High-throughput/high-content microscopy-based screens are powerful tools for functional genomics, yielding intracellular information down to the level of single-cells for thousands of genotypic conditions. However, accessing their data requires specialized knowledge and most often that data is no longer analyzed after initial publication. We describe Mineotaur (http://www.mineotaur.org), a open-source, downloadable web application that allows easy online sharing and interactive visualisation of large screen datasets, facilitating their dissemination and further analysis, and enhancing their impact. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13059-015-0836-5) contains supplementary material, which is available to authorized users

Antal, Bálint

Carazo Salas, Rafael E

Chessel, Anatole

PubMed

High-throughput/high-content microscopy-based screens are powerful tools for functional genomics, yielding intracellular information down to the level of single-cells for thousands of genotypic conditions. However, accessing their data requires specialized knowledge and most often that data is no longer analyzed after initial publication. We describe Mineotaur ( http://www.mineotaur.org ), a open-source, downloadable web application that allows easy online sharing and interactive visualisation of large screen datasets, facilitating their dissemination and further analysis, and enhancing their impact.</p

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                          Antal, B., Chessel, A., & Carazo Salas, R. E. (2015). Mineotaur: a tool forhigh-content microscopy screen sharing and visual analytics. GenomeBiology, 16, [283]. https://doi.org/10.1186/s13059-015-0836-5Publisher's PDF, also known as Version of recordLicense (if available):CC BYLink to published version (if available):10.1186/s13059-015-0836-5Link to publication record in Explore Bristol ResearchPDF-documentThis is the final published version of the article (version of record). It first appeared online via BioMed Central athttps://genomebiology.biomedcentral.com/articles/10.1186/s13059-015-0836-5 . Please refer to any applicableterms of use of the publisher.University of Bristol - Explore Bristol ResearchGeneral rightsThis document is made available in accordance with publisher policies. Please cite only the publishedversion using the reference above. Full terms of use are available:http://www.bristol.ac.uk/pure/about/ebr-termsAntal et al. Genome Biology  (2015) 16:283 DOI 10.1186/s13059-015-0836-5OPEN LETTER Open AccessMineotaur: a tool for high-contentmicroscopy screen sharing and visualanalyticsBálint Antal, Anatole Chessel and Rafael E. Carazo Salas*AbstractHigh-throughput/high-content microscopy-based screens are powerful tools for functional genomics, yieldingintracellular information down to the level of single-cells for thousands of genotypic conditions. However, accessingtheir data requires specialized knowledge and most often that data is no longer analyzed after initial publication. Wedescribe Mineotaur (http://www.mineotaur.org), a open-source, downloadable web application that allows easyonline sharing and interactive visualisation of large screen datasets, facilitating their dissemination and furtheranalysis, and enhancing their impact.Main textDespite groundbreaking discoveries in genomics, the ge-nomes of most organisms remain black boxes with thefunction of the majority of genes and gene products stillunknown. High-throughput/high-content microscopy-based screening (HT/HCS) provides an increasinglypowerful tool to discover and functionally annotategenes and biological pathways, and in less than a decadehas led to several important discoveries, like the systematicgenome-wide identification of genes important for mitosis,endocytosis, the cytoskeleton, and other fundamentalprocesses (Chia et al. [1], Cotta-Ramusino et al. [2],Collinet, C. et al. [3], Neumann et al. [4] Graml et al. [5]).A current limitation of HT/HCS projects is that evenafter they are finalised, just accessing and visualisingtheir output requires specialist expertise in image anddata analysis, limiting their use and accessibility. This,along with the lack of a standardized way to manage andshare the biological big data generated by HT/HCSscreens with the wider community, limit the commu-nity’s capacity to fully exploit the rich, quantitative func-tional genomic information contained in those projectsand thus the return on the investment made in them(Earnshaw [6]).