dataesr/scientific-paragraphs-categorization huggingface dataset:
The instructions below show how to use Alien_Hunter and SeqWord Gene Island Sniffer software for GEI prediction (see Note 15). alien_hunter TargetGenome. fasta AlienHunterOutputFile -a 6. Alien_Hunter will generate three result files: two feature tables and a plot file. Look for the ". sco" file, which indicates coordinates and scores for the predicted genomic islands. 8. For each genome, a ". out" file with the results will be written to the output folder inside the software directories. 9. SeqWord Gene Island Sniffer 1. 0 generates a circular genomic map showing GEI location and nucleotide composition using different metrics. This map is useful to visualize the detected island, and must be compared with the GIPSy results for highquality GEI annotation.
ICE and Integron Analysis and Prediction ICEs are self-transmissible integrative elements found in both Gram-positive and Gram-negative bacteria encoding conjugationrelated genes  [27] , whereas integrons show a diverse genetic organization, containing a number of combinations of gene cassettes, mostly related to antibiotic resistance and often associated with transposons and conjugative plasmids  [28, 104, 105]. GEI prediction software, such as Alien_Hunter, may detect these anomalous regions; however, they are not capable of distinguishing GEIs, ICEs, and integrons. Therefore, in addition to the IS, prophage, and GEI predictions, a complete MGE analysis should include searches against the ICE and integron reference databases, such as ICEberg (http://db-mml. sjtu. edu. cn/ICEberg), and INTE-GRALL (http://integrall.
Visualizing Prediction Results in Circular and Linear Genomic Maps GView is a program for visualizing genomes. Although not developed specifically for MGE analyses, it can be used for viewing the results obtained in the previous steps in a broader genomic context, either for complete genomes or single contigs. It is also possible to visualize the previous results with the Artemis genome browser, which additionally allows users to perform manual curation of annotations, thus making it very useful for refining automated predictions (see Note 16). However, most MGE prediction software does not generate results in formats compatible with these programs. To integrate predictions with these viewers, we have developed Mobile Genetic Element Coordinates Extractor (MoGECE), which extracts mobile genetic element locations in prokaryotic genomes from outputs created by the MGE analysis software described in the previous section and generates files compatible with GView and Artemis.
Formatting Prediction Results with MoGECE 1. 0. 1 1. Download the MoGECE package from https://www. github. com/danillo-alvarenga/mogece/, extract it and move it to a folder in your software directory. 2. Enter a terminal from the MoGECE folder and add it to your environment: echo 'export PATH¼$PATH:'$(pwd) >> ~/. bashrc; source ~/. bashrc 3. Go to the directories containing the relevant output files described in the previous subsections and run the program indicating the output file, the program in which this file was created, and which program visualization should be used. For Alien_Hunter, for example, issue the following command to the terminal in the outputs directory: MoGECE. py -f alienhunter. sco -l -a -g 4. MoGECE will output either a feature table for visualization in Artemis, a comma-separated values table for GView, or both according to the parameters you have chosen when running the program.
The files produced will be named according to the program output in which they were based, e. g. , an Alien_Hunter output file used as input for MoGECE with the -a and -g parameters will be processed and two files will be created: alienhunter. csv and alienhunter. ft. 3. In the "Options" menu, select the bacterial and plant plastid Genetic Code Table  11. 4. In the "File" menu, select "Open" and open the target genome in the GenBank file format. 5. In the genome annotation window, go to the "File" menu and click on "Read an Entry. " Navigate to the directory containing the feature table created with MoGECE, change file types to "all files," and select the table (see Note 17). Figures  2  and 3  show examples of analyses generated by this protocol. These circular genomic maps were produced by the GView software, and are used as a template for interpreting the results obtained.
The right panel of Fig. 3  shows that Sulfolobus solfataricus carries large numbers of ISs and GEIs, and few prophages. The low number of prophages might be due to the presence of at least three CRISPR loci detected by MinCED (orange bars on the left panel). The CDS analysis of the surrounding genes of the CRISPR loci indicate the presence of Cas1 to 7 ORFs (CRISPR-associated genes), suggesting that this archaea may have had a long history of battle against phages. Indeed, a comparative analysis with the use of the CRISPRdb indicates that this genus carries several CRISPR loci. The other regions marked on the right panel follow the pattern shown in previous figure examples. Overall, these results emphasize that multiple predictors and databases must be used for a proper MGE analysis (see  Note 20) , and that manual expert annotation is a mandatory second annotation step.
