dataesr/scientific-paragraphs-categorization huggingface dataset:
Consequently the coverage of the network may increase as some unknowns become elucidated. The results obtained when visually inspecting metabolite libraries in the context of mapping networks highlights the fact that the network structure can be of interest for guiding future annotation. This approach has already been proposed  (Rogers and Girolami, 2005; Silva et al. , 2014)  and could be implemented in the pipeline. This generic pipeline was applied to two chemical libraries for illustration purposes. It could also scale up to repositories storing metabolomics experimental datasets like MetaboLights  (Haug et al. , 2013). The main remaining issue is that there is not for now a standardized way to identify metabolic networks. This issue can be solved in two ways. One option would be to use predefined identifiers for each genome-scale model (like for BioModels).
The drawback is that this requires all metabolic networks to be stored and described in a centralized database. A second option would be, to devise a standardized method of creating a genome-scale model identifier in a similar manner as it has been achieved for compounds with InChIs. Previous work has proposed the use of authors' names and number of genes in the model  (Thiele and Palsson, 2010). Unfortunately this initiative has not been widely adopted, and should maybe be reactivated and enriched to take into account a larger range of information on the network. Finally, providing flexible web services is in the scope of current efforts of the metabolomics community to create data analysis pipelines implemented in generic frameworks like Galaxy. For instance, this approach will be integrated in the Workflow4Metabolomics developed by MetaboHub  (Giacomoni et al.
The important point is that SaaS is not restricted to a single scenario and allows users to create their own way of using the data. Interaction and data exchange processes contribute to consolidate information by cross data enrichment. In fact, the link between MetExplore and PeakForest/GP interaction allows scientists to evaluate the relevance of the whole chemical library for their organisms of interest. The link with a network analysis tool such as MetExplore allows these libraries to be mined in the context of the metabolism. In particular, it can guide analysts in the choice of standards they will have to analyze and store in the database. We propose a scenario where the pipeline is applied to the whole chemical library, but it can also be used at the level of metabolites. For instance, in the next major release of PeakForest, metabolite cards will be enriched by displaying all metabolic networks each metabolite belongs to via an on-the-fly request to MetExplore.
The approach proposed here is generic and could be implemented in other network repositories than MetExplore such as BIGG or BioModels, giving the opportunity to map data on a larger range of metabolic networks. Naming conventions for genome-scale models will be the main bottleneck for this purpose. Whilst we demonstrate the use of this protocol on two chemical libraries, our method is designed to be sufficiently generic so that it be implemented in other libraries (e. g. ,  MassBank, Horai et al. , 2010)  and metabolomics data repositories (e. g. , MetaboLights,  Haug et al. , 2013). Use of standardized metabolite identifiers makes it is possible to apply the proposed protocol to metabolite lists generated by various technological platforms (LC-MS, GC-MS, or NMR), either alone or in combination.
FUNDING This work was supported by the  French Ministry of Research and National Research Agency  as part of the  French MetaboHUB, the national metabolomics and fluxomics infrastructure  (Grant  ANR-INBS-0010 ). YG was supported by the  Wellcome Trust  grant  105614/Z/14/Z. This work was supported by  PhenoMeNal  project,  European Commission 's  Horizon 2020 programme , grant agreement number  654241.
Segmentation and tracking of cells in long-term time-lapse experiments has emerged as a powerful method to understand how tissue shape changes emerge from the complex choreography of constituent cells. However, methods to store and interrogate the large datasets produced by these experiments are not widely available. Furthermore, recently developed methods for relating tissue shape changes to cell dynamics have not yet been widely applied by biologists because of their technical complexity. We therefore developed a database format that stores cellular connectivity and geometry information of deforming epithelial tissues, and computational tools to interrogate it and perform multi-scale analysis of morphogenesis. We provide tutorials for this computational framework, called TissueMiner, and demonstrate its capabilities by comparing cell and tissue dynamics in vein and inter-vein subregions of the Drosophila pupal wing.
