dataesr/scientific-paragraphs-categorization huggingface dataset:
The LAST tool was applied to produce fast and sensitive whole-genome alignments. We screened for best one-to-one alignments using custom scripts removing repetitive and suboptimal alignments. Blocks of shared collinearity were constructed using the BlockDis-playSatsuma script from the Satsuma v1. 17 package  57. Blocks of collinearity were further combined allowing unaligned regions of up to 100 kb and visualized in the sea bass genome browser. Phylogenetic reconstruction. Twenty publicly available genomes were downloaded and formatted as SPALN v2. 12 databases. The Ensembl proteins from O. niloticus were used to predict genes in all species by homology using SPALN v2. 12. Predicted proteins for each species were filtered to retain only the bestscoring hit to O. niloticus, showing nearly complete coverage and a minimum alignment of 60% of amino-acid residues to an O.
niloticus protein. We only retained 621 1:1 orthologous proteins that were found in all 20 genomes. Multiple alignments computed for each protein using ClustalW2. 1 were then concatenated and Prottest used to determine the best amino-acid substitution model (JTT I þ G) and phyML used to build phylogenetic trees. Other phylogenetic reconstruction methods were also tested (Neighbor Joining, FastTreeMP, MrBayes) and in all trees the nodes were highly supported and only a single difference in tree topology appeared between NJ and the other methods. Visualization of the trees was carried out in TreeDyn. Enrichment pathway analysis. Chromosome gene sets were tested for pathway enrichments in comparison with the whole-genome gene set. Sea bass genes were queried against KEGG orthology (KO) database using KOBAS  58.
KO terms of the annotated genes within each sea bass chromosome were used to identify statistically enriched related pathways in chromosomal regions after applying the FDR correction. Detection of gene synteny and collinearity. MCScanX's algorithm was used to perform synteny and collinearity detection across the genome of different species, as well as within the sea bass genome to search for collinear blocks indicative of duplication events and gene family expansions. We used the fraction of collinear genes as a metric to identify collinear blocks. Circular genome representations were created using Circos  59. Detection of duplicated genes. Sea bass-specific gene duplications were detected using a pipeline that interrogates the Ensembl database of orthology groups Compara, using genes from eight teleost genomes to build Compara gene trees (D.
The tree topology was used as user tree option in the analysis of branchand site-specific codon evolution, which was implemented in the Data Monkey web server (http://www. datamonkey. org/). Branch Site REL  63  , a branch-specific test for positive selection was used to assess the presence of significantly divergent branches in the gene tree. A mixed effects model of evolution  64  test was used to assess the presence of codons/positions under positive selection. RAD sequencing, SNP discovery and genotyping. Wild sea bass individuals were sampled from both the Atlantic Ocean (n ¼ 50) and the western Mediterranean Sea (n ¼ 50), where two distinct lineages have been documented for D. labrax 4 (Supplementary Table  9 ). Three individuals from the closely related spotted sea bass D. punctatus were sampled in a Tunisian lagoon and used as an outgroup.
Population genetic analyses. Genomic patterns of nucleotide diversity and genetic differentiation were computed in Stacks v1. 07 as the weighted average of p and F ST in 150-kb windows  65. We used a custom script to calculate the mean proportion of fixed differences per bp (d f ) between D. labrax and D. punctatus in 150-kb windows. SNPs were annotated to exonic, intronic or intergenic regions using SNPdat 67 , and the mean expected heterozygosity in different regions was calculated with VCFtools v0. 1. 11 (ref. 68). Variable recombination rates along each chromosome (r ¼ 4N e r per kb) were estimated using a Bayesian reversible-jump MCMC scheme under the crossingover model of interval in LDhat 69 , with 10 million iterations and 5 million burnins. Population structure was examined using a principal component analysis in SNPRelate  70.
Acknowledgements This work was initiated during the  MARINE GENOMICS EUROPE  project  EU-FP6 505403  and was supported by grants from the  Max Planck Society  ( MPG  for genome sequencing, assembly and annotation),  LIFECYCLE EU-FP7 222719  and  BMBF-01GS0805  to R. R. , and the  French ANR  grants  LABRAD-SEQ 11-PDOC-009-01  to P. -A. G. and  REGULBASS 09-GENM-003  to B. G. M. T. received a fellowship from the  MPG , H. K. was partly financed by  LIFECYCLE EU-FP7 222719  and F. K. by the  BMBF-01GS0805  grant. L. B. was supported by the grant  Progetto 12/MI/2004  from the  Veneto Region. R. S. T. M. and B. L. were in receipt of fellowships  SFRH/BPD/66742/2009  and  SFRH/BPD/89889/2012  from the  Foundation for Science and Technology of Portugal.
