dataesr/scientific-paragraphs-categorization huggingface dataset:
The AAV vector was produced and purified by ultracentrifugation on double cesium chloride gradient as described in Ayuso et al. (Ayuso et al. , 2010). The titer in vector genomes (vg) was determined by real-time PCR targeting the AAV2 ITRs and following the conditions described by D'Costa et al. (D'Costa et al. , 2016). Identification of GC-rich regions and homopolymers in rAAV vector sequence GC-rich regions were identified in the recombinant AAV2/8-CAG-GFP vector sequence using the program NTContent (http://github. com/emlec/NTContent ), included in the SSV-Conta package. The following parameters were used: window size, 200; step size, 20 or window size, 50; step size, 25. Mononucleotide repeats composed of at least six nucleotides and simple sequence repeats (SSR) were localized along the AAV vector genome using the MISA-web server (https://webblast.
Quality control of the DNA sequencing libraries The quality of the DNA libraries was controlled by electrophoresis on a microchip using the High Sensitivity DNA kit (Agilent Technologies, Santa Clara, CA). Electrophoregrams were obtained after migration in the Agilent Bioanalyzer 2100 instrument and after analysis using the Agilent 2100 Expert Software. The total DNA concentration of the libraries was determined on the Qubit 4 fluorometer using the Qubit 1X dsDNA High Sensitivity Assay kit (ThermoFisher Scientific, Waltham, MA). Adapter-ligated DNA fragments were quantified by real-time PCR using the Universal qPCR KAPA SYBR Fast kit (Roche, Basel, Switzerland) before Illumina sequencing.
Bioinformatics Analysis Base call (BCL) files were converted into FASTQ files with the Illumina bcl2fastq2 Conversion Software (Illumina, San Diego, CA). Programs that are included in the SSV-Conta package (https://github. com/emlec/SSV-Conta) were then used to quantify and characterize all DNA species that are present in a rAAV vector lot: Quade, a FASTQ files demultiplexer, Sekator, an adapter trimmer, RefMasker to mask sequence homologies and ContaVect to analyze residual DNAs  (Lecomte et al. , 2019). Briefly, FASTQ files were demultiplexed with Quade according to their barcodes. The paired-end reads were assigned to a sample when the combination of the two barcodes (index read 1 and index read 2) was correct and if each base of the barcodes had a PHRED quality score of at least 25. Passed paired-end reads were trimmed using Sekator, according to the sequence quality and removing the adapter, as described in Lecomte et al  (Lecomte et al.
, 2019). The distribution of residual DNA was determined using RefMasker and ContaVect programs. The reference sequences were indicated in the ContaVect configuration files in the following order: the phage φX174 genome (GenBank accession number J02482. 1), the phage λ genome (J02459. 1), the rAAV genome, the plasmid backbone sequence, the plasmid helper sequence, the adenovirus 5 (Ad5) sequence (nucleotides 1 to 4344 of the Human adenovirus 5 complete genome, AC 000008) and the human genome (GRCh38 primary assembly). Using RefMasker, homologies between two reference sequences were masked on the second reference sequence in the list order, replacing homologous nucleotides with an N base symbol. ContaVect was run, applying the following main parameters: minimum mean read quality, 30; minimum quality mapping for read validation, 20; minimum mapping size, 25 bases.
Unmapped and mapped reads that did not fulfill these criteria were excluded. Sequencing coverage along each base of the vector plasmid was generated using the program SSV-Coverage, a program included in the SSV-Conta package. Sequencing data have been deposited in the European Nucleotide Archive (ENA) at EMBL-EBI under the accession number PRJEB38306 (https://www. ebi. ac. uk/ena/data/view/PRJEB38306 ).
After DNA extraction, the single-stranded rAAV genome is converted into double-stranded DNA (dsDNA) using random hexamers to generate a template compatible with Illumina sequencing library preparation workflow. Illumina-compatible sequencing libraries are then prepared using a custom protocol (Figure  1 , protocol ). DNA is sheared by sonication, end-repaired and A-tailed and adapters are ligatedvia a 3-prime T-overhang. DNA fragments that are flanked by adapters are amplified by 15 cycles of PCR. Finally, the library is paired-end sequenced using the Illumina HiSeq platform and the data are processed using our dedicated bioinformatics pipeline (https://github. com/emlec/SSV-Conta) (Lecomte et al. , 2019). The PCR amplification step has already been described as being the principal source of bias during sequencing library preparation  (Aird et al.
