dataesr/scientific-paragraphs-categorization huggingface dataset:
2f ). Fold change between Signal (central pixel) and background (upper left corner 5×5 pixels) was computed. For Extended Data Fig. 5f , APA were generated for loops filtered on their size (<200kb) and around the best 80 cleaved DSBs. 597 and 17206 loops in damaged (80 damaged TADs) and undamaged TADs were retrieved respectively in Replicate 1, while 645 and 19150 were retrieved for Replicate 2. Fold change between Signal (central pixel) and background (lower left corner 5×5 pixels) was computed. APA heatmaps were reprocessed using ggplot2 in order to display counts at the same color scale between -DSB and +DSB conditions. For Extended Data Fig. 5g , loop strength was extracted from APA files enhancement. txt corresponding to enrichment foldchange (Peak to Mean, P2M). Differential loop strength was the log-ratio of two conditions loop strengths (+DSB/-DSB).
γH2AX spreads within prior TAD as revealed by 4C-seq (a) 4C-seq tracks before DSB induction obtained for three independent biological replicates and γH2AX ChIP-seq track after DSB induction for different viewpoints (red arrows) localized at three AsiSI sites (black arrows). ChIP-seq data are smoothed using 100 kb span, 4C-seq using a 50 kb span. (b) Example of the Hi-C pattern obtained on chromosome 1 at a 500kb resolution (left) together with a magnification at a 10kb resolution (right). (c) CTCF and calibrated-SCC1 ChIP-seq tracks. (d) Average profile of CTCF ChIP-seq around all loop anchors on the genome (determined using this Hi-C dataset, see Methods), validating both CTCF ChIP-seq and Hi-C datasets. (e) γH2AX ChIP-seq after DSB induction, 4C-seq and CTCF ChIP-seq peak position before DSB induction are shown (peaks in blue contain a CTCF motif in the forward orientation and peaks in red a CTCF motif in the reverse orientation).
Acknowledgments We thank the genomics core facility of EMBL for high throughput sequencing as well as the facilities and expertise of the high throughput sequencing core facility of the  I2BC (Centre de Recherche de Gif) ,  F. Beckouet  ( CBI ) for advice on the yeast work,  J. Rispal  for occasional experimental help, and  C. Normand  ( CBI ) for continuous helpful discussions. Work in J. E. H. 's lab was funded by grant  R35 GM127029  from the  U. S. National Institutes of Health. F. Z. was supported by the  National Institute of General Medical Sciences Training  Grant  TM32GM007122. E. R. is supported by  Labex Ecofect  ( ANR-11-LABX-0048 ) of the  Université de Lyon, Fondation FINOVI  and by the  European Research Council  ( ERC-StG-LS6-805500 ) under the European Union's  Horizon 2020 research  and innovation programs.
Funding in G. L. 's laboratory was provided by grants from the  European Research Council  ( ERC-2014-CoG 647344 ), the  Agence Nationale pour la Recherche  ( ANR-14-CE10-0002-01  and  ANR-18-CE12-0015 ), the  Institut National Contre le Cancer (INCA) , the  Ligue Nationale Contre le Cancer (LNCC). C. A is a recipient of a  FRM fellowship  ( FRM FDT201904007941 ).
RESULTS Photobacterium damselae subsp. piscicida secretes an NlpC/P60 family protein. Photobacterium damselae subsp. piscicida (Phdp) virulent strains have a relatively simple profile of secreted proteins in mid-exponential-growth-phase cultures  (45). Apart from AIP56 toxin, no other proteins have been identified and characterized. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) analysis of proteins from Phdp extracellular products (ECPs) precipitated with trichloroacetic acid (TCA) revealed a band of approximately 55 kDa that was excised from the gel and subjected to matrix-assisted laser desorption ionization2time of flight mass spectrometry (MALDI-TOF MS). The obtained MS data were used in a Mascot search against the NCBI database resulting in the identification of a hypothetical protein from Photobacterium damselae subsp.
damselae (Phdd) CIP 102761 (VDA_000779; NCBI accession number EEZ39759). The 1,479-nucleotide homologous sequence in the Phdp MT1415 strain (accession number TJZ86030. 1) was then amplified using primers designed based on the VDA_000779 sequence. In silico analysis (SignalP 5. 0 and NCBI conserved domain search) of its 499-amino-acid translation product predicted a Sec signal peptide (M 1 to A 19 ), followed by an N_NLPC_P60 putative stabilizing domain (Pfam PF12912), an SH3b1 (Pfam PF12913/12914), and an NlpC_P60 domain (Pfam PF00877), classifying it as a protein belonging to the NlpC/P60 family, hereafter referred to as PnpA (Photobacterium NlpC-like protein A). PnpA is encoded in a genetically unstable chromosomal region, and its expression levels are similar at exponential and stationary phases of growth.
