H4K20me2, 53BP1, and gH2AX G1 and G2 samples were sequenced using Illumina NextSeq 500 (single-end, 75 bp reads) at EMBL Genomics core facilities (Heidelberg, Germany). H4K12ac, H2AZac, and H3 were sequenced using Illumina HiSeq 2500 (single-end, 50 bp reads) at EMBL Genomics e4 Molecular Cell 72, 250-262.
e1-e6, October 18, 2018 core facilities (Heidelberg, Germany). Previously published data include RAD51 and XRCC4 ChIP-seq in OHT-treated DIvA cells  (Aymard et al. , 2014) , RNA Polymerase II (S2P) in untreated DIvA cells  (Cohen et al. , 2018)  and MethylCap-Seq data in U2OS cells  (Deplus et al. , 2014)  The quality of each raw sequencing file (fastq) was verified with FastQC (https://www. bioinformatics. babraham. ac. uk/projects/ fastqc/). All files were aligned to the reference human genome (hg19) and processed using a classical ChIP-seq pipeline: bwa (http://bio-bwa. sourceforge. net/) for mapping and samtools (http://www. htslib. org/) for duplicate removal (rmdup), sorting (sort) and indexing (index). Coverage for each aligned ChIP-seq dataset (. bam) were computed with the rtracklayer R package and normalized using total read count for each sample.