dataesr/scientific-paragraphs-categorization huggingface dataset:
Protein abundance level The total protein concentration of the supernatant was determined by the Coomassie blue test  [67]  using the Coo Protein assay Kit (Interchim). The corresponding volume of protein concentration (10 μg/ml) was pipetted, homogenized with 1. 5 ml of acetone/Trichloro-acetic acid (1/4; v/v), stored for 2 h, at -20 °C, and centrifuged at 20000 g for 20 min (4 °C). The resulting pellets were washed with acetone and centrifuged at 20000 g for 20 min (4 °C) twice over before being dried. The pellets were resuspended in a Laemmli buffer and the equivalent of 10 μg of protein was boiled for 2 min (90 °C) and loaded onto a 10 % SDS-PAGE. Migration was stopped once proteins had entered the separating gel. After Colloïdal Blue staining, a sole band was cut. As described elsewhere in the literature  [68] , proteins were digested, either directly (first dataset) or after reduction/alkylation (second dataset), by trypsin and the resulting peptide mixture was analyzed by nano LC MS/MS (Nano Liquid Chromatography Mass spectrometry) on an Ultimate 3000 nanoLC system (Dionex, The Netherlands) coupled either to a nanospray LTQ-Orbitrap XL (ThermoScientific, Bremen, Germany) or a nanospray Q-Exactive (ThermoScientific, Bremen) mass spectrometer.
Ten microliters of peptide digests were loaded onto a 300 μm-inner-diameter × 5 mm C18 PepMapTM trap column (LC Packings) at a flow rate of 30 μL. min -1. The peptides were eluted from the trap column into an analytical 75 mm id × 15 cm C18 Pep-Map column (Dionex, The Netherlands; solvent A = 0. 1 % formic acid in 5 % ACN; solvent B = 0. 1 % formic acid in 80 % ACN)) with a 4-40 % linear gradient of solvent B in 105 min. The separation flow rate was set at 300 nL. min -1. The mass spectrometer operated in positive ion mode at 1. 8 kV. On the Orbitrap, data were acquired in a data-dependent mode alternating an FTMS scan survey over the range m/z 300-1700 and six ion trap MS/MS scans with Collision Induced Dissociation (CID) as activation mode. MS/MS spectra were acquired using a 3 m/z unit ion isolation window and normalized collision energy of 35.
On the Q-Exactive, data were acquired in a datadependent mode alternating a scan survey over the range m/z 300-2000 and 15 MS/MS scans with Higher-energy C-trap Dissociation (HCD) as activation mode. MS/MS spectra were acquired using a 3 m/z unit ion isolation window and normalized collision energy of 25. In both cases, only +2 and +3 charge states were selected for fragmentation. Dynamic exclusion duration was set to 30s. Data were searched by SEQUEST through Proteome Discoverer 1. 4 (Thermo Fisher Scientific Inc. ) against the UniProt Vitis vinifera L. Reference Proteome Set database (version 2014. 07; 29,837 entries). Spectra from peptides higher than 5000 Da or lower than 350 Da were rejected. Datasets were searched with the following parameters: mass accuracy of the mono-isotopic peptide precursor and peptide fragments was set to 10 ppm and 0.
The ethanol was discarded, and precipitated DNA was dried for 20 min at 37°. Finally, DNA was dissolved in 80-100 ml of TE buffer. PCR reactions (50 ml) were performed using 1 ml of extracted DNA solution as template. Primers for PCR gel shift analysis were designed to obtain 150-200 bp fragments surrounding the on-target crRNA sequence (Table  S3 ). Pri-mer3plus software (http://www. bioinformatics. nl/cgi-bin/primer3plus/ primer3plus. cgi) was used to design primers  (Untergasser et al. 2007). PCR fragment shifts were evaluated in 3% agarose TBE gel-electrophoresis. When a shift was detected, the fragment was sequenced. Sequences were aligned by CodonCode Aligner V5. 1. 5 from LI-COR. Alignments were curated manually to find mutations around the PAM sequence of the corresponding on-target genomic sequence.
Off-target screening Regions surrounding potential off-target sites predicted by the CRISPOR software (Table  S1 ) were sequenced in the mutants. Primers flanking the corresponding off-target sequence were designed using Primer3plus software (Table  S2 ). Sequences were aligned by CodonCode Aligner V5. 1. 5 from LI-COR. Alignments were curated manually to detect potential mutations around the PAM sequence of the corresponding off-target sites.