Target Identification of ACE4 Aptamer In order to elucidate what could be the target of the ACE4 aptamer, its affinity was evaluated on MCF-7 cells pre-treated with trypsin or proteinase K. Both treatments completely abolished the binding of the aptamer suggesting its target could be a membrane protein (data not shown). Different protocols have been used to isolate and identify the target protein of aptamers selected using cell-SELEX  [34] [35] [36]. Here, we used a slightly modified protocol previously described by Berezovski et al  [38]  (Fig. 2A ). The ACE4 aptamer was biotinylated at the 39-end before incubation with MCF-7 cells. The cells strongly bound to the aptamer were collected at the surface of magnetic streptavidin beads before mild lysis in a solution containing 0. 1% triton. After several washings, the proteins bound to the aptamers were eluted with urea, separated by SDS-PAGE electrophoresis, and visualized after silver staining.
Each sample were gel purified, mixed at equimolar concentration and then sequenced onto a single lane of the microfluidic cell using 80 sequencing cycles in single read. Millions sequences per round of cell-SELEX were obtained. A quality score filter was used and sequences were sorted thanks to indexing sequences. Finally, around 1 million sequences per round of cell-SELEX were obtained in fastq files. Deep sequencing data were loaded on Galaxy project platform (http:// galaxyproject. org/) and are available at https://usegalaxy. org/u/ nicolas1988/h/rawreadseqetbr under the accession number RawReadSeqETBR. 10,000 sequences were selected randomly per round of cell-SELEX and then aligned. Group of close sequences (few mutations between them) were identified, named and counted.
Binding of Radioactive Aptamers with Purified AnxA2t 1pmole of 59-[ 32 P] labeled ACE4 aptamer and scramble sequence (3610 3 Bq. pmole 21 ) were incubated with different concentrations (0, 2, 5, 7. 5, 10, 12. 5, 25 nM) of a hetero-tetramer form of Annexin A2 (AnxA2t) purified from bovine lung (Interchim, France, ref: A80109B). The incubation was performed in 200 ml of RPMI 1640 in the presence of 3 mg of yeast tRNA during 30 min at room temperature. Then the mixture was passed through a nitrocellulose filter (HAWP 0,45 m, Millipore) to recover oligonucleotides bound to AnxA2t. After extensive washing with RPMI 1640, the filter was dried before analysis and quantification by Phosphorimaging using a STORM apparatus (GE healthcare). Specific binding curve was obtained by subtracting background values obtained with a scramble sequence for every data point.
The camera images of the different source positions were used for the reconstruction of the fluorescent signal according to a procedure that is described in detail elsewhere  [49]. The output of the optical reconstruction was given in 3D matrices of fluorescent signal with a voxel resolution of 0. 6760. 6761 mm 3 (X, Y, Z). Visualization and quantification of the fDOT signal was performed using the Brainvisa medical imaging processing software (http://brainvisa. info/index_f. html) using a previously reported calibration curve  [50]. To determine statistical significance of tumor uptake between ACE4 aptamer and scramble sequence, mean fluorescence values were subjected to unpaired t-test analysis using GraphPad Prism 6 with a confidence interval of 95% (n = 3).
Acknowledgments The authors would like to thank  Dr. Rui Sousa  ( University of Texas, San Antonio ) for his generous gift of a T7Y639F RNA polymerase-expressing plasmid,  Isabelle Janssens  for their valuable technical assistance, as well as  Rosy Favicchio  and  San Francisco Edit  for English editing. The high throughput sequencing was performed using the facilities and expertise of the high-throughput sequencing platform of  IMAGIF (Centre de Recherche de Gif  -www. imagif. cnrs. fr). Liquid chromatography-mass spectrometry (nano-LC-MS/MS) was performed using the Plateforme d'analyses proteomiques '' EDyP-Service '' from  CEA , iRTSV, INSERM  U880 ,  UJF.