Non-autonomous and Partial Elements The occurrence of non-autonomous MGEs in Prokaryotic genomes is quite common. These mainly include: (a) MITEs (Miniature Inverted-Repeat Transposable Elements), composed of the inverted repeats of an IS or Tn and some short, interstitial, noncoding DNA, capable of impacting prokaryotic genome evolution by acting as substrates for genome rearrangements and by modifying gene expression by creating new promoters; (b) partial MGEs representing relics of prophages, ISs, and GEIs, mostly generated by genome decay or other recombination events. In spite of their abundance, the predictors and software used in this guide are not able to detect MITEs, or to classify complete and partial MGEs. This level of MGE analysis requires manual and expert annotation of the predicted results, mainly based on BLAST analysis against the MGEs databases, such as ISfinder, PHAST, ACLAME, ICEberg and INTEGRAL, and literature search (see Note 23).
Notes 1. Some programs can recognize files only when they have certain extensions. If you are running into file recognition errors, try changing the extension. The GenBank flatfile format (. gbff) is virtually identical to the previous GenBank format (. gb or. gbk), but most prediction software only recognizes the ". gbk" or ". gb" file extensions. In such circumstances, the ". gbff" extension should be renamed ". gbk. " 2. For bacteria with multiple chromosomes, it is better to analyze each chromosome separately instead of grouping them into a multi-fasta or multi-gb file. This is due to the different ways that programs deal with such files, which might result in errors when comparing their outputs. 3. Although some software has been developed for Linux distributions and OS X, they may work on other Unix-like systems such as BSD and Solaris as well.
Alternatively, most Python packages may be installed using the pip or easy_install managers, and Perl packages are also available from cpan. 7. Some programs include a compiled version of the BLAST software that may run into library issues. If you encounter this issue, remove the embedded BLAST executable and create a symbolic link to your system's executable by starting a terminal on the executable directory and typing the following command: rm blastn; ln -s $(which blastn) blastn 8. The general IS annotation pipelines do not consider the ISCR (Insertion Sequence with Common Regions) elements. For that level of analysis and annotation it is necessary to follow the standards proposed by Toleman and collaborators  [7]  and to use the ISCR Database (http://medicine. cf. ac. uk/infectimmun/research/infection/antibacterial-agents/iscrelements/).
After drawing the Gview map, investigate the IS genomic context of the gene to identify possible ISCRs. 9. Avoid using spaces in file or directory names as some commands and programs (such as Alien_Hunter) may have trouble recognizing them. Suppress spaces or replace them with underlines. 10. New ISs should be submitted to ISfinder for attribution of a standard name and inclusion in the database. 11. Since many prophages are inserted near tRNA/tmRNA genes, results from tRNA predictor software compared by BLASTn with the complexity filter disabled against the given genome should be used to improve the detection of insertion sites and to define the prophage borders. This can be done with tRNAScan-SE  [106]  or Aragorn  [107]. 12. For prophage prediction refinement, further comparisons can be made by using the PHAST (http://phast.
wishartlab. com) and ACLAME (http://aclame. ulb. ac. be/) databases. 13. After compiling a program and exporting its path to the global environment, it is necessary to reload bash, the default Ubuntu terminal interpreter, so that its location can be updated. If the exported program path is no longer accessible after closing a terminal emulator window, reload bash by typing the command source ~/. bashrc in a terminal or by restarting your computer. 14. If you have trouble starting Docker, first make sure that the service is still running by typing sudo service docker start in the terminal. 15. For GEI prediction refinement, further comparisons can be made by using the SeqWord Genome Browser (http://www. bi. up. ac. za/SeqWord/mhhapplet. php) and IslandViewer3 (http://www. pathogenomics. sfu.
Download the MinCED source code from https://github. com/ctSkennerton/minced. Go to the Downloads folder in the file manager, right-click on the downloaded file and select "extract here. " Move the extracted folder to the directory where you wish to keep software and databases files. 4. Go to the folder where a makefile can be found, right-click on a blank space, and select "open in terminal. " In the opened terminal, compile the executable files: make 5. Export the MinCED directory by issuing the following command: echo 'export PATH¼$PATH:'$(pwd) >> ~/. bashrc; source ~/. bashrc 6. Still in the terminal, change to your genomes directory: cd /home/bioinfo/genomes 7. Run MinCED on your target genome: minced -gff TargetGenome. fasta MinCEDResults. gff 8. Repeat the previous step for each genome you wish to analyze.