Efficient CRISPR-Cas9 multiple gene targeting The web-based CRISPOR program was used to search and select the crRNA, and to predict potential off-target sites  (Hsu et al. 2013). Using this tool, a unique crRNA that would target two or more PpKAI2L genes at the same time was not found. Hence, a highly specific crRNA was selected for each PpKAI2L gene separately (Table  S1 ). The tracrRNA sequence was fused to each specific crRNA, to generate sgRNA under the control of P. patens U3 or U6 promoters (Figure  S1 ). The PEG-mediated transformation protocol was adapted to deliver a mix of the sgRNA plasmids along with the plasmid containing the S. pyogenes human codon-optimized Cas9 (Sp-hCas9) into P. patens protoplasts (Figure  S2 ). We initially selected five out of the 13 PpKAI2L genes (clade i) as targets, these genes being related most closely to the Arabidopsis KAI2/HTL gene (Lopez-Obando et al.
ACKNOWLEDGMENTS We thank  Andrew Lloyd  for his helpful comments. The research of S. B. and C. R. and the postdoctoral fellowship for M. L. -O. were supported by the  Agence Nationale de la Recherche  (contract  ANR-12-BSV6-004-01 ). F. N. benefits from the support of the  Agence Nationale de la Recherche  (program  ANR-11-BTBR-0001-GENIUS ). The Institut Jean-Pierre Bourgin benefits from the support of the  Labex Saclay Plant Sciences-SPS  ( ANR-10-LABX-0040-SPS ).
Bioinformatic Analysis The B. cenocepacia strain J2315 plasmid pBCJ2315 sequence is available in GenBank under the accession number NC_011003. The RAST (Rapid Annotation Subsystems Technology) program was used to re-annotate the ORFs present in the plasmid sequence  (Overbeek et al. , 2014). PHYRE (Protein Homology/analogy Recognition Engine) was used for the prediction of secondary and 3-dimensional structure of a protein amino acid sequence  (Kelley et al. , 2015). The phylogenetic tree was constructed using MEGA (Molecular Evolutionary Genetics Analysis) software, which employs the Maximum Likelihood method based on the JTT matrix-based model  (Koichiro et al. , 2011). GenScript bioinformatics software was selected to analyze AT content (http://www. genscript. com). The program Scan for Matches was used to localize IRs having at least 5 bp in length and less than two mismatches (http://www.
Bioinformatic Analysis of the Ptw Plasmid A preliminary annotation of the Ptw plasmid sequence for B. cenocepacia J2315 was available  (Holden et al. , 2009). We have used the RAST Bioinformatic Program to obtain a detailed annotation of the ORFs present in the plasmid, based on a comparative study against the NCBI database. As previously reported  (Engledow et al. , 2004; Holden et al. , 2009) , a 45 kb region was found to encode genes with similarity to the F plasmid T4SS genes  (Lawley et al. , 2003). However, the adjacent DNA region to T4SS genes, which included the putative relaxase and coupling protein genes previously described, ptwC and ptwD4, showed higher similarity to the Dtr region of conjugative plasmid R388: PtwC shows 33% of amino acid identity with R388 relaxase TrwC, and 27% with relaxase TraI of the F plasmid.
The MEGA5 program  (Koichiro et al. , 2011)  was used to determine its phylogenetic position in MOB F family (Figure  S1 ), confirming that PtwC belongs to the MOB F family, and R388-TrwC is its closest relative out of the Burkholderia spp homologs. In most conjugative systems, the oriT is located close to the relaxase; accessory nicking proteins are also often encoded in this DNA region. Analysis of the DNA region upstream of ptwB led to the identification of a small ORF of 429 bp (pBCA060) coding for a hypothetical protein with a predicted RHH structure related to other members of the RHH superfamily  (Heidelberg et al. , 2000) , and thus a likely accessory nicking protein of PtwC; we named it PtwA after TrwA, the accessory nicking protein of TrwC, although both proteins do not share significant amino acid identity.
Results are shown in Table  3. While no transconjugants were observed with the F or R46 transfer systems, a low level of mobilization of the Ptw-oriT containing plasmids was observed when the R388 transfer system was present in the donor cell. Transconjugants were analyzed by restriction analysis (data not shown), showing both the mobilizable and helper plasmids as separate entities in the transconjugants (clearly distinguishable by their different copy number), thus ruling out plasmid conduction by cointegrate formation with the conjugative plasmid. Restriction analysis with HindIII and SacI rendered the expected fragments for the helper pSU2007 (32. 5 kb) and mobilizable plasmids pEF022 or pEF031: 2640 bp fragment in all cases (ptw oriT + ptwA + 2/3 ptwB) plus a fragment of 4700 or 8800 bp, respectively.