Methods for analyzing these core behaviors have been developed independently in several labs  (Blanchard et al. , 2009; Bosveld et al. , 2012; Etournay et al. , 2015; Guirao et al. , 2015). However, these analysis tools have not yet been made available to other users in an easy to use and well-documented form. Here, we propose a generic data layout and a comprehensive and well-documented computational framework called TissueMiner (see Box 1) for the analysis of epithelial dynamics in 2D. It enables biologists and physicists to quantify cell state properties and cell dynamics, their spatial patterns and their time evolution in a fast, easy and flexible way. It also facilitates the comparison of quantities within and between tissues. To make TissueMiner accessible to a novice, we provide tutorials that guide the user through its capabilities in detail and release a workflow that automatically performs most of the analysis and visualization tasks we reported previously for Drosophila pupal wings  (Etournay et al.
, 2015). These tutorials operate using one small example dataset and 3 large wild-type datasets corresponding to the distal wing blade, which we also provide. The code for TissueMiner, along with tutorials and datasets, are publically available (Box 1). We illustrate the utility and power of these tools by performing a more extensive analysis of pupal wing morphogenesis focused on differences in the behavior of vein and inter-vein cells. Wing veins are specified during larval stages, but only become morphologically distinct during prepupal and pupal morphogenesis. During pupal morphogenesis, the dorsal and ventral surfaces of the wing epithelium become apposed to each other on their basal sides, except in the regions that will give rise to veins -here the basal surfaces of dorsal and ventral cells form a lumen.
Early on in development, the vein cells are indistinguishable from their neighbors, but at late stages, vein cells become a different shape. Veins also become narrower, which is assumed to be due to the number of vein cells falling. However, the way in which cells behave to bring about these changes has not been studied in detail. Etournay, Merkel, Popovic, Brandl et al. have now developed a toolkit called TissueMiner that enables users to store large amounts of data about cells and analyze how cells collectively shape an organ. TissueMiner was then used to identify vein cells at late stages of wing development and follow them backward in time to reveal their position at early stages. This showed that veins become narrower and more elongated because the cells that make up the veins shrink more than cells in other regions.
Results We analyze epithelial morphogenesis within TissueMiner in three steps (Figure  1 -figure supplement 1). First, all epithelial cells of the tissue are digitalized (segmented) and automatically tracked over time using the interactive TissueAnalyzer software  (Aigouy et al. , 2010 (Aigouy et al. , , 2016;; Sagner et al. , 2012) , which is included in the TissueMiner framework. This software generates segmented images, referred to as segmentation masks that contain information about cell geometry, cell neighbor topology and cell ancestry, which are essential for the study of morphogenesis  (Aigouy et al. , 2010; Sagner et al. , 2012; Etournay et al. , 2015). Second, we use a TissueMiner automated workflow to extract this information from the images and store it in a relational database.
This workflow also automatically performs most of the visualization steps we describe in this paper (Materials and methods, and Appendix 1). Third, we use TissueMiner's powerful and convenient library of tools for R and Python to query the database to both visualize the data and quantitatively compare cell properties and behaviors between different movies and subregions of the tissue. Time-lapse datasets are rich with information, and one important set of tools that TissueMiner provides is the ability to visualize this information on the tissue. Such type of visualization can reveal interesting spatial and temporal patterns of core cell behaviors and can guide subsequent analyses. This is, however, insufficient for quantitatively comparing regions within the same tissue or even comparing how the tissue behaves across replicates or various conditions.
Therefore, we developed tools to enable the user to define regions of interest, synchronize movies in time, and align all tissues to a common orientation. We then provide tools to easily plot average quantities in different regions or across movies. For each type of measurement, we refer to the tutorials regarding the specific visualization tools we have built (Box 1). Preparing the dataset (TM R-User Manual sections 1. 1 to 1. 5) Before conducting any analysis, the TissueMiner automated workflow reads three configuration files that contain (1) user-defined regions of interest (ROI's), (2) time offsets for movie synchronization, and (3) the rotation angle used to align the tissue to a standard orientation (Figure  1 -figure supplement 1).