To investigate the genetic context of pnpA in Phdp MT1415 strain, the draft genome sequence of MT1415 was obtained in this study. Then, homologous DNA sequences of a number of Phdp and Phdd isolates were additionally retrieved from the GenBank database and subjected to comparative sequence analysis (Fig. 1 ). This revealed that the PnpA-encoding gene is invariably linked to a downstream gene encoding an RNase T and to an upstream gene encoding an a-galactosidase, the latter being a pseudogene in some Phdp isolates. As a whole, the DNA flanking pnpA underwent a massive insertion of transposase genes (IS elements of the IS1 and IS91 families) likely followed by accumulation of inactivating mutations, resulting in a collection of pseudogenes. This process of gene decay not only affected the transposase genes themselves but also flanking genes encoding enzymes putatively involved in sugar metabolism, as a-galactosidases, a-amylases, and pullulanases (Fig.
The crystal structure of PnpA was determined at 1. 4-Å resolution by molecular replacement with DvLysin (PDB entry 3M1U, 26% sequence identity), an endopeptidase from Desulfovibrio vulgaris Hildenborough  (26). The crystal asymmetric unit contains two PnpA molecules, which are essentially identical (root mean square deviation [RMSD], of 0. 5 Å for 457 aligned Ca atoms). Table  S1  in the supplemental material summarizes the data collection, processing, and refinement statistics. Analysis of the intermolecular packing interfaces within the crystal lattice suggests that the molecule behaves as a monomer in solution, which is in agreement with the molecular mass estimated by size exclusion chromatography. The PnpA monomer has an overall structure similar to that of DvLysin  (26) , namely, one N-terminal "c-clip" or "N_NLPC_P60" stabilizing domain (residues N 20 -N 133 ), two SH3b domains (SH3b1, residues I 134 -V 218 ; SH3b2, residues D 219 -T 295 ), and the C-terminal NlpC/P60 catalytic domain (residues P 296 -K 499 ) (Fig.
Bacterial cell extracts and extracellular products. Phdp was grown in TSB-1 at 25°C with shaking (160 rpm) and centrifuged (6,000 Â g, 5 min, 4°C), and the pellets (total cell extracts) and culture supernatants were collected. Supernatants were filtered (0. 22 mm) to obtain extracellular products (ECPs). For SDS-PAGE, proteins in the ECPs were precipitated with trichloroacetic acid (TCA) as previously described  (45). PnpA identification. ECPs from Phdp strain MT1415 were subjected to SDS-PAGE followed by Coomassie blue staining. A protein band of approximately 55 kDa was analyzed by MALDI-TOF MS in a 4800 Proteomics Analyzer (Applied Biosystems) at TOPLAB GmbH. The MS data were used for a Mascot search against the NCBInr sequence database. Draft genome sequence of Phdp MT1415 and genomic context of pnpA locus.
To delete the PnpA-encoding gene in Phdp MT1415, it was necessary to obtain at least 2 kb of upstream and downstream sequences free of repetitive insertion sequence elements that would compromise the specific recombination steps during allelic exchange. Therefore, the draft genome sequence of strain MT1415 was obtained, using an Illumina platform as previously described  (48)  and deposited in the GenBank database under accession number SUMH00000000. A comparative analysis was conducted by retrieving the genomic contexts of pnpA genes in different Phdp and Photobacterium damselae subsp. damselae (Phdd) isolates whose draft or complete genomes are available in the GenBank database. The GenBank locus tag numbers of the pnpA homologues used in this analysis are VDA_000779 (Phdd type strain CIP 102761), PDPUS_2_00834 (Phdp 91-197), PDPJ_2_00460 (Phdp OT-51443), BEI67_17705 (Phdp L091106-03H), and BDMQ01000002 (Phdp DI21).