FUNDING This work was supported by grants  BIO2013-46414-P  from the  Spanish Ministry of Economy and Competitiveness  to ML and  BFU2011-25658  from the  Spanish Ministry of Science and Innovation  to FS. U1047 is supported by  INSERM  and the  Université de Montpellier. AV was recipient of a " Chercheur d'avenir" award from La Region Languedoc-Roussillon , and SB was supported by a doctoral fellowship co-funded by the  Region Languedoc-Roussillon  and INSERM.
The pipeline was generated by scripts written in Python language and entitled MG-Digger. Metagenomes previously found to contain megavirus-like sequences were tested as controls. MG-Digger was able to annotate 100s of metagenome sequences as best matching those of giant viruses. These sequences were most often found to be similar to phycodnavirus or mimivirus sequences, but included reads related to recently available pandoraviruses, Pithovirus sibericum, and faustoviruses. Compared to other tools, MG-Digger combined stand-alone use on Linux or Windows operating systems through a user-friendly interface, implementation of ready-to-use customized metagenome databases and query sequence databases, adjustable parameters for BLAST searches, and creation of output files containing selected reads with best match identification.
, 2012). With next-generation sequencing (NGS) technologies, the time and cost of genome sequencing has fallen dramatically. Big data generated by these new technologies, and thus needing to be stored and analyzed, is considerable, reaching over one Terabp (Tbp) per run  (Shendure and Ji, 2008). Moreover, the number of metagenomic studies conducted each year is growing. Storage and analysis of these data can be difficult due to their size and the time required for analyses, even for powerful computers and softwares. Lots of softwares have been described for analysing metagenomic data including, for instance, Metavir  (Roux et al. , 2014) , metAMOS  (Treangen et al. , 2013) , MG-RAST  (Glass et al. , 2010) , and VIROME 1. However, these tools, although usually sophisticated, have been found to display one or more among the following limitations: availability, installation on our bioserver for stand-alone use on both Linux and Windows operating systems, ease of use, analysis of customized query and target sequence databases, setting of parameters for sequence similarity searches, automation, information provided in output files, and ability to handle large sequences sets.
Because there is growing interest in giant viruses in the field of evolutionary biology and in environmental and clinical virology, it is necessary to systematically and repeatedly search in metagenomes for sequences from these viruses or close relatives. For this purpose, we established a database of ready-to-use metagenomes and a bioinformatics pipeline which detects reads in these metagenomes that are the most similar to sequences from a given set. We used this tool, which we entitled 'MG-Digger' because it allows to dig into metagenomes to identify reads of interest, to detect metagenome sequences matching those from Megavirales representatives.
Type of Metagenomes The database of metagenomes was created with both environmental and human metagenomes. Control metagenomes, required to validate the functionality and effectiveness of our tool, consisted of metagenomes previously described as containing giant virus-related sequence reads. They comprised metagenomes from sewage and human sera  (Loh et al. , 2009) , from the plasma of patients with liver diseases  (Law et al. , 2013)  and from water samples from the Indian Ocean  (Williamson et al. , 2012). In addition, 16 soil metagenomes recently investigated for the presence of giant virus sequences  (Kerepesi and Grolmusz, 2015b)  were analyzed.
Type of Metagenome Files Metagenome sequence files were in various formats, including FASTA, FASTQ, and SRA (for Sequence Read Archive). It was therefore necessary to unify all these formats into a single one so that the sequences of reads (sequencing product of a size ranging between ≈60-400 nucleotides) were themselves in the same format. The SRA of the NCBI stores metagenomes from scientific projects after their compression and incorporates an accession number. The advantage of this format is data compression, up to a factor of five, while conserving sequencing data. SRA files can be converted into FASTA and FASTQ files using the bioinformatics tools developed by NCBI in the SRA tools' package 5.