* Correspondence: cre20@cam.ac.ukGenetics Department, University of Cambridge, Downing Street, CambridgeCB2 3EH, UK© 2015 Antal et al. Open Access This article iInternational License (http://creativecommonsreproduction in any medium, provided you gthe Creative Commons license, and indicate if(http://creativecommons.org/publicdomain/zeHere we introduce Mineotaur, a web-based interactivevisual analytics tool we developed to provide an efficientway to share the large (>106 points) amounts of image-derived feature data acquired by HT/HCS, and to allow thescientific community complete access to easily visualizeand inspect HT/HCS data, linking with images when avail-able (detailed documentation of Mineotaur can be accessedat http://docs.mineotaur.org).While there are open source [7] and commercial soft-ware (http://www.tableau.com/, http://www.moleculardevices.com/systems/high-content-imaging/acuityxpress-high-content-informatics-software, http://spotfire.tibco.com/)which allow visual analysis of HT/HCS data, Mineotaur isunique in that it lets scientists publish their screens in astandard way together with a free pre-packaged visual ana-lytical toolkit. Once an instance has been set up the aim isfor end users, for example biologists without deep compu-tational knowledge, to be able to access data with a min-imal investment of time and effort.The pipeline for data sharing and visual analytics usingMineotaur can be seen in Fig. 1. Mineotaur is based ona property graph model (Robinson et al. [8]) to handleHT/HCS screens, which is implemented using Java(http://java.oracle.com), and a graph database, Neo4j(http://www.neo4j.com) (see the Additional file 1: FigureS1, Figure S2, Figure 3 and the Online Methods for de-tails of the data model and implementation). Neo4j is adatabase system built upon the concept of propertys distributed under the terms of the Creative Commons Attribution 4.0.org/licenses/by/4.0/), which permits unrestricted use, distribution, andive appropriate credit to the original author(s) and the source, provide a link tochanges were made. The Creative Commons Public Domain Dedication waiverro/1.0/) applies to the data made available in this article, unless otherwise stated.Fig. 1 Pipeline to share high-throughput/high-content microscopy screens. The acquired images and feature data extracted from them can beused to generate a Mineotaur instance, allowing research groups to share their data in a standard way. The public Mineotaur servers let theinterested members of the scientific community to gain an understanding and confidence of the data by accessing features and images in a fewclicks, generating plots on-the-fly and provides a toolkit for researchers discover new hypotheses using the data and export and sharesupporting findingsAntal et al. Genome Biology  (2015) 16:283 Page 2 of 5graphs, which models all objects of interest (e.g. geno-types/cell lines, genes, images, cells, etc.) as nodes in agraph, and edges in the graph represent a connectionbetween them (e.g. an edge between ‘cell’ nodes and an‘image’ node signify ‘cells extracted from that image’).Each node also stores an arbitrary list of property values(e.g. for each cell, the value of each of the quantitativefeature measurements extracted from it by the computa-tional pipeline) and labels (e.g. a gene identified as a ‘hit’for a given process in a HT/HCS screen can be anno-tated as implicated in that process), providing flexibilityto different data sources and to updates/changes in thedata, which is almost impossible in standard relationaldata models. For convenience, we provide a standardway to generate a graph model and a Mineotaur instancefor any HT/HCS screen simply from a CSV (commaFig. 2 Architecture of the Mineotaur system. The Mineotaur web server canREST. The web server handles the interaction with the graph database conseparated values) file. An example data model for the [5]screen can be seen in Additional file 1: Figure S1, whileAdditional file 1: Figure S4 and Additional file 1: Figure S5shows how example input files for the Mineotaur instancegeneration need to be formulated.The data stored in Mineotaur can be accessed by bothan interactive web-based Graphical User Interface(GUI), which enables intuitive ad-hoc querying and plot-ting of the descriptive information extracted from theHT/HCS microscopy screens in real-time, and program-matically, using a REST interface (see Fig. 2 for thearchitecture of Mineotaur). Thus, the screen data can beexploited by both experimental and computational biol-ogists. An example screen from Mineotaur’s web inter-face is shown in Fig. 3. As a design principle, we wantedto allow users to easily construct complex queries andbe accessed from both a web interface and programatically usingtaining the HT/HCS dataFig. 