dataesr/scientific-paragraphs-categorization huggingface dataset:
Coverage data were exported as bigwig (file format) for further processing BLESS data processing Samples were sequenced with HiSeq 2500 (Illumina) according to our custom protocol for low-diversity samples  (Mitra et al. , 2015) , generating 61-bp paired-end reads containing BLESS barcodes (distal and proximal). Both barcodes were removed using cutadapt, and BLESS data were aligned on hg19 using bwa in paired-end mode (bwa aln and bwa sampe). In order to prevent reads that represent bona fide DSB signal, but start exactly at cleaved AsiSI sites from being marked as duplicates and improperly discarded, fragments were reconstituted from paired reads using Rsamtools and GenomicAlignments (R packages). Fragments with lengths > 500 bp were dropped as aberrant and the remaining fragments were de-duplicated (fragments with exact same start and end position on genome were considered as duplicates and only kept once).
In Figures  4E  and S5D , total ChIP-seq count for each dataset (untreated condition) was compared for HR and NHEJ DSBs. Individual modifications were determined as enriched in HR or NHEJ using one-sided Mann-Whitney-Wilcoxon tests (if p value < 0. 05). In Figure  S5E , total ChIP-seq count for each dataset was compared in treated and untreated conditions, separately for HR or NHEJ DSBs. Individual modifications were determined as increased or decreased following DSB induction using one-sided Mann-Whitney-Wilcoxon tests (p value < 0. 05). In Figure  S6B , Spearman correlation coefficients were determined using the cor() function in R and the correlation matrix was generated using the corrplot package. For Figure  S4B , to check if DSBs repaired by HR or NHEJ exhibit a non-random distribution among the main 3D genomic compartments (A1, A2, B1, B2, B3, B4 and NA (non-assigned))  (Rao et al.
, 2014 )), we compared the distribution of all genomic loops among these compartments with the distributions for loops containing either HR-repaired or NHEJ-repaired DSBs (no loop contained both HR and NHEJ sites). The p values were calculated using hypergeometric probability distribution, with the null hypothesis that HR and NHEJ loops are distributed among the 3D compartments in the same manner as all genomic loops. Conservative Bonferroni correction for multiple hypothesis testing was applied to correct p values to reflect testing all 3D compartments for both possible enrichments and depletions. Averaged ChIP-seq profiles Averaged ChIP-seq profiles were generated using the R package ggplot2. The x axis represents genomic position relative to DSB and the y axis represents the mean coverage at each bp, except for larger windows (1 Mb scale) where data were smoothed using a 50 kb span.
Log2 ratio was computed using the bamCompare tool from deepTools (http://deeptools. readthedocs. io) with two bam files (before and after damage) as inputs. Positive and negative values for log2 ratio are respectively represented in red and blue. For gH2AX, 53BP1, RAD51, and XRCC4, ChIP-seq data are only available for damaged cells. In this case, the y axis only represents the mean ChIP-seq coverage in the damaged condition.
ACKNOWLEDGMENTS We thank the  GeneCore facility of EMBL  for high-throughput sequencing. We thank  K. M. Miller  ( University of Texas ) for critical reading of the manuscript and  J. -Y. Masson/G. Dellaire  for reagents and advice for the Clover/HR assay. Funding was provided by  Polish National Science Centre  ( 2011/02/A/NZ2/ 00014  to K. G. ,  2016/21/B/ST6/01471  to N. D. , and  2015/17/D/NZ2/03711  to M. S. ) and  Foundation for Polish Science  (TEAM to K. G. ). Funding to M. R. , B. F. , and N. D. was provided by  NIH  ( 5 R01 GM 112131 ). J. C. acknowledges funding support from the  Canadian Institutes of Health Research  ( FND-143314 ) and holds the  Canada Research Chair in Chromatin Biology and Molecular Epigenetics. M. A. was supported by the  Fondation pour la Recherche Medicale (FRM).
Funding in G. L. 's laboratory was provided by grants from the  European Research Council  ( ERC-2014-CoG 647344 ),  Agence Nationale pour la Recherche  ( ANR-14-CE10-0002-01  and  ANR-13-BSV8-0013 ), the  Institut National contre le Cancer (INCA) , and the  Ligue Nationale contre le Cancer (LNCC). T. C. is an INSERM researcher.