For the pnpA negative Phdd strain RM-71, the draft genome sequence as a source of homologous flanking DNA sequences was used (accession number NZ_LYBT00000000. 1). The DNA sequences were handled with Vector NTI 10. 3. 0 sequence editor (Invitrogen). Recombinant PnpA. The pnpA open reading frame (ORF) (GenBank accession number TJZ86030. 1) was amplified from Phdp MT1415 genomic DNA using Pfu DNA polymerase (Thermo Scientific) and primers 59-cgcccATGGATATAAATAAACATTTAATGC-39 and 59-gcgctcgagTTTTTCAAATAGATATTTTTC-39 (target sequences are in uppercase letters) and cloned into pET28a(1) using the NcoI and XhoI restriction sites, in frame with a C-terminal 6ÂHis tag. Mutation of C 324 to alanine was achieved by site-directed mutagenesis by inverse PCR using Q5 high fidelity DNA polymerase (New England BioLabs), pET28-PnpA as the template, and primers (59-GCCTCTGGTTTATTAAAAAGGTTATTCAGC-39 and 59-ATCATTATTGAAATC-CATTCCCCC-39).
Crystals were obtained by mixing protein solution (6. 7 mg ml 21 in 50 mM Bis-Tris [pH 6. 5] and 500 mM NaCl) with an equal volume of crystallization solution (100 mM imidazole [pH 8. 0], 15% [wt/vol] polyethylene 8000 [PEG 8K]). Crystals appeared after 24 to 48 h. The crystals were cryo-protected by sequential transfer into their crystallization condition with increasing concentrations of ethylene glycol (up to 30% [vol/vol]) and then flash-frozen in liquid nitrogen prior to data collection. Data collection, structure solution, and refinement. Diffraction data were collected at beamline Proxima-1 of Synchrotron SOLEIL (Saint-Aubin, France) (64) on a Dectris Pilatus 6M detector (750 images, 0. 2°rotation, 0. 2-s exposure) and indexed and integrated with XDS  (65). Space group determination, data scaling, and merging were performed with POINTLESS and AIMLESS from the CCP4 program suite  (66).
The structure of PnpA was solved by molecular replacement with Phaser MR as implemented in the CCP4 program suite (66, 67) using the coordinates of a putative gamma-D-glutamyl-L-diamino acid endopeptidase from Desulfovibrio vulgaris Hildenborough (DvLysin, PDB entry 3M1U, 26% sequence identity) as the search model. Phase refinement and initial model building were performed using ARP/wARP  (68). Model completion and refinement were done iteratively with COOT (69) and Phenix. refine (70, 71) , respectively. Refinement and structure validation statistics are summarized in Table  S1  in the supplemental material. All illustrations of macromolecular models were produced with PyMOL  (72). The experimental data were deposited with the Structural Biology Data Grid (73) under accession number https:// doi.
It was then stepped down to 0% over and held for 10 min to return initial conditions. Exactive Focus was operated under electrospray ionization in positive mode and data-dependent acquisition mode (ddMS2) control by Xcalibur 4. 0. For structural confirmation of muropeptides, higherenergy collisional dissociation (HCD) fragmentation was set up with a normalized collision energy at 20%. Data were processed both with the software TraceFinder 3. 3 (Thermo Fisher Scientific) and Xcalibur 4. 0 for peak area determination. For testing PnpA activity against macromolecular PG, PGs from Phdp and several bacterial species, purified as described above, were incubated with 100 mg PnpA or inactive PnpA C324A at 37°C overnight in 50 mM Tris (pH 8. 0) and 300 mM NaCl. PGs incubated with vehicle were used as controls.
After digestion, PGs were analyzed by HPLC or HPLC/HRMS as described above. Accession number(s). The draft genome sequence of strain MT1415 was obtained and deposited in the GenBank database under accession number SUMH00000000. The experimental data were deposited with the Structural Biology Data Grid (73) under accession number https://doi. org/10. 15785/SBGRID/ 736. The structure factors and atomic coordinates of PnpA are deposited in the RCSB Protein Data Bank with accession number 6SQX.
ACKNOWLEDGMENTS We are grateful for access to the HTX crystallization facility (Proposal ID:  BIOSTRUCTX_8167 ). The support of the X-ray  Crystallography Scientific Platform of i3S (Porto, Portugal)  is also acknowledged. This work was financed by  Fundo Europeu de Desenvolvimento Regional (FEDER)  funds through the  COMPETE 2020 Operacional Program for Competitiveness and Internationalization (POCI) ,  Portugal 2020 , and by  Portuguese funds through Fundação para a Ciência e a Tecnologia/Ministério da Ciência, Tecnologia e Ensino Superior (FCT)  in the framework of the project  POCI-01-0145-FEDER-030018 (PTDC/CVT-CVT/30018/ 2017 ). A. D. V. was supported by national funds from  Fundação para a Ciência e a Tecnologia (FCT) , I. P. , within the scope of the Norma Transitória - DL57/2016/CP1355/ CT0010.