, 2011). Indeed, AT-  (Oyola et al. , 2012)  and GC-rich  (Benjamini and Speed, 2012)  fragments are less efficiently amplified than other regions in the genome which can lead to a bias resulting of a lower sequencing coverage. Consequently, sequencing library preparation protocols including a PCR step can cause an uneven distribution of reads coverage across the DNA. In the context of AAV vector analysis by HTS, this bias could cause an underestimation of AT-and/or GC-rich sequences and the impossibility to identify SNV and indels in these regions. To determine more precisely the impact of the base composition on the Illumina sequencing coverage, we first developed a new program, named NTContent (https://github. com/emlec/NTContent). This program is based on a sliding window analysis and requires a DNA sequence in FASTA format as input.
It generates a tab-delimited text file composed of two columns indicating for each given position the percentage of the requested nucleotide combination (Supplementary Figure  S1  ). The NTContent program was applied to a rAAV genome sequence containing the CAG promoter followed by the GFP reporter gene and the SV40 polyadenylation signal sequence. The CAG promoter was chosen because it has high GC content and it is known to be difficult template for PCR amplification. Figure  2a  shows the percentage of GC along the rAAV genome, the sequencing coverage obtained by SSV-Seq from a rAAV8 vector batch and from its corresponding plasmid vector. Both for the plasmid and the rAAV sample, two major drops in the sequencing coverage appeared in the CAG promoter around position 666 (asterisk, region 1) and position 1421 (asterisk, region 2) of the rAAV genome.
The superimposed graphs revealed that the two sequencing drops in the CAG promoter are clearly related to a high GC content. A more precise analysis of the percentage of GC was performed using the same program NTContent but taking into account the nature (A, T, G, or C) of 50 successive bases with a step of 25 bases. This analysis showed that the two steep drops coincide with regions composed of more than 90% of GC (Supplementary Table  S1 ). In order to further investigate the origin of the sequencing drops, the presence of A, T, C, and G nucleotide stretches along the rAAV genome was analyzed (Figure  2b-c  ). Of note, long G/C homopolymers have already been reported as a source of bias during sequencing on Illumina systems such as HiSeq instrument  (Shin and Park, 2016). A succession of C, T and G homopolymers was observed in region 1 of the CAG promoter between position 588 and 676 (Figure  2b  ).
C and G stretches were also detected in region 2, upstream the steep drop, between position 1290 and 1391 (Figure  2c  ). A previous study suggested that the presence of repetitive mononucleotides at the active site of a polymerase can lead to its dissociation from the DNA  (Fazekas et al. , 2010). For SSV-Seq, the PCR amplification is realized using the PfuUltra II Fusion HotStart DNA Polymerase. The number of nucleotides that fills the active site of this polymerase is 6. Thus, the software MISA was used to seek nucleotide repeats equal or greater than 6. Regions 1 and 2 of the CAG promoter contain 6 out of 14 homopolymers of [?] 6 bases detected in the AAV vector genome (Supplementary Table  S2  ). In particular, the first region showing a drastic coverage drop includes two stretches of 8 and 16 mononucleotides (C and G, respectively).
To this purpose, the software MISA and the new bioinformatics tool NTContent developed here (available athttps://github. com/emlec/NTContent) can be extremely useful as prediction tools. In order to monitor any potential bias in SSV-Seq, an internal normalizer is also processed in parallel to the rAAV samples. Composed of a mix of the plasmid vector and other potential residual DNA species (producer cell DNA, helper plasmids), this control enables to visualize and compare the sequencing coverages obtained from the rAAV sample and from the plasmid vector, as shown on Figure  2. A coverage drop in the CAG promoter, which has a local GC percentage higher than 90%, was previously observed using SSV-Seq  (Kondratov et al. , 2017). The same observation has been reported by another group using Fast-Seq, a technique based on Tn5 tagmentation  (Maynard et al.
Acknowledgments We thank all the staff of the  Vector Core (CPV) at the University Hospital of Nantes  (https://umr1089. univnantes. fr/plateaux-technologiques/) for production of the AAV reference vectors and for technical assistance. Illumina sequencing was conducted at the  Genomics core facility of Nantes, France. We are most grateful to the  Genomics and Bioinformatics Core Facility of Nantes (GenoBiRD, Biogenouest)  for its technical support. We particularly thank  Adrien Léger  for his advices regarding the development of bioinformatics tools.