Generation of random positions In order to generate a control set of non-DSB regions, we first computed a thousand random positions on the entire genome (excluding chromosome Y) using R. These random sites were filtered for being at least 1 Mb away from the top 150 cleaved positions with IRanges::findOverlaps() function. We also excluded regions with a null ChIP-seq count (in any of our datasets) in a 1 kb window in order to avoid regions that are systematically underrepresented in ChIP-seq experiments. Finally, 80 sites were randomly picked from the remaining list.
Lectins, and related receptors such as adhesins and toxins, are glycan-binding proteins from all origins that decipher the glycocode, i. e. the structural information encoded in the conformation of complex carbohydrates present on the surface of all cells. Lectins are still poorly classified and annotated, but since their functions are based on ligand recognition, their 3D-structures provide a solid foundation for characterization. UniLectin3D is a curated database that classifies lectins on origin and fold, with crosslinks to literature, other databases in glycosciences and functional data such as known specificity. The database provides detailed information on lectins, their bound glycan ligands, and features their interactions using the Protein-Ligand Interaction Profiler (PLIP) server.
Deciphering the glycocode, i. e. the information carried in the 3D-structure of glycans, is therefore of general interest. The complexity of cell surface glycan structures and the corresponding sophisticated biosynthetic machinery required to assemble them are matched by the variety of protein receptors able to recognize them. Among them, lectins are carbohydrate-binding proteins that decipher the glycocode  (3, 4). Lectins are present in all living organisms, from virus to plants or mammals  (3, 5). They are involved in cell-cell interaction and signaling and play a role in innate immunity through their ability to discriminate between self and non-self  (6). They are key elements in many infectious Nucleic Acids Research, 2019, Vol. 47, Database issue D1237 processes and therefore are the targets for the development of novel classes of anti-infectious compounds  (7).
Lectins can be engineered, providing new tools for biomarkers and vectorization  (8). They are also used in glycomics studies notably with the recent development of lectin arrays  (9, 10). Until recently, carbohydrate structures were poorly annotated in the protein data bank, but initiatives such as pdbcare  (11)  and Privateer  (12)  now provide tools for controlling stereochemistry and quality of carbohydrate moieties in 3D-structures. The structural knowledge of the binding sites and of their interactions with ligands is a prerequisite for the design of high-affinity glycomimetic ligands. The fine description of the network of hydrogen bonds, van der Waals contacts, ionic bonds, but also of the bridging water molecules and the bivalent cations is imperative. Further insight into the oligomeric state is equally important: lectins generally exhibit several carbohydrate binding sites, resulting in strong avidity to the dense structure of glycans at the surface of cells.
The design of multivalent glyco-compounds able to fit with the lectin topology allows for the synthesis of highly specific ligands  (13). While active carbohydrate enzymes are well covered in the CAZy database (http://www. cazy. org/)  (14) , lectins are still poorly characterized and crossreferenced. A collection of lectin structures is available in the Glyco3D portal (http://glyco3d. cermav. cnrs. fr) that provides a broad range of structural information for the glycosciences  (15). Two other databases are maintained and accessible: the Lectin Frontier Database (LfDB: https://acgg. asia/lfdb2/) provides information on the specificity of 400 lectins  (16) , and LectinDB (http://proline. physics. iisc. ernet. in/lectindb/) records information mainly about sequences of plant lectins  (17). However, no database provides full interconnection between structural information on lectin and other bioinformatics resources related to lectins, ligands and function.
The content and use of UniLectin3D is described below. It is centered on 3D data, using PDB information, with associate protein sequence features and appropriate curation of the glycan topology. It provides a family-based classification and cross-links to specialized glyco-related databases. In particular, each carbohydrate ligand can be seen as part of the full carbohydrate structures. Finally, the 3D visualization of contacts between the lectin and the ligand is visualized via the Protein-Ligand Interaction Profiler (PLIP) server  (24).