Genomes of Giant Viruses The sequences used as queries to search for related sequences in metagenomes were nucleotide (genomes and genes) and protein (putative gene products) sequences from the members of the proposed order Megavirales, including asfarviruses, ascoviruses, iridoviruses, poxviruses, phycodnaviruses, mimiviruses, marseilleviruses, pandoraviruses, P. sibericum, faustoviruses, and from the mimivirus virophages. All these sequences are available from the NCBI GenBank sequence databases with the exception of the sequences of five faustoviruses other than Faustovirus strain E12, which were isolated in our laboratory and whose genomes were not available from GenBank at the time of the analysis  (Reteno et al. , 2015). The risk of incorrectly annotating a metagenomic read based on wrong information deposited in the query sequence database was very limited, because most of the sequences of megaviruses and virophages had been analyzed often over the past decade by several teams and sequence sets from giant viruses of amoeba and virophages were manually curated.
MG-Digger Implementation and Configuration We used the Python language, which is widely used in bioinformatics for programming. The advantages of this language include open access, potential multi-platform use (Windows, Unix, MacOS), simple syntax that makes it highly accessible compared to other programming languages, and very complete libraries. A library is a set of functions, which are gathered and made available. The Biopython library, for instance, manipulates biological and bioinformatics data, and is considered a script language. BLAST+  (Camacho et al. , 2009 ) is a similarity search tool for nucleotide or protein sequences developed by the NCBI. BLAST+ runs the Basic Local Alignment Search Tool (BLAST;  Altschul et al. , 1990 ) in a local server with an existing database. This database can be created with the makeblastdb application available in BLAST+, and in this case was built from our megavirus and virophage sequence database.
BLAST+ makes similarity searches possible without internet access and can be used by employing command lines. It can be easily integrated into a pipeline. BLAST+ has the significant advantage of being able to be used on a multiprocessor server, which decreases the time taken for analyses while increasing the number of processors used. Nevertheless, the nr (protein) and nt (nucleotide) sequence databases used by this tool are very large (≈90 Gbp), which requires assigning ≈105 GB for them. In addition, these databases must be periodically updated to analyze the latest sequences published.
Giant Virus Database The nucleotide (genes and genomes) and protein (putative gene products) sequences were obtained for a total of 93 Megavirales members and five virophages (Supplementary Table  S2 ). The Megavirales members included amoebal giant viruses with seven mimiviruses, three marseilleviruses, two pandoraviruses, P. sibericum, and six faustoviruses. This giant viral sequences database can be completed and updated at any time to include newly described genomes or partial sequences.
Bioinformatics Pipeline Operation The pipeline dedicated to the search for giant virus-related sequences in metagenomes comprises several scripts written in Python language and include independent modules (Figure  1 ). These modules automatically operate successively, without the need for any user intervention. Alternatively, the user can launch a single module to perform only part of the analysis. The type of BLAST analysis performed by the pipeline can be chosen, depending on the nature of the sequence set to study. Hence, BLAST analyses that are launched can use nucleotide or protein queries and target sequences. MG-Digger is freely downloadable for Linux and Windows 8 as a compressed folder. This folder contains a tutorial (README. txt file) with instructions on installing and using MG-Digger.
It also contains an executable file that opens a user-friendly graphic interface (Figure  2 ) and a configuration file that sets the path to the local BLAST database from this interface. This interface was implemented to speed up and make it easier for nonbioinformaticians to use MG-Digger. MG-Digger only requires the additional installation of Biopython on the user's computer 9 , Python 2. 7. x 10 and BLAST+ version 2. 2. 28 or later 11 to operate. It can operate on standard desk computers, as the minimum requirements are a 1 GHz 32-or 64-bit processor, 1 GB of RAM and 120 Go of hard disk space. For easier access for our research laboratory community and faster processing, MG-Digger has been installed on the URMITE bioserver. In short, the operation of the pipeline includes three main steps (Figure  1 ).
In the second step, a metagenome database is created and metagenomic reads that show significant similarity with sequences from giant viruses are retrieved by a tBLASTn search when protein sequences are used as query, or a BLASTn search when genes or genomes are used as query, using BLAST+  (Camacho et al. , 2009). In the third step, these reads are tested by BLAST against the GenBank nr or nt sequence database [plus any additional sequences; here, the sequences of five unpublished faustoviruses obtained in our laboratory were added (see Supplementary Table  S2 )]. Such reciprocal BLAST hit strategies are widely used for the identification of orthologs  (Jordan et al. , 2002; Li et al. , 2003). Reads are considered as related to giant viruses or virophages if their best 'hit' is a giant virus or virophage sequence (default significance threshold being an e-value of 10 -5 ).