Defining regions of interest (howto Video 1) As cellular behaviors may be spatially patterned, one should have the ability to quantify and compare cell dynamics within different ROI's. TissueMiner provides a Fiji macro (draw_n_get_ROIcoord. ijm) to manually define a set of ROI's directly on one given image of the movie. This program manages several lists of ROI's, which the user can create, modify and delete. These lists help maintain the consistency of ROI labels, which is essential for subsequent analysis (Video 1). In addition, defining ROI's of different shapes and following them backwards and forwards in time (Figure  1A-E' ) is a useful method to visualize tissue deformations (Figure  1D-D' , Box 1. TissueMiner can be found on the web-based repository GitHub https:// github. com/mpicbg-scicomp/tissue_miner#about along with its documentation and tutorials.
Several possibilities are offered to the user to run TissueMiner. For beginners we highly recommend the use of the docker, which allows to package an application with its dependencies into a standardized unit for software development (https://www. docker. com/) (Nickoloff, 2015). Using a provided docker image for TissueMiner, users can directly run it without any further setup being required. Additional instructions and examples are detailed in the supplementary information and on GitHub. We also provide one example biological dataset that can be used to run TissueMiner tutorials in R. In addition, we give access to 3 databases corresponding to wild-type pupal movies of the distal wing blade. These datasets are available at https://github. com/mpicbg-scicomp/tissue_miner#datasets along with the processed images.
Tutorials can be found at https://github. com/ mpicbg-scicomp/tissue_miner#documentation. DOI: 10. 7554/eLife. 14334. 003 Video 2). These ROI's can be defined at any frame within the movie. Thus, it is even possible to specify a region based on morphological features that only arise late in the morphogentic process under study, which is true of wing veins for example (see Figure  1E-E' ). ROI definition allows the user to define morphologically relevant regions of interest and compare the behavior of cells in the different regions. By default, TissueMiner generates two regions of interest -raw and whole_tissue -in order to select cell populations by name. The raw ROI corresponds to all segmented and tracked cells. However cells located at the tissue margin may move in and out of the field of view of the microscope lens.
Cell area and elongation (TM R-User Manual sections 2. 2 -2. 5, Pytutorial sections 2. 1 -2. 2) Morphogenesis is often characterized by changes in cell area and elongation. In the TissueMiner workflow, these properties are calculated from the original segmentation masks and stored in the database (Materials and methods). To visualize the evolution of the cell area pattern at the scale of the whole tissue, we map the area values of each individual cell to a gradient color scale (see Figure  2A -A', Video 4). Each cell contour is filled with a color that corresponds to its area. Figure  2A ' shows the pattern of cell areas in the wing at the end pupal wing blade elongation. This visualization scheme reveals that cells in the proximal hinge and in wing veins have a smaller cross-sectional area (blue) at this time.
For example, the coarse-grained elongation nematics shown in Figure  2C , highlight the global alignment of cell elongation in the proximal-distal direction at 22 hAPF. Packing geometry (TM R-User Manual section 2. 6, Py-tutorial section 2. 3) Cells in the wing become progressively more hexagonal during pupal wing morphogenesis  (Classen et al. , 2005). To visualize packing geometry, we map the neighbor number of each cell to a discrete color code (Figure  2D -D', Video 7). This makes changes in packing geometry during morphogenesis immediately obvious (22 and 31 hAPF). Plotting temporal evolution of average cell properties (TM R-User manual sections 3. 3 to 3. 6, Py-tutorial section 3) The visualization tools described above effectively reveal detailed spatial patterns of cell properties.