Financial support This research was supported by the  Fondation d'Entreprise Thérapie Génique en Pays de Loire , the  Commissariat Général à l'Investissement (ANR Program, Investissements d'Avenir, Preindustrial Gene Therapy vector consortium) , the  Centre Hospitalier Universitaire (CHU) of Nantes  and the  Institut National de la Santé et de la Recherche Médicale (INSERM).
Confocal Laser-Scanning Microscopy, cell death observation and root architecture measurements Confocal microscopic observations were carried out using the Axio observer Z1 microscope with the LSM 700 scanning module and the ZEN 2010 software (Zeiss). GFP and propidium iodide (PI) were excited using the 488nm argon-ion laser and collected at 500-550 nm (GFP) or 600-656 nm (PI). Excitation of roGFP2 was performed at 488 and 405 nm and a bandpass (BP 490-555 nm) emission filter was used to collect roGFP2 signal. For background subtraction, signal was recovered using a BP 420-480 nm emission filter during excitation at 405 nm. Image analyses and quantifications were performed as previously described  (Schwarzländer et al. , 2008) , using the public domain image analysis program ImageJ 1. 52i (https://imagej.
nih. gov/ij/). For measurement of cell death in roots, seedlings were stained with 100 µg. mL -1 PI (Sigma-Aldrich) for 5 min before imaging. Measurements of RAM length and LRP counting were performed by confocal microscopic observations after PI staining. RAM length was determined by the distance from the quiescent center to the region where endodermis cells were elongating  (Truernit and Haseloff, 2008). Measurements of primary root elongation was performed using the NeuronJ software as described  (Trujillo-Hernandez et al. , 2020). Counting of LR numbers was performed using a stereomicroscope SMZ18 (Nikon, Japan). LR and LRP density were reported for the entire root length.
AGC target was fixed to 3x10 6 ions in MS and 10 5 ions in MS/MS with a maximum ion accumulation time set to 100 ms for MS and MS/MS acquisitions. All other parameters were set as follows: capillary temperature, 250°C; S-lens RF level, 60; isolation window, 2 Da. Acquisitions were performed with Excalibur software (Thermo Fisher Scientific, San José, CA, USA) and to improve the mass accuracy of full-scan MS spectra, a permanent recalibration of the instrument was allowed using polycyclodimethylsiloxane (C 2 H 6 SiO) 6 , m/z 445. 12003 Da) as lock mass. Proteomic data processing: MS raw data of HMW complexes were processed with ProteomeDiscoverer 2. 2 (Thermo Fisher Scientific, San José, CA, USA) and searched against the UniprotKB Arabidopsis database (25/10/2015; 31480 entries) combined with a database of classical contaminants using an in-house Mascot search server (Matrix Science, London, UK; version 2.
4). Mass tolerance was set to 10 ppm for the parent ion mass and 20 mmu for fragments, and up to two missed cleavages per tryptic peptide were allowed. Methionine oxidation, deamidation of asparagine and N-terminal acetylation were taken into account as variable modifications and cysteine carbamidomethylation as a fixed modification. Peptide and protein False Discovery Rates (FDRs) were determined by searching against a reversed decoy database. Peptide identifications were filtered at 1% FDR using the Percolator node. Proteins were filtered at 1% FDR and reverse and contaminants proteins were removed. MS raw data were processed with MaxQuant software (version 1. 6. 0. 13) and with the associated Andromeda search engine using the UniprotKB Arabidopsis database (25/10/2015; 31480 entries) combined with the MaxQuant database of common contaminants.
MaxQuant output was further processed and analyzed using Perseus software (version 1. 6. 07). Protein quantification was performed using the ProteinGroups. txt file after removing reverse and contaminant proteins and the Log2 transformation of quantitative data. Proteins showing at least two unique and razor peptides and having quantitative data in at least three biological replicates were considered for statistical analysis of each temperature condition using a Benjamini-Hochbergcorrected Student's t-test. Proteins showing a FDR < 0. 05 were considered significantly enriched. For both temperature conditions, proteins having quantitative data in biological samples expressing the tagged GRXS17 but not in the five corresponding control samples were treated separately. In this case, only proteins showing quantitative data in three out of the five biological replicates with at least two unique and razor peptides from samples expressing tagged GRXS17 were considered as significant.