GEI Prediction with Alien_Hunter 1. 7 1. Download the Alien_Hunter source code from http://www. sanger. ac. uk/science/tools/alien-hunter, extract the package and move it to your software folder. 2. Go to the folder where an alien_hunter file can be found, rightclick on a blank space, and select "open in terminal. " In the opened terminal, add the directory to your global environment variables: echo 'export PATH¼$PATH:'$(pwd) >> ~/. bashrc; source ~/. bashrc 3. Change to your genome directory in the terminal: cd /home/bioinfo/genomes 4. Run Alien_Hunter on your fasta-formatted genome: alien_hunter TargetGenome. fasta AlienHunterOutputFile 5. You may opt to view the resulting prediction file on the Artemis genome browser. To do so, follow the procedure for installing Artemis described in the first step of Subheading 3.
Go to the Downloads folder in the file manager, right-click on the downloaded file and select "extract here. "3. Move the extracted folder to the directory where software and database files will be stored. In this and the following examples, we will use/home/bioinfo/software as the default path for the installed software and/home/bioinfo/genomes for genome files. Replace this path with your own directory paths whenever necessary (see Note 9). 4. Go to the folder where a setup. pyfilecan be found, right-click on a blank space and select "open in terminal. "Inthe opened terminal, install the software:python setup. pybuild; sudo python setup. pyinstall 5. Find the data subdirectory inside the src directory and modify the data. cfgfile to the following: On these examples, we also use TargetGenome as proxy for the genome filename and AnalysisDirectory for the results directory, which should be replaced with the names of your file and directory.
1. 2. 3. 123 Download the MinCED source code from https://github. com/ctSkennerton/minced. Go to the Downloads folder in the file manager, right-click on the downloaded file and select "extract here. " Move the extracted folder to the directory where you wish to keep software and databases files. 4. Go to the folder where a makefile can be found, right-click on a blank space, and select "open in terminal. " In the opened terminal, compile the executable files: make 5. Export the MinCED directory by issuing the following command: echo 'export PATH¼$PATH:'$(pwd) >> ~/. bashrc; source ~/. bashrc 6. Still in the terminal, change to your genomes directory: cd /home/bioinfo/genomes 7. Run MinCED on your target genome: minced -gff TargetGenome. fasta MinCEDResults. gff 8. Repeat the previous step for each genome you wish to analyze.
3. 5. 1 51 GEI Prediction with Alien_Hunter 1. 7 1. Download the Alien_Hunter source code from http://www. sanger. ac. uk/science/tools/alien-hunter, extract the package and move it to your software folder. 2. Go to the folder where an alien_hunter file can be found, rightclick on a blank space, and select "open in terminal. " In the opened terminal, add the directory to your global environment variables: echo 'export PATH¼$PATH:'$(pwd) >> ~/. bashrc; source ~/. bashrc 3. Change to your genome directory in the terminal: cd /home/bioinfo/genomes 4. Run Alien_Hunter on your fasta-formatted genome: alien_hunter TargetGenome. fasta AlienHunterOutputFile 5. You may opt to view the resulting prediction file on the Artemis genome browser. To do so, follow the procedure for installing Artemis described in the first step of Subheading 3.
Open a terminal from your genome folder and run OASIS with the appropriate file and directory names:OASIS -g TargetGenome. gb -o OASISAnalysisDirectory 7. Repeat the previous step for each genome you wish to analyze. 8. OASIS will produce two files -a fasta file containing both nucleotide and amino acid sequences of each detected IS, and a General Feature Format file (". gff" extension) containing information about the predicted insertion sequences. Please note that, unlike ISsaga, OASIS does not classify ISs into families. The data obtained from OASIS must be parsed through ISfinder for this purpose (see Note 10). 3. 3. 2 IS Detection 1. Download the OASIS source code from https://github. com/ with OASIS dgrtwo/OASIS. 2. [BLAST] BLAST_EXE¼/usr/bin/blastall FORMAT_EXE¼/usr/bin/formatdb 6. 3.
For instance, the means by which electrons are donated to the LPMO active site modulate the apparent range of reactivity  28, 35. + a b - L s A A 9 A T a A A 9 A C v A A 9 + -+ - Cell 6 Cell 5 Cell 4 Cell 3 Cell 2 Glc NcLPMO9M MYCTH_92668 CvAA9A LsAA9A MYCTH_112089 PcLPMO9D NcLPMO9F MtLPMO9A PaLPMO9A GH61-2 Ta AA9A NcLPMO9E NcLPMO9C NcLPMO9D GH61-1 NCU00836 PaLPMO9E PaLPMO9H Fig. 1  Sequence similarity between LsAA9A, CvAA9A and TaAA9A, and analysis of their reaction products. a Distance tree of 444 selected AA9 sequences (see Methods). Blue, purple and orange labels designate AA9 enzymes that oxidize the sugar ring at C1, C4 and C1+C4, respectively. See Supplementary Table  1  for protein accession numbers. Unlabelled branches represent AA9 enzymes for which the regioselectivity of oxidation is not available from the literature.