The output of this pipeline consists of four simple files: first, the raw BLAST output tab-delimited file for reads matching a query sequence; second, a BLAST output file for metagenomic reads for which the best hit is possibly a 8 http://www. mediterranee-infection. com/index. php 9 http://biopython. org/wiki/Download 10 https://www. python. org/downloads/ 11 ftp://ftp. ncbi. nlm. nih. gov/blast/executables/blast+/ query sequence; third, the FASTA file of reads for which the best BLAST hit is possibly a query sequence; and fourth, a file with analyses data that includes metagenome identification, numbers of metagenomic reads processed, extracted and found to have as best hit one or several query sequences, the identification of these query sequences, the duration of analyses and number of central processing units (CPU) used.
Metagenome Database A total of 1,045 metagenomes were collected and stored on our server in FASTA format after having been processed with MG-Digger. They were generated from 227 environmental samples from 21 studies and 818 human samples from 17 studies (Supplementary Table  S1 ). The total size of the metagenome database corresponded to approximately 1 Tbp, and was comprised of files whose total sequence lengths ranged from 5 Mbp to 12 Gbp per study. These files were prepared for further analysis by MG-Digger and their size had been reduced by MG-Digger. For instance, for control metagenomes, the amount of sequences was reduced by 53%, decreasing from 20. 2 to 9. 6 Gbp.
(2009) , who only searched for asfarvirus-like sequence reads using BLASTx. In the metagenome described by  Law et al. (2013)  obtained from plasma samples from humans with liver disease, the number of reads showing a significant similarity with giant virus sequences was 1,706 out of 42,706,883 (0. 004%; Figure  3B ); in this study, BLASTx search against the GenBank nr database was performed with an e-value cutoff of 1e -5 after assembly of reads. The best hit for these 1,706 reads was a phycodnavirus in 453 cases, a mimivirus in 399 cases, a poxvirus in 372 cases, an iridovirus in 326 cases, a faustovirus in 54 cases, an asfarvirus in 27 cases, a pandoravirus in 20 cases, a marseillevirus in 17 cases, an ascovirus in 14 cases, P. sibericum in six cases, and virophages in 18 cases. The predominance of phycodnavirus-related reads found here is consistent with findings by Law et al.
sibericum in 19 cases, a faustovirus in 11 cases, a pandoravirus in six cases, a marseillevirus in two cases and a poxvirus in two cases. These results are somewhat similar to those reported by  Williamson et al. (2012)  using APIS (Automated Phylogenetic Inference System) and BLASTp as tools, which showed a predominance of phycodnavirus-related reads followed by mimivirus-related reads. Regarding the analysis we conducted using Metavir 2, we downloaded the file from the Metavir website 12  that contains the reads identified by this software using BLASTx comparison with the NCBI Refseq complete viral genomes protein sequences database (with 1e -3 and 50 as e-value and bit-score thresholds, respectively). We then selected reads best matching megaviruses or virophages by checking the identification of these best matches in GenBank using their gi, and found 26,506 reads (Figure  4A ).
Finally, we performed a BLASTx search (e-value threshold,   Loh et al. , 2009) , plasma from patients with liver diseases (B;  Law et al. , 2013)  and water from the Indian Ocean (C;  Williamson et al. , 2012). 1e -3) against the NCBI GenBank non-redundant protein sequence database (nr; same version available in November 2015 as that used with MG-Digger) to check whether best hits were megaviruses or virophages, and we found only 1,031 reads that fulfilled this criterion. Of these reads, 473, 447, 32, 19, 9, 5, 2, and 2 had as best hits phycodnaviruses, mimiviruses, iridoviruses, Pithovirus sibericum, faustoviruses, pandoraviruses, marseilleviruses, and poxviruses, and 42 reads had as best hits virophages (Figures  4A, B ). MG-Digger detected all the 1,031 reads detected by Metavir 2 as having a Megavirales member or virophage sequence as the best hit.
In addition, MG-Digger identified 79 additional reads to the set recovered by Metavir 2, i. e. , 8% more; best matches for these reads were related to phycodnaviruses (n = 41), mimiviruses (15), virophages (20), faustoviruses (2), and pandoraviruses (1). This difference may be due to different strategies for selecting the reads that match giant viruses and virophages (BLASTx for Metavir 2 and tBLASTn for MG-Digger) or to differences in viral set used (the RefSeq viral protein sequences database for Metavir 2 and a customized database of megaviruses and virophages for MG-Digger). Overall, the proportion of metagenomic reads identified as having a megavirus or a virophage as best BLAST hit tended to be higher using MG-Digger than Metavir 2 (p = 0. 09). Nevertheless, both tools showed an excellent agreement (k = 0.