We therefore devised a method to measure deformation caused by these cellular processes such that they sum to the measured tissue deformation. Tissue deformation can be decomposed into isotropic and anisotropic parts that distinguish changes in area (compression/expansion) from changes in aspect ratio (pure shear, for details see also Materials and methods). The quantities describing area changes are scalar, whereas the quantities describing shear rate in a 2D-network are nematic tensors harboring two distinct components that describe the orientation and magnitude of the shear. Tissue area changes can be calculated based on cell area change and the number of cells gained and lost by divisions and extrusions -information that is all available in the TissueMiner database  (Etournay et al. , 2015).
Validation of tissue deformation measurements using computergenerated cells To test the reliability of TissueMiner in calculating large cell and tissue deformations, we created two computer-generated movies of hexagonal cells sheets (Videos 12, 13). In one movie, we imposed a constant isotropic expansion rate of 3. 50 10 À2 per frame, without any anisotropic deformation. In the second movie, we imposed a constant pure shear along the x-axis with a rate of 1. 75 10 À2 per frame, and without any isotropic expansion. The amounts of isotropic expansion and pure shear have been chosen to be at least 10 times higher than what we measure between subsequent frames of pupal wing movies. We then asked if TissueMiner could quantitatively recapitulate the respectively imposed deformation rates. In each dataset, TissueMiner automatically defines a 'whole_tissue' region of interest that corresponds to a consistent set of cells that are always visible (about 100 cells in the isotropic expansion movie and about 50 cells in the pure shear movie, green labels in Videos 12 and 13).
Discussion Quantitative image analysis of developing epithelia is a powerful approach to understanding morphogenesis, but the tools with which to tame and analyze these complex data have not been widely available in a standard and well-documented format. Here we provide an introduction to the capabilities of TissueMiner and tutorials for its use. TissueMiner provides general strategy to store and analyze large data sets of interwoven objects by combining state of the art tools for data mining. It allows quantification and visualization of epithelial morphogenesis at multiple scales -from individual cells to entire tissues. It provides both a generic database format and a multi-platform toolkit to interrogate and visualize data and quantify cellular contributions to large-scale epithelial deformations.
TissueMiner has been designed to be versatile and expandable. The database format we provide standardizes the organization of tracked cell data and collects all data into a single file per movie. Such a standardized data format facilitates data sharing between different sources, thereby enhancing cross-laboratory reproducibility. As the database stores positional information about cells and cell contacts, as well as cell neighbor topology, it could also be useful for parameterizing simulations of epithelial remodeling by vertex models or other physical network models. The scheme of our relational database is expandable: additional properties of cells, bonds and vertices can be appended to the database without affecting the relationships between tables. As a consequence, our current query tools to interrogate the database remain functional, even if the database is extended with new properties of cells, bonds and vertices.
TissueMiner takes advantage of the advanced graphical capabilities of R and Python to enable the visualization of patterns of deformation and cell state properties directly on the movie images or quantitatively summarized in graphs. In particular, R provides a flexible grammar with which to manipulate tables obtained from the database and to easily plot graphs  (Wickham, 2009; Francois and Wickham, 2015). TissueMiner also offers multiple options for coarse-graining data in space and time through an expandable collection of scripts, which constitutes the TissueMiner library for R or Python. These two easy-to-learn programming languages give TissueMiner its great flexibility to both address general questions of epithelial morphogenesis and project-specific questions, and enable automation, parallelization and customization of user-specific workflows.
A relational database to store the history of cells, their lineage and their constituent bonds and vertices The visualization and quantification of cell dynamics underlying tissue morphogenesis rely on the ability to extract information about cell geometry, cell neighbor topology and cell histories from time-lapse movies  (Aigouy et al. , 2010; Etournay et al. , 2015). We use TissueAnalyzer to segment and track the cell network over time. This results in a series of digital images that contain this information (Figure  6 -figure supplement 1). To facilitate its access and use, we developed tools in the TissueMiner framework to extract and convert this information initially stored in images into a specific database format (see details in appendix 1), which we call 'TM-DB' (schematically outlined in Figure  6A ).