Methods Phylogenetic tree. AA9 is a family with more than 6000 sequences listed in NCBI nr and JGI databases in 2016. Because of high variability in the N-terminal portion of LPMO amino-acid sequences, no significant global alignment of LPMOs can be obtained-thereby limiting global downstream phylogenetic analyses. We chose therefore to extract the highly variable N-terminal half of these sequences (which includes two histidine residues involved in the coordination of the copper atom) for phylogenetic analysis as well as to limit the analysis to sequences that are closely related to each other and to those that have been biochemically characterized in the literature. We reduced the set of AA9 sequences to those that gave BLAST bitscores greater than or equal to a value of 200, using LsAA9A and TaAA9A as queries.
HPAEC was performed on a CarboPac PA1 column (Dionex) with injections of 20 μl and elution at 0. 4 ml min -1. The elution profile was: 0-3 min, 10 mM NaOH (isocratic); 3-6 min, 10 → 1 mM NaOH (linear gradient); 6-19 min, 1 mM NaOH (isocratic); 19-37 min, 45 mM NaOH, 225 mM sodium acetate (isocratic). A pulsed amperometric detector (PAD) with a gold electrode was used. PAD response was calibrated using markers (500 pmol). X-ray crystallography and PDB database searches. All crystallization trials were set up in MRC two-well plates at room temperature using an Oryx-8 robot (Douglas Instrument). Crystals were obtained by sitting-drop vapour diffusion technique in drops of 0. 3-0. 5 μl with a reservoir volume of 100 μl. Pre-incubation with 1-2 mM Cu(II) acetate for 30-60 min was carried out for all crystallization trials.
Crystallization and post-crystallization experimental details are shown in Supplementary Table  4. Crystals were cryocooled in liquid nitrogen and all datasets were collected at cryogenic temperatures (100 K) at either the MX beamlines I911-2/I911-3 at MAX-lab in Lund, Sweden, or at the MX beamlines ID23-1, ID23-2 or ID30-B at ESRF, Grenoble, France (Table  2 ). All crystals were obtained by vapour diffusion technique set up in sitting-drop MRC plates, with a reservoir volume of 100 μl and at room temperature using an Oryx-8 robot (Douglas Instrument). All data were collected at cryogenic temperatures (100 K) after cryocooling the crystals in liquid nitrogen. Oligosaccharide substrates used for soaking were purchased from Megazyme (MLG (G4G4G3G and G4G3G4G), xylotriose (Xyl 3 ), xylotetraose (Xyl 4 ), xylopentaose (Xyl 5 ), xyloglucan heptasaccharide (XXXG), cellotriose (Cell 3 ) or provided by Novozymes A/S (cellopentaose (Cell 5 )).
12. CvAA9A was deglycosylated in 20 mM MES, pH 6. 0, 125 mM NaCl by incubation with ~0. 03 units per mg CvAA9A of endoglycosidase H from (Roche Diagnostics, 11643053001), and then buffer exchanged to 20 mM Na-acetate pH 5. 5. Intergrown crystals were initially obtained in an index screen in conditions of 1. 5-2. 0 M (NH 4 ) 2 SO 4 (and in some cases 0. 1 M NaCl) in pH 6. 5-8. 5 (0. 1 M of either Bis-Tris, HEPES or Tris). The crystals diffracted to 2. 0-3. 5 Å resolution but were multiple. Crystal conditions were optimized in a range of 1. 2 M-2. 6 M (NH 4 ) 2 SO 4 (±0. 1 M NaCl) in pH 6. 5-8. 5, which produced crystal plates suitable for mounting. A data set collected at I911-3 on a crystal grown in 0. 1 M Bis-Tris pH 6. 5, 2. 0 M (NH 4 ) 2 SO 4 could be processed in P2 1 to 2. 5 Å. A preliminary CvAA9A structure with four molecules in the asymmetric unit was solved by Molecular Replacement using MOLREP with modified coordinates of the highresolution structure of LsAA9A (PDB 5ACH), which is 41% identical, as a model and refined isotropically to an R free of 32%.