This work was supported by grants from the '' Agence Nationale pour la Recherche'' [projects ANR-RNTS TomoFluo3D, ANR-PNANO nanorings and under the frame of Euro-NanoMed  (project  META )]; the  FMT-XCT European program  [Grant agreement no. 201792] and the  European Molecular Imaging Laboratory (EMIL) network  [ EU  contract  LSH-2004-503569 ]. AC was supported by a PhD fellowship [CEA-irtelis]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
When grown on soil, plants were placed in a culture chamber (22 • C, 50% humidity, 16 h light) in individual pots containing a vermiculite-perlite-soil mix (1:1:1, vol/vol/vol, Tref substrate GV01). To measure the growth response during stress, plants were grown on vertical plates containing solid medium with concentrations of mannitol increasing from 0 to 0. 3 M. Roots were monitored every day and photographed, then the root length was measured from digital images using ImageJ software. To assess stress recovery, 8-day-old seedlings were subjected to 24 h of hyperosmotic stress then rinsed with water and placed on soil for 1 week. The first two leaves of at least 30 seedlings per sample were placed flat on moist filter paper then photographed. Leaf area was measured from digital images using ImageJ software.
DATA DEPOSITION Microarray data from this article were deposited in the Gene Expression Omnibus (http://www. ncbi. nlm. nih. gov/geo/) under accession number GSE15063 [bac2-1 mutant transcriptome modification during osmotic stress (Arabidopsis thaliana)] and in CATdb (http://urgv. evry. inra. fr/CATdb/) as Project RS08-05_BAC2 according to "Minimum information about a microarray experiment" standards.
STATISTICAL ANALYSIS OF MICROARRAY DATA Normalization and statistical analysis was based on two dye swaps per comparison (i. e. , four arrays each containing 24,576 GSTs and 384 controls) as described in  Gagnot et al. (2008). The methods are available in the R package "Anapuce" (http://cran. r-project. org/web/packages/anapuce/index. html). First, one normalization per array was performed to remove systematic biases without background subtraction. Next, a global intensity-dependent normalization was performed using the lowess procedures  (Yang and Thorne, 2003)  to correct dye bias. Finally, for each block, the log-ratio median calculated over the values for the entire block was subtracted from each individual log-ratio value to correct for effects on each block, as well as potential print-tip, washing and/or drying effects.
MICROARRAY DATA ANALYSIS The list of differentially expressed genes was analyzed further. Upregulated and downregulated genes were ranked in an Excel spreadsheet according to their expression ratios. The number of genes in each cluster was visualized by using Venny software online (http://bioinfogp. cnb. csic. es/tools/venny/index. html). Gene annotations were found on the TAIR website (www. arabidopis. org) and genes with related functions were grouped together in a table. Gene ontology (GO) term finder software was used to find biological function of genes  (Boyle et al. , 2004)  (http://amigo. geneontology. org/cgi-bin/amigo/term_enrichment).
Urea that accumulates strongly in BAC2-OE lines could be the product of arginine degradation by arginase activity after it has been transported into mitochondria by BAC2. Ornithine and citrulline, two molecules that are products of arginine catabolism, Detailed list of TF genes that are differentially expressed in bac2-1. Negative log2 ratios are shown on a blue background, positive ones on a yellow background. Biological functions of genes were determined by amiGO Gene Ontology Software  (Boyle et al. , 2004). Detailed list of amino acids and arginine metabolism related genes which are differentially expressed in bac2-1. Negative log2 ratios are shown on a blue background, positive ones on a yellow background. Biological functions of genes were determined by amiGO Gene Ontology Software  (Boyle et al.
When plants such as Arabidopsis are grown on Ni deficient soil supplemented with Biological functions of genes were determined by amiGO Gene Ontology Software  (Boyle et al. , 2004). urea, urea accumulates because degradation is slow  (Mérigout et al. , 2008; Witte, 2011; Arkoun et al. , 2012). As urea accumulates in leaves of BAC2-OE lines, the urease enzyme might not be active enough to eliminate the excess urea produced by an increased arginine mitochondrial transport and catabolism. Our data showed that a higher urease activity was detected in the presence of Ni in the medium and consequently, urea content decreased in BAC2-OE plants. Excess accumulation of urea in BAC2-OE, is thus due to both induced arginine catabolism and ineffective urea degradation in limiting Ni experimental conditions.