ACKNOWLEDGMENTS The authors would like to thank Marion Hamon for technical assistance for mass spectrometry (A-B) GRXS17 holdase activity toward citrate synthase (CS; A) or malate dehydrogenase (MDH; B) was measured by light scattering. CS or MDH were incubated alone (Control), with ovalbumin or with the different GRXS17 proteins. (A) Holo-GRXS17 or apo-GRXS17 in their reduced or oxidized forms were incubated with CS at a 1:1 (0. 5:0. 5 µM) ratio at 43°C. Ovalbumin ratios were 1:1 (0. 5:0. 5 µM). (B) Ratios of apo-GRXS17/MDH were 0. 2:1 (0. 1:0. 5 µM), 0. 5:1 (0. 25:0. 5 µM) and 1:1 (0. 5:0. 5 µM). Ovalbumin ratios were 1:1 (0. 5:0. 5 µM). (C-D) GRXS17 foldase activity was measured by reactivation of the glucose 6phosphate dehydrogenase (G6PDH) activity after denaturation with urea. The zero time point represents the starting activity for the reactivation assay with 100% being the activity before heat denaturation.
We therefore used the tBlastn program (protein query to search against translated genomes) using a previously identified type I toxin CD0956. 2 as a query, as it can overcome annotation and ORF detection defects. We identified gene CD0977. 1 and two other novel putative genes, unannotated on the genome, that we named CD0904. 1 and CD0956. 3. These genes code for small proteins of 47, 35 and 34 amino acids, respectively (Fig. 1A ). Prophages phiCD630-1 and phiCD630-2 share a large region of homology with almost identical sequences, which include a duplication of CD0977. 1 and CD0956. 3 (Fig. 1B ). In contrast, CD0904. 1 is unique to phiCD630-1 and no other toxin gene homolog could be identified within phiCD630-2. Transcript reads were detected in regions of these putative genes by RNA-seq  22  (Fig.
2B ), as previously reported  22. A c-di-GMP-regulated read-through transcript of about 400 nt, as well as a terminated transcript of about 140 nt were detected in this strain by Northern blotting using a riboswitch-specific probe. In contrast, abundance of toxin transcripts was not affected by fluctuations of c-di-GMP levels. We then mapped the transcriptional start (TSS) and termination sites for the genes of the potential RCd11/RCd12-CD0977. 1/CD2889 TA modules by 5'/3'RACE analysis (Fig. S3 , Table  S3 ). The results obtained agreed generally well with the transcript lengths deduced from TSS mapping, RNA-seq and Northern blot. Taken together, these data suggest the presence of two tandem TSS for RCd11, i. e. P1 associated with c-di-GMP-dependent riboswitch and P2 located downstream from the riboswitch (Fig.
Type I TA are prevalent in C. difficile phage genomes Since we identified additional toxin variants of type I TA systems after careful inspection of phiCD630-1 full genome, we decided to re-scan for possible ORFs in every available phage genomes of C. difficile using the permissive algorithm of the NCBI ORFfinder software. ORFs with minimal length of 60 nucleotides as well as nested ORFs were detected. A blastP search against the corresponding proteins allowed the identification of toxin homologs in all C. difficile prophage genomes (functional phages) (Fig. 6 ). Moreover, toxin sequence alignments revealed the high conservation of the hydrophobic N-terminal region, as well as the lysine-rich, positively charged region at the C-terminus. Hence, these data suggest the functionality of the toxins and reinforce their proposed role for phage maintenance and preservation.
ACKNOWLEGEMENTS This work was supported by Agence Nationale de la Recherche ("CloSTARn", ANR-13-JSV3-0005-01 to O. S. ), the Institut Universitaire de France (to O. S. ), the University Paris-Saclay, the Institute for Integrative Biology of the Cell, the Pasteur Institute, the DIM-1HEALTH regional Ile-de-France program (LSP grant no. 164466), the CNRS-RFBR PRC 2019 (grant no. 288426 № 19-54-15003) to O. S. , and a Vernadski fellowship to A. M. We would like to thank Marc Monot for helpful discussions. was introduced into an innocuous location of phiCD630-1, within the intergenic region between CD0946. 1 and CD0947 genes, encoding a hypothetical protein and a putative scaffold protein, respectively. Cultures grown for 24 hrs were plated on erythromycin-containing agar plates and cells that lost the prophage were selectively killed.