DATA SOURCE AND DATABASE CONSTRUCTION UniLectin3D is available at https://www. unilectin. eu/ unilectin3D. It is developed with PHP version 7, Bootstrap version 3 and MySQL database version 5. 6. The interface is compatible with all devices and browsers. The pages are dynamically generated to match the research criteria selected by the user in the query window. Interactive graphics are developed in JavaScript based on D3JS libraries version 3. A tutorial can be accessed on the first page. The UniLectin3D database is populated with (i) the lectins referenced in the Glyco3D database until 2016, (ii) manually filtered PDB structures based on the weekly release and (iii) PDB structures containing a carbohydrate processed with PLIP to filter lectins from glycoproteins, glycan transporters or cazymes.
Based on manual filtering, lectin structures are classified by Origin, Class and Family. The Class divides lectins into large groups based on structures. The Family divides lectins mostly by species. This classification is further refined according to the type of protein fold and to the nature of the linked glycans. The glycan description, i. e. monosaccharide content and condensed IUPAC nomenclature (http://www. sbcs. qmul. ac. uk/ iupac/2carb/38. html), is entered by manual curation with correction of possible errors in the PDB nomenclature. UniLectin3D cross-references several other databases (Table  1 ). It links to the glyco-related SugarBindDB, GlyConnect (https://glyconnect. expasy. org) and GlyTou-Can (https://glytoucan. org/) through the shared IUPAC sequences of glycans. A control of the quality of the glycan structure and stereochemistry is provided by a link to PDB-care (www.
glycosciences. de/tools/pdb-care/). Since SugarBindDB also contains protein data, other links from UniLectin3D are made via UniProt accession numbers  (25). Information about the specificity of lectins is provided through links to the Consortium for Functional Glycomics (http://www. functionalglycomics. org). The taxonomy identifier and PubMed reference links are based on the information collected in the corresponding PDB entries. The general flowchart of UniLecin is displayed in Figure  1. A unique lectin, as referenced by a UniProt accession number, may be associated with multiple PDB structures. Lectins stored in the curated section of UniLectin3D have at least one referenced structure.
OVERVIEW OF LECTIN DATA The UniLectin3D database contains 1740 lectin structures that have been manually curated; this corresponds to 428 different lectin sequences (as of 29 August 2018). Bibliographic entries cover 765 published articles describing at least one structure. The first classification level, referred to as Origin, separates the lectins into the main kingdoms of the living world. The second level orders the lectins according to the protein fold into 75 classes. The third level separates the lectins according to their species in 309 families. Some statistics can be obtained from the sunbursts displayed on the home page (Figure  2 ). One-third (534) of the lectin structures are from animal origin. The most populated class of animal lectins are the galectins; these proteins bind specifically to ␤-galactoside  containing glycans.
The second most populated class is the C-type lectins that contain calcium in their binding sites. Plant lectins, which were the first to be studied, represent 24% (416) of the content of the database. They comprise the very large and diverse family of L-type lectins (previously described as legume lectins) and the jacalin-like lectins with subfamilies specific for mannose and galactose. There is a growing number of 3D structures of lectins that are reported to occur in bacteria 21% (368), viruses 13. 6% (239) and fungi 9% (157). The number of structures of lectins from algae, protists, parasites and molds is growing slowly reflecting the pace of elucidation of their biological functions. The lectin fold is defined by the relative spatial arrangement of secondary structure elements. It characterizes the multiplicity of binding to glycans and is directly linked to the ability of lectins to bind and cross-link glycancontaining molecules in a multivalent fashion.
CURATED INFORMATION FOR EACH LECTIN For each of the 1740 structures of the database, a feature viewer is available with different levels of information. The example of a norovirus capsid protein complexed with blood group A tetrasaccharide (PDB code: 3ASP) (33) is displayed in Figure  5. The top window displays the Lite-Mol viewer  (34)  for rapid visualization of the 3D X-ray structure. Options are available for full-screen display and for an interactive change of the display parameters. A second LiteMol viewer panel displays the specific ligand in its close interaction with the lectin-binding site. Curated classification, species, lectin fold, bibliographic references, resolution of the structure and complementary information in the form of comments (mutation,complex. ) are also provided.