This work had also support from the  State Agency for Research (AEI) of Spain  cofunded by the  FEDER Program  from the  European Union  (grants  AGL2016-79738-R  and  BIO2016-77639-P ) and from the  French Government 's  Investissement d'Avenir program ,  Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases " (grant  ANR-10-LABX-62-IBEID ; http://www. agence-nationale-recherche. fr/investissements-d-avenir/). A. R. was supported by a postdoctoral fellowship from the  Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases " and from an  Infec-ERA grant  ( INTRABACWALL -16-IFEC-0004-03 ). Author contributions are as follows: conceptualization, J. L. , A. D. V. , and  N. M. S. D. S. ; data curation, P. J. B. P. ; formal analysis, J. L. , C. P. , J.
Using such particle size estimation, we evaluated Mw of the HNPs after exploiting Mw data reported for HNPs of composition similar than those considered in this work. As detailed below, this procedure constrains the modelling of protolytic titration data versus pH and electrolyte concentration and makes transparent the inherent electrostatic limits of NICAD model. In contrast, it advocates for the recourse to more rigorous soft PB theoretical framework. In line with the above elements, we selected five proton titration data sets published for HNPs with known particle radius, namely: the Ribeirão Preto Humic acid (FPHA)  [34] , Suwanee River Fulvic Acid (SRFA)  [28] , Laurentian Fulvic acid (LFA)  [35]  and the fulvic (Hf:FA) and humic acid (Hf:HA) from Sierra de Buio, Spain  [36]. A detailed description of these HNPs, of their size measurement and of the estimation of their molar mass (Table  1 ) is provided in Supporting Material (SM, section A therein).
maxH1 (𝑠𝑠) and  maxH2 () may be transformed in mol kg -1 , by multiplication by   , resulting in  maxH1 and  maxH2 , respectively. The set of eqs 1-6 fully determines the dependence of the searched charge  on pH and ionic strength. A Fortran program (named SPBT for Soft Poisson Boltzmann-based Titration) was developed to compute eqs 1-6. In particular, the solution of eq 2 was derived according to the numerical strategy employed elsewhere  [42]  using Fortran COLSYS subroutine  [43]  which approximates the solution through spline-collocation at Gaussian nodes and selects mesh subdivision following an auto-adaptative strategy. This algorithm has already been proven extremely efficient for evaluation of e. g. complex soft particle electrokinetics  [44]  and soft macrosurface electrohydrodynamics  [45].
Accordingly, in this work we elaborate a new approach for the analysis of NPs proton titration data where Poisson-Boltzmann theory for soft particle electrostatics is consistently implemented. The particle charge is evaluated from proper spatial integration of the local particle charge density defined by an isotherm reflecting the chemical binding of charge determining protons to ionogenic sites located at the particle surface and within the particle body volume, and corrected by local electrostatics retrieved from numerical solution to the nonlinear Poisson-Boltzmann equation valid for weakly to highly charged particles. Unlike NICAD, this framework is used to generate proton titration curves with a consistent account of particle size-dependent potential profile over the whole spectrum of Debye layer extension, i.
C. Details of here-developed SPBT-PEST treatment of proton titration data and associated proton affinity spectra. This section describes in detail the optimization of the model parameters rp, Mw,  maxH1 In the first step of the data treatment, an input data file containing the measured HNPs charge Q (in mol kg -1 ) vs. pH at given solution ionic strength I and the associated proton affinity spectrum (dQ/dpH vs. pH) is created. This spectrum was computed using Matlab via cubic spline smoothing of the experimental Q vs. pH data and subsequent derivation of the splined data with respect to pH. The second step consists in establishing initial estimates for all parameters. As far as rp and Mw are concerned, their respective estimates are listed in Table  1  whereas those for  maxH1 ,  maxH2 , mH1, mH2,  � 1 and  � 2 were evaluated from the proton titration curve Q vs.
Once this parameters optimization criterion is satisfied, the updated values of rp and Mw are given by PEST in a result file that further contains the Q and dQ/dpH vs. pH and I theoretically generated by SPBT. In step 3b of Figure  S1 , SPBT-PEST duo resumes the full procedure detailed in step 3a albeit with now considering rp and Mw as non-adjustable parameters with values given by those obtained at the end of step 3a, and with adjusting  maxH1 ,  maxH2 , mH1, mH2,  � 1 and  � 2 to achieve Q and dQ/dpH vs. pH and I data fitting. This is done via modification of the parameter template file that indicates to PEST which parameters it should now adjust and which ones should be maintained constant all along the iterated fitting procedure. Further details on the above procedure with step-by-step treated examples are provided in the form of a SPBT-PEST fitting manual that is available on request together with the SPBT executable file.