ACKNOWLEDGMENTS This research was supported by the  CNRS  and the  French Ministry of Sciences and Technology. We thank  Delphine Gey  and  Marie-Laure Martin-Magnette  from  URG-INRA, Evry, France  for help producing the microarray data and  Sylvie Citerne from Plateforme de chimie du végétal at INRA Versailles  for the amino acid quantification. We thank  UPMC Paris  undergraduate students Justine Aussant,  Cécile Brocard ,  Cristina Dominguez-Corchero ,  Guillaume Dubeaux ,  Fiona Henderson ,  Shamsuddin Miah Khoka , and  Victorien Point  for their contribution to this study. This manuscript was edited by  Emendo Bioscience  (info@emendo. co. uk).
Importance of archiving data The importance of the public archiving of scientific data and results is well appreciated by the molecular structural biology community. The Protein Data Bank (PDB)  13  is the oldest surviving electronic archive of biomolecular data, and it now stores over 100,000 atomic models determined by a variety of techniques; the Biological Magnetic Resonance Data Bank (BMRB; http://www. bmrb. wisc. edu/) archives data related to NMR experiments; and the Electron Microscopy Data Bank (EMDB)  14  archives 3D EM reconstructions, including electron tomograms. Since 2003, the PDB has been managed by the Worldwide PDB consortium (wwPDB; http://wwpdb. org/)  15 , which includes the Protein Data Bank in Europe (PDBe; http://pdbe. org/)  16 , the Research Collaboratory for Structural Bioinformatics (RCSB PDB; http://rcsb.
org/), the Protein Data Bank Japan (PDBj; http://pdbj. org/) and the BMRB. Similarly, since 2007 the EMDB has been managed by the EMDataBank (http:// emdatabank. org/) partners PDBe, RCSB PDB and the US National Center for Macromolecular Imaging (NCMI)  17. These professionally and collaboratively managed archives provide mutual integration (for example, describing which PDB entries have been fitted to a particular EMDB map) and also link to other key biological resources such as UniProt  18  , Gene Ontology (GO)  19  and Pfam 20 through the Structure Integration with Function, Taxonomy and Sequences (SIFTS) resource (http://pdbe. org/sifts/)  21. The EMDB was established at the EMBL European Bioinformatics Institute (EMBL-EBI) in 2002 and has become the single global archive for EM-derived 3D reconstructions of biomacromolecules and their complexes, with over 2,400 released maps (http://pdbe.
org/ emstats/; August 2014). Most EMDB entries are based on single-particle experiments, but the EMDB also archives results from other EM techniques such as 2D electron crystallography (~1% of EMDB holdings) and ET (~14%). In part as a result of the workshop and ensuing discussions, the PDBe is developing a pilot archive for raw 2D image data underpinning EM and ET structures in the EMDB (Electron Microscopy Pilot Image Archive (EMPIAR); http://pdbe. org/empiar/). This archive will be invaluable for the development of new validation and data-processing methods as well as for teaching and software testing. In cellular structural biology, huge volumes of imaging data are routinely collected, and the metadata and results are very complex. This has driven the development of databases for imaging data, and there are now several open-source projects providing image-database and repository functionality.
The value of data integration Integration of EMDB and PDB data with each other and with other major biological data resources makes structural data more easily discoverable and accessible to the wider biomedical community and allows nonspecialists to benefit from the rich biological insights afforded by the archived structural data. The cellular imaging techniques described above all have the potential to contribute unique 3D perspectives on the cellular context of biomacromolecules and their complexes. However, for this potential to be fully realized, the relevant data have to be publicly archived, annotated and integrated with other data. At present, there are no dedicated archival resources for most of these imaging techniques. In addition, appreciation of the importance of mandatory deposition (and validation) of experimental data upon publication is not yet as pervasive in the cellular-imaging community as it is in the molecular structural biology community.