Related structures with the same UniProt accession number or with strong sequence similarity (> 50%) are listed. Several levels of sequence information are provided for the glycan ligand, including the monosaccharide com- position, the IUPAC nomenclature detailing linkages and the glycoCT format that is used in glycoinformatics for providing a unique description of glycan structure  (35). Links to external databases (see Table  1 ) complement information regarding published articles, taxonomy, crystallography  (36) , sequence and glycan array specificity when available at the Consortium for Functional Glycomics site. Links to SugarBind, the database of pathogen lectins  (23) , are provided when relevant. Graphical representations of the lectin and binding site structures have been prepared using the PyMol (https://pymol.
org/2/ by Schr ödinger) or Lite-Mol (https://www. litemol. org/) (33) software. We take particular care in complying with the recommended standard representation of carbohydrates known as SNFG  (37) , and as mentioned above using the popular glycoCT encoding format  (35). These options facilitate cross-linking to glycorelated external databases such as GlyConnect. The third panel is devoted to the description of the molecular interactions between a lectin and its glycan ligand. In this process of molecular recognition, the formation of specific non-covalent interactions between proteins and ligands is the key factor  (38). The PLIP is a tool to detect biologically relevant interactions, considering eight different interaction types and including water-mediated contacts  (24). For this web server, PLIP interaction data were precalculated for all lectin-glycan complexes in the database and can be readily visualized in the browser using the PLIP applet from the SWISS-MODEL web server  (39) , based on NGL  (40).
DISCUSSION AND CONCLUSION The lack of annotation of carbohydrate binding-proteins is a limitation to the functional interpretation of many cellular processes. Lectins are still poorly annotated in databases and genomes, therefore our effort in establishing UniLectin3D focuses on addressing this problem. The inclusion of cross-references to UniLectin3D in UniProt demonstrates that our data curation effort is heading in the right direction. Furthermore, a deeper embedding of UniLectin3D in the set of glyco-resources of the Glycomics@ExPASy collection is in preparation. Not only will SugarBindDB and GlyConnect shortly refer back to UniLectin3D (straightforwardly via their shared UniProt accession numbers) but we will also integrate lectin data in the glyco-epitope search tools  (42). Currently, GlyCon-nect can be queried to reveal associations between glycosylated proteins and full glycan structures from which potential glyco-epitopes can be extracted.
The next step of integration with UniLectin3D will open the possibility of displaying the potential set of lectins that bind those extracted epitopes. Cross-linking with glycan ligand information is more complicated than with protein information since there is no equivalent yet of the UniProt universal resource in glycobiology. Furthermore, there is not yet either a distinction between annotated and unannotated glycans in glycodatabases. The GlyTouCan repository only registers glycan structures but do no annotate them  (21) , whereas the GlyConnect database annotates glycans but mainly stores those that are attached to proteins, leaving out glycolipids among others. Nonetheless, the international community of glycoinformatics in which we partake, is jointly addressing these issues and fast progress is expected to reduce the current difficulties of cross-referencing adequate glycan ligand information.
The amount and the quality of the 3D-structures of lectins from diverse origins are such that meaningful searches can be conducted leading to eventual new classifications, predictions of the occurrence of structural motifs (folds, quaternary structures, topology. ) all the ways to understanding the structural basis underlying the exquisite binding to complex glycans. Since recognition of glycan is the main function of lectins, a particular effort was invested in the description and representation of interaction in the binding sites, with several types of image and graphical viewer. The use of PLIP  (24)  provides a detailed description of non-covalent contacts that represents a new resource for medicinal chemists involved in developing glycomimetics targeted to lectins. The UniLectin project, which includes the Unilectin3D database, will expand in the next years with the integration of additional predictive tools.
). A simple search box is available on the UniLectin3D home page and allows searching by keywords, PDB or UniProt accession numbers, fragments of glycan sequences or textual fragments of the title of a publication. The advanced search offers a selection of criteria. Lectin can be searched by structure or by sequence family with the support of drop-down lists. The classification of lectins (Origin, Class and Family) D1240 Nucleic Acids Research, 2019, Vol. 47, Database issue.