4. 2). Residues were coloured according to high hydrophobicity (red) and low hydrophobicity (blue). 630Δerm. The location of primers used to screen for mutants is represented. (B) PCR products amplified using the indicated primers from the parental strain 630Δerm (WT) and the ΔphiCD630-2 strain (Δ). A product of 1,348 bp could be amplified with primers JP528-JP527 if phiCD630-2 had been deleted, whereas a product of 1,339 bp could be amplified with primers JP570-JP527 if phiCD630-2 was still present. difficile phage genomes available in the NCBI database. The Gegenees software (v2. 2. 1) was used to produce the heatmap of genome similarity. Similarity scores are based on a fragmented all-againstall pairwise alignment using BLASTn and the accurate alignment option (fragment size, 200; step size, 100).
The colours reflect the similarity, ranging from low (red) to high (green). Phages were assigned to a genus if they clustered closely to another phage previously described as a member of that genus. The phylogenetic tree is based on the sequence similarity scores from the same wholegenome comparison and was constructed using the neighbour joining method with the SplitsTree4 software (v 4. 13. 1). Phylogenic analysis of toxins is also represented.
Methods: This study investigates the effects of increasing numbers of FADs (aggregation sites) on the movements of tunas, through the comparison of electronic tagging data recorded from 146 individuals tunas (yellowfin tuna, Thunnus albacares, and skipjack tuna, Katsuwonus pelamis) tagged in three instrumented anchored FAD arrays (Mauritius, Oahu-Hawaii and Maldives), that differed according to their distances among neighboring FADs. The effect of increasing inter-FAD distances is studied considering a set of indices (residence times at FADs and absence (travel) times between two visits at FADs) and their trends. Results: When inter-FAD distances decrease, tuna visit more FADs (higher connectivity between FADs), spend less time travelling between FADs and more time associated with them. The trends observed for the absence (travel) times appear to be compatible with a random-search component in the movement behaviour of tunas.
Acoustic tagging database During dedicated acoustic telemetry experiments, the three anchored FAD arrays were instrumented with acoustic receivers which detect the presence of tagged fish within a given detection range. Details of the telemetry experiments are described in Govinden et al. [19] , Robert et al. [41]  and Rodriguez-Tress et al. [42]. The available data sets collected during these experiments consisted of acoustic detections from 49, 92 and 52 tropical tunas tagged in the FAD arrays of Mauritius, Hawaii and the Maldives, respectively. The movements of tunas within the instrumented FAD arrays were monitored for between 38 and 50 days in Mauritius, from 3 months to 1 year in the Maldives and for more than 1 year in Hawaii. The available detection data were grouped into cohorts according to species, size category and FAD array (Additional file 1).
Data analysis: associative and movement behavior For each category, the time that tuna spent at (residence time), or away from (absence time) FADs was calculated. The residence times corresponded to the Continuous Residence Times (CRT)  [5, 12, 37] , which reflected the  duration within which a tagged fish was continuously detected at the same FAD without day-scale (> 24 h) absences and without being detected at any other FAD in the array. The time that tuna spent away from FADs was defined as the Continuous Absence Times (CAT)  [5, 19]. A CAT corresponded to the time interval between two consecutive CRTs. The times spent away from FADs were separated into two categories: (i) absences with returns to the same FAD of origin following periods longer than 24 h or, (ii) movements between different FADs in the array.
Accordingly, the nature of consecutive CRTs (two consecutive visits to the same FAD or at different FADs) resulted in two different types of CATs: CAT return (s) and CAT diff (s). The durations of CRT, CAT return and CAT diff , were compared between FAD arrays for each species-size category, using the data recorded during the full study period for each array. When only two arrays were compared, a Mann-Whitney test  [29]  was performed. Where the three AFAD arrays were compared, a pairwise approach was used and a Kruskal-Wallis test  [28]  applied, followed by a Dunn post hoc test  [15]  with a p-value adjusted by the Holm method  [24]. Finally, the number of return movements (NCAT return ) and the number of movements between different FADs (NCAT diff ), as well as the total number of residences times (NCRT) were also compared between FAD arrays for each species-size category.
All analyses were conducted using the R software (R Core Team 2018 version 3. 4. 4). Mann-Whitney tests were performed with the function "wilcox. test" (twosample) in the "stat" package, Kruskal-Wallis tests were performed with the function "kruskal. test" in the "stat" package and Dunn tests were performed using the function "posthoc. kruskal. dunn. test" in the "PMCMR" package  [39].