Such additional columns for the TM-DB format are shown in blue in Figure  6 -figure supplement 2. When extracting information from a database using so-called queries, these columns serve as bridges connecting the information stored about related items. In essence, this structure creates a generic relational model to represent complex cell tracking data in 2D. In practice, the data for each movie is stored in a separate SQLite database file. Since all movie files are stored using the same database structure, automated data mining and visualization are greatly facilitated. For the same reason, usage of the TissueMiner database format encourages exchange of both movie data and analysis tools.
An automated workflow compliant with high performance computing platforms To help the user to perform complex tissue morphogenesis analysis, we developed an automated pipeline that uses the tracked data from TissueAnalyzer as an input to build the database and perform all downstream analyses described above. To do so, we use the snakemake workflow engine developed by  Koster and Rahmann (Koster and Rahmann, 2012). This engine channels the different processing steps into a well-formed workflow graph. Snakemake automatically determines the execution order, provides means for error recovery and job control, and supports High Performance Computing (HPC) environments. By using snakemake we enable the user to easily run and monitor TissueMiner, while maintaining a proper decoupling of tools as independent executables.
Practically, the user defines a workflow definition file in which processing steps are defined as a set of execution rules, namely a list of scripts to be run along with required input(s) and expected output(s). Snakemake automatically builds a directed graph from which the execution order of processing steps is inferred. If only one branch of the graph needs to be run, the engine will ensure that all input data are present and will automatically run upstream steps if necessary. This engine also provides the possibility to visualize a directed acyclic execution dependency and execution state graph (DAG) for a given workflow (see Figure  7 ). One major advantage of a workflow engine such as snakemake is that it can run the workflow on various architectures -from single-core workstations to multi-core servers and clusters -without the need to modify the rules, thereby facilitating reproducible research.
To simplify the TissueMiner The snakemake engine can generate a directed acyclic graph (DAG) where we show an example here. This graph represents both the execution dependency (grey arrows) and the execution state of the workflow (solid or dashed line). Each box corresponds to an execution rule, namely a program to be run along with required input(s) and expected output(s). This DAG can be generated at any time when running the workflow (see documentation). Solid lines indicate the rules that have not been executed yet, whereas dashed lines depict completed jobs. The first rule to be executed is called 'prepare_movie': it prepares the tracked images from TissueAnalyzer to be converted by the parser into tables of values containing all necessary entities along with their properties ('parse_tables' rule).
Then the 'make_db' rule is executed for building the database. Following the grey arrows can one navigate into the next steps of the workflow. The 'roi_tracking' rule filters out cells in contact to margin cells including user-defined regions of interest, and the 'roi_movie' rule allows us to visualize regions of interest over time. The 'deformation_movies' and 'db_elongation_movies' rules generate annotated movies to visualize the deformation of the tissue and the cell state properties (area, elongation). The 'four_way' rule detects four-way vertices and performs basic statistics on vertices. The 'tri_create' rule performs the triangulation of the network for further shear calculation and visualization ('shear_calculate' and 'shear_movies'). It also enables triangle tracking and mapping to each type of cell event ('tri_categorize').
14334. 030 installation procedure, we provide a pre-configured system to be loaded in the docker software available at http://docker. com. The TissueMiner docker image can be run without any setup using provided example data or custom user data as detailed out on the TissueMiner GitHub project page. More advanced users can use TissueMiner directly from the command-line with or without snakemake and can thus perform simultaneous analyses of multiple movies. A user-friendly data-mining library to easily collect information for comparing multiple datasets After applying our automated workflow to different movies, the results can be easily compared using a collection of command-line tools written in R and Python. These tools aggregate different experiments for plotting and performing comparative analysis.