FUNDING Swiss  Federal Government ,  State Secretariat for Education, Research and Innovation SERI, ANR PIA Glyco@Alps  [ ANR-15-IDEX-02 ];  Alliance Campus Rhodanien Co-Funds  (http://campusrhodanien. unige-cofunds. ch). Funding for open access charge:  Glyco@Alps  [ ANR-15-IDEX-02 ].
ncbi. nlm. nih. gov/pubmed Bibliographic information pdb-care http://www. glycosciences. de/tools/pdb-care/ Structural check of carbohydrates (11) Taxonomy http://www. ncbi. nlm. nih. gov/taxonomy Organism taxonomy UniProtKB http://www. uniprot. org Lectin functional annotation (25) CFG http://www. functionalglycomics. org Experimental data of lectins specificity PLIP http://plip. biotec. tu-dresden. de Identification of protein-ligand interactions (24) LiteMol https://www. litemol. org/ 3D visualization of lectin structure (34) NGL http://nglviewer. org/ Molecular graphics (40) ProtVista http://ebi-uniprot. github. io/ProtVista/ Visualization and annotation of sequences (27).
Identification of miR-106a-3p targets Since a single miRNA can potentially target hundreds of genes  10  , we next crossreferenced the predicted targets of miR-106a-3p using four different algorithms (miRanda, miRDB, DianaMT and miRWalk). We found 67 genes common to the four algorithms (Figure  4A ). In parallel, to study the effect of the over-expression of miR-106a-3p on global gene expression patterns, we isolated total RNA from HMEC cells transfected with miR-106a-3p mimic and performed microarray analysis (HG-U133 Plus 2. 0). The results indicated that transfection of miR-106a-3p induced significant changes in the expression of 6465 genes (p value <0. 05; Table  S1 ) when compared to controls. To establish whether the global expression changes observed upon miR-106a-3p overexpression correlated with the data from prediction algorithms (Figure  4A ), both datasets were intersected (Table  S1 ).
RNA isolation and miRNA microarray Total RNAs were isolated from three independent samples of HMEC-transfected cells using the miRNeasy Kit (Qiagen) according to the manufacturer's instructions. The quantity and size of RNAs were analyzed for concentration, purity and integrity by using spectrophotometric methods in combination with the Agilent Bioanalyzer (Agilent Technologies). Microarray analyses were performed on 3 independent replicates of mimic control transfected cell samples (control), 3 independent replicates of miR-106a-3p transfected cell samples. Data were analyzed and normalized using the Rosetta Resolver Error Model. In order to remove systemic noise from the data, genes with low intensity values that are close to background were filtered out. We use standard deviation of the background (σ) value to estimate background.
Acknowledgments This work was supported in part by grant from " La Region Nouvelle-Aquitaine " is warmly thanked for financial support. We thank  Dr. E. Chevet  and  Prs J. Robert  and  T. Hupp  for critical reading of the manuscript,  Pr. P. Soubeyran  for leadership at the  SIRIC BRIO, Dr. V. alérie LeMorvan  for help in setting up the RT-qPCR system, and  Dr. R. Nookala  of  Institut Bergonié  for the medical writing service.
The integration of publicly available and new patient-derived transcriptomic datasets is not straightforward and requires specialized approaches to deal with heterogeneity at technical and biological levels. Here we present a methodology that can overcome technical biases, predict clinically relevant outcomes and identify tumour-related biological processes in patients using previously collected large reference datasets. The approach is based on independent component analysis (ICA) -an unsupervised method of signal deconvolution. We developed parallel consensus ICA that robustly decomposes merged new and reference datasets into signals with minimal mutual dependency. By applying the method to a small cohort of primary melanoma and control samples combined with a large public melanoma dataset, we demonstrate that our method distinguishes celltype specific signals from technical biases and allows to predict clinically relevant patient characteristics.
, 2014 , Teschendorff, Journee et al. , 2007)  and deduce interactions between biological processes  (Lee & Batzoglou, 2003). Importantly, ICA results are concordant with clinical data, such as cancer subtypes  (Biton et al. , 2014)  and survival, suggesting ICA as a potentially useful tool for patient diagnostics and prognostics. The method can also recognise and remove biologically irrelevant signals such as technical factors and/or confounders. Therefore, such an approach can make use of large sets of high-throughput transcriptomic (or other) data that were collected through different stages of technological progress. For cancer research, one of the most valuable data sources is The Cancer Genome Atlas (TCGA), which holds over 10 000 patient-derived samples including various levels of omics data: DNA, RNA, and proteins.