Supporting material. A. Detailed description of the HNPs considered in this work, of their size measurement and of the estimation of their molar mass. B. Details of the NICA-Donnan modelling framework. C. Details of here-developed SPBT-PEST treatment of proton titration data and associated proton affinity spectra. D. Evaluation of the initial estimates of the chemical parameters involved in eq 6. E. Performance of SPBT-PEST in recovering HNPs proton titration and affinity spectra data. F. Comparison between SPBT-PEST and NICAD reconstructions of proton titration data. Additional details on the procedure outlined in E with step-by-step treated examples are provided in the form of a SPBT-PEST user manual that is available on request together with the SPBT executable file.
Funding This work has been supported by The  PALADIN  project, publicly funded through the  French National Research Agency  under the " Investissements d'avenir" program  with the reference  ANR-10-LABX-04-01  Labex CEMEB. SG acknowledges support from the  Agence Nationale pour la Recherche  through the Grant  GENOSPACE ANR-16-CE02-0008. The funding body did not play any role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.
Availability of data and materials Project name: EvoLaps. Project home page: http:// www. evola ps. org/. Operating system(s): Platform independent. Programming language: EvoLaps client side is based on JavaScript with D3. js and Leaflet libraries, while the server side is based on PHP, Tcl and Python scripts. Other requirements: none. License: GNU GPL. Any restrictions to use by non-academics: none. EvoLaps source code and the datasets used in this study are available on http:// www. evola ps. org/.
are limited by the availability of sequences in barcoding reference databases; a lack thereof results in high percentages of unassigned sequences. In this study we (i) present the current status of known vs. barcoded marine species at a global scale based on online taxonomic and genetic databases; and (ii) compare the current status with data from ten years ago. Then we analyzed occurrence data of marine animal species from five Large Marine Ecosystems (LMEs) classified as biodiversity hotspots, to identify any consistent disparities in COI barcoding coverage between geographic regions and at phylum level. Barcoding coverage varied among LMEs (from 36. 8% to 62. 4% COI-barcoded species) and phyla (from 4. 8% to 74. 7% COI-barcoded species), with Porifera, Bryozoa and Platyhelminthes being highly underrepresented, compared to Chordata, Arthropoda and Mollusca.
Thus, an integrative approach (combining barcoding and morphology) is needed for reliable species identification and high-quality biodiversity assessments. The rapid generation of high volumes of data resulting from the recent advances in "Omics" technologies and high-throughput sequencing methods poses great opportunities for metabarcoding, metagenomics and metatranscriptomics studies  (Raupach et al. 2016; Wangensteen et al. 2018; Holman et al. 2019 ). Yet, substantial gaps remain in public DNA barcoding databases, leading to a high proportion of unassigned sequences in metabarcoding-based studies (e. g. ,  Martin et al. 2021) , and substantial uncertainty in the estimated number of species living in a surveyed area  (Valentini et al. 2016). Furthermore, many national and regional initiatives aimed at expanding DNA barcode databases are not providing complete species documentation, including morphology, ecology, and biology.
Moreover, databases are usually not fully curated and harmonized by the time funds are exhausted after project completions  (Collins et al. 2020). Consequently, some databases may have limited accessibility or are taken offline only a few years after their publication, and the obtained sequences are not always uploaded on public barcoding databases (see Online Resource S1, based on  Radulovici et al. 2010 and Trivedi et al. 2016 ). In addition, most DNA barcoding initiatives were, and still are, focusing on terrestrial or freshwater environments (see Online Resource S1) or on marine organisms that can serve as bioindicators of environmental impacts  (Weigand et al. , 2019). However, more substantial contributions are needed to improve the genetic knowledge for underrepresented marine taxa at a global scale.
Moreover, we selected five Large Marine Ecosystems (LMEs, https://www. lmehub. net) considered as biodiversity hotspots (Mediterranean Sea, Caribbean Sea, North Sea, Indonesian Sea, and Red Sea) to detect possible disparities in COI barcoding coverage at regional scale and to underline where more efforts should be enrolled from now on. Data for all marine animal species worldwide were gathered from the World Register of Marine Species (WoRMS,  Horton et al. 2021) , while their presence within the five LMEs was retrieved from the Ocean Biodiversity Information System (OBIS, Intergovernmental Oceanographic Commission of UNESCO; http://www. iobis. org accessed on May 2021). Correspondent availability of COI barcode records at species level were retrieved from the National Center for Biotechnology Information (NCBI, https://www.