It is in this context that PDBe and OME organized discussions on the needs, challenges and opportunities for the archiving, integration and dissemination of data from emerging cellular imaging techniques. The participants included experts on 3D imaging, on scales from molecules to cells, by a range of techniques including X-ray crystallography, macromolecular EM, ET, 3D-SEM, SXT and LM; experts on archiving 3D structural data, including representatives from the EMDataBank, the wwPDB, The Cell: an Image Library (http://www. cellimagelibrary. org/), the JCB DataViewer (http://jcb-dataviewer. rupress. org/) and the Cell Centered Database (http://ccdb. ucsd. edu/); and experts on visualization and segmentation of 3D imaging data. The principle of public archiving of cellular imaging data is broadly supported, but there are major obstacles that have to be addressed, including the sheer complexity of representing metadata from diverse methods.
There are also technical challenges involved in storing and especially in transporting large data sets. Many of the new techniques routinely produce data sets of 10 GB or more. In contrast, the largest data set currently in the EMDB is 5 GB (EMD-2489). Changing the mindset of the cellular-imaging community about the importance of public archiving of its imaging data could be a challenge. Finally, even with public archives in place, the integration of 3D cellular data with molecular structural data and the effective dissemination of structural data, information and insights to nonspecialist users are anything but trivial. The development of the tools and capabilities that can integrate these linked yet different data types should have high priority. To illustrate the benefits of data deposition, curation and integration, we describe molecular and structural resources related to herpes simplex virus (HSV) and show how linking of different data sets could lead to new biological insights and engender hypotheses that could not be derived from the individual data sets.
A pilot archive should be established for SXT and 3D-SEM data Addressing the archiving needs for all the cellular imaging techniques in a single archive is unrealistic and impractical. It would require substantial funding to set up and maintain such an archive, and it would also require a wide range of disparate imaging communities to contribute to, support and promote this effort and to agree on standards, policies, formats, etc. However, establishing a pilot archive focusing on a few targeted techniques would allow for experience to be gained in dealing with the technical challenges and in working with the stakeholder communities to promote data deposition. Depending on the success of such a pilot archive and the future funding landscape, it might be opportune to incorporate the archive into the EMDB and gradually expand it to accommodate other imaging techniques.
Two techniques recommended as the initial focus for such a pilot archive are SXT and 3D-SEM. Each still has a relatively small community that includes many scientists with a background in X-ray crystallography or EM who are aware of the importance of public data archiving. The EMDB data model could be fairly easily adapted to cover SXT and 3D-SEM. Nevertheless, both techniques offer sufficient challenges such as establishing community consensus on formats and deposition requirements, linking with existing molecular archives and handling 3D-SEM data sets, which are typically large (commonly 0. 1-1 TB) compared to those from ET.
Correlative LM data should be archived with the companion EM or ET data In principle, CLEM data could be archived in extant public archives dedicated to LM, with cross-references provided to the accompanying EMDB entries. However, currently, there is no single LM archive that has a stature comparable to that of the EMDB in the EM community. Moreover, users would need to deposit data and provide metadata to two different archives for correlative experiments. It is therefore recommended that the EMDataBank investigate the feasibility of archiving correlative LM data in the EMDB with the provision of metadata descriptors to allow the LM and EM data to be linked. The pilot archive for SXT and 3D-SEM proposed above should be designed to accommodate correlative LM data.
Segmentations with biological annotation are essential for integration of cellular and molecular (structure) data resources Biologically annotated segmentations need to be archived alongside the 3D reconstructions to facilitate the integration of cellular imaging data with other biological data. Most segmentation packages allow for free-text annotation of segmented objects, but this is not sufficient for integration purposes, which require database identifiers (for example, UniProt identifiers) and terms from established ontologies (for example, GO). After an earlier workshop (Data Management Challenges in 3D Electron Microscopy)  28  , the PDBe has been working with members of the EM community to develop a data model that supports biological annotation of segmented objects. The data model is generic and software and method agnostic and could be used to handle segmentations from SXT and 3D-SEM as well.
Tools are needed to make it easy to produce, annotate and deposit segmentations, including format-conversion tools and a tool for interactively annotating segmentations. Currently, Euro-BioImaging is developing a common region-of-interest (ROI) data model (http:// scijava. org/roi-model/) that could be useful for representing segmentations. Integrated multiscale viewers are needed to allow effective dissemination, analysis and knowledge discovery of cellular and molecular structure data. The potential synergy of cellular and molecular structural-biology methods can be realized fully only if data from different archives can be combined and integrated. This requires appropriate crossreferences and common annotation between entries in the various structural and image archives as well as publicly available tools for searching and visualization of the combined holdings of these archives.