From another data set (collected on a crystal grown in presence of 0. 1 M NaCl; Supplementary Table  4 ) a structure solved (using the preliminary one) with six molecules in the asymmetric unit could be fully modelled and refined resulting in the complete CvAA9A structure (PDB 5NLT; Table  2 ). The six molecules in the asymmetric unit are very similar (average RMSDs of 0. 08 Å). The density of MeHis1 is less clear in chains C and F, and in particular methylation is not as obvious in all chains. Soaks (with 1. 2 M Cell 3 ) of CvAA9A were also prepared. Data were collected to 2. 1 Å and the electron density showed a Cell 3 molecule, which however was not bound at the active site. Soaks with Cell 6 damaged the crystals. Each data set was processed using XDS  45  (the resolution cutoff was chosen on the basis of a CC½ around 50%) and subsequently scaled using XSCALE.
Refmac5  46  was used for restrained refinement of the structures in which LsAA9A: Xyl 5 was refined anisotropically, while LsAA9A:Xyl 3 was refined anisotropically for protein atoms and isotropically for all other atoms. All other structures were refined isotropically for all atoms. For LsAA9A:G4G4G3G the structure was best modelled by the G4G4G portion of the substrate bound mainly in subsite -1 to +2 (80% occupancy) and with a minor conformation occupying subsite -2 to +1 (20% occupancy). Near subsite, -2 a number of water molecules were modelled with 80% occupancy. Ligands and structures were modelled in COOT  47  and validated using MolProbity (within COOT) and Procheck (CCP4 suite), which reported Ramachandran plots with 99% of residues in allowed regions for all structures. Scaled data statistics and refinement statistics are summarized in Table  2.
To identify potential stacking interactions of the β-face of β-mannose with His, the PDB database was searched with Glyvicinity  48. First all protein/β-mannose interactions within a distance cut-off of 4. 0 Å for structures determined at a resolution better than 3. 0 Å were identified. Among these, only two structures were found where the interactions involved His residues and the pyranose O5. The interactions between the imidazole and the pyranose rings were side by side or almost perpendicular, and thus not comparable with the +1 subsite interactions of the LsAA9A complexes. EPR spectroscopy. Continuous wave (cw) X-band frozen solution EPR spectra of 0. 2-0. 3 mM solution of LsAA9A or CvAA9A (in 10% v/v glycerol) at pH 6. 0 (50 mM sodium phosphate buffer with or without addition of 200 mM NaCl or 20 mM MES buffer, 200 mM NaCl) and 165 K were acquired on a Bruker EMX spectrometer operating at ~9.
30 GHz, with modulation amplitude of 4 G, modulation frequency 100 kHz and microwave power of 10. 02 mW (three scans). Avicel cellulose, konjac glucomannan, tamarind xyloglucan and birchwood xylan were added to the EPR tube containing the protein as solids. Alternatively, glucomannan and xylan were heated until dissolution (ca. 2 min) to make solubilized 1% (w/v) stock solutions in water, which were then used for addition of excess polysaccharide to LsAA9A. Cellohexaose and xylohexaose were added to the protein solution either from stock solutions in water or as a solid up to 60-or 150-fold excess, respectively. For the experiments in the presence of xylohexaose, additional NaCl was added to the protein alone or the protein:Xyl 6 mixture from a 5 M stock solution. Due to the high amount of protein required by the technique, the data presented are from single EPR experiments, although the spectra with Cell 6 , Xyl 6 and avicel were performed in at least duplicate.
Acknowledgements We thank  MAX-lab, Sweden  and the  European Synchrotron Radiation Facility (ESRF) ,  France , for synchrotron beamtime and assistance. Travel to synchrotrons was supported by the  Danish Ministry of Higher Education and Science through the Instrument Center DANSCATT  and the  European Community 's  Seventh Framework Programme  (FP7/ 2007-2013) under BioStruct-X (grant agreement  283570 ). This work was supported by the  UK Biotechnology and Biological Sciences Research Council  (grant numbers  BB/ L000423/1  to P. D. and P. H. W. , and  BB/L021633/1  to P. H. W. ) and the  Danish Council for Strategic Research  (grant numbers  12-134923  to L. L. L. and  12-134922  to K. S. J. ).
Data availability. Protein Data Bank: Atomic coordinates and structure factors for the reported crystal structures were deposited under accession codes 5NLT (CvAA9A), 5NLS (LsAA9A-Cell 5 ), 5NLR (LsAA9A-G4G4G3G), 5NKW (LsAA9A-GM), 5NLQ (LsAA9A-Xyl 3 ), 5NLP (LsAA9A-Xyl 4 ), 5NLO (LsAA9A-Xyl 5 ) and 5NLN (LsAA9A-Xyl 5 -Cu II ), GenBank: Sequence data for CvAA9A were deposited under accession code KY884985. Raw EPR data are available on request through the Research Data York (DOI: 10. 15124/5810c962-148c-4328-ab92-895e2dae4d3c). The data that support the findings of this study are available from the corresponding author upon request.