3 Unique distinguishing features of Mineotaur. a The web interface allows on-the-fly analysis of the data. b Queries are visualized asinteractive plots. c Analytics tools like regression lines can be used to generate new hypotheses about the data. d Subsets of data can be selectedfor in depth analysis. e From a gene-level plot, cell-level information can be reached from a context menu in one click. f Multiple plots can becompared to one another using the plot comparison toolAntal et al. Genome Biology  (2015) 16:283 Page 3 of 5traverse through to raw images or well-establishedgenomic databases in a few clicks. Feature data can beaccessed at multiple levels (e.g. genes and cells), if suit-able data are available. The user can discover multidi-mensional associations between different descriptorsby generating scatter plots with adjustable axes, oranalyze the distribution of single features for certainconditions in the form of (multi)histograms and kerneldensity estimation plots. From scatter plots, the userscan access the raw images if they are available online(for example using Omero [9]) with a single click onthe data points. Furthermore, users can jump from gene-level scatter plots to extracting cell-level knowledge with asingle click.The queries can be transformed by filtering the datapoints at the different levels of the investigated condi-tions (e.g. gene or cells annotations). The queried datacan be further analysed by allowing area selection withina plot, regression line fitting, plot transformation andplot comparison.To ensure the reusability and reproducibility of thedata analysis, Mineotaur users can generate a link allow-ing them to share the plots they generate (including anyfilters applied), as well as export their data to differentformats such as vector graphical format SVG (to exportplots) or a comma separated CSV text file containingthe raw data values, for use with standard spreadsheettools (for an explanation of how to export data seeAntal et al. Genome Biology  (2015) 16:283 Page 4 of 5http://docs.mineotaur.org/en/latest/plot_tools.html#down-load). We also provide a way to enrich publications byallowing users to embed interactive charts from Mineo-taur to web pages (to be used e.g. as an interactive figurein the HTML version of an online material in journals).The querying capabilities of Mineotaur can be seen inAdditional file 2: video S1, while an example post-querycan be seen in Additional file 3: video S2. The code isopen-source and is accessible at https://github.com/antal-balint/Mineotaur/ under GPL license.To demonstrate the capabilities and versatility of thetool, we used data from two published screens. First, agenomic multi-process HT/HCS screen recently pub-lished by our group [5], containing quantitative pheno-typic annotations of hundreds of genes influencing cellshape, microtubules and cell cycle progression in fissionyeast (Schizosaccharomyces pombe). The screen consistsof images from tens of 96-well plates, with each wellcontaining cell populations knocked out for a specificnon-essential S. pombe gene (except for a few wells con-taining positive/negative controls). The data consists of138 000 images, from which 1.7 million cells and 5.5million microtubules were computationally identifiedand quantitated, leading to 131 features extracted fromeach cell. For details on the experimental pipeline andits connection to Mineotaur see Methods and Additionalfiles 1, 2, 3, 4 and 5. Secondly, we also generated a Mine-otaur instance from a subset of an HT/HCS screen in-vestigating the signalling network controlling the Golgiapparatus in human cells [10], which contains 624 featuresfor 1 580 242 cells, imaged from 353 different condi-tions. Additional file 4: video S3 shows the step-by-stepreconstruction of a figure from [5], while the recon-structed figure can be seen in Additional file 1: FigureS6. A demonstration instance for the latter dataset canbe accessed at http://demo.mineotaur.org/.In summary, we have developed Mineotaur, a graphmodel based web application, allowing visual analyticsand sharing of HT/HCS projects amongst the entirecommunity, computational and non-computational alike.We believe the intuitive interface, versatility and scalabil-ity will greatly potentiate the return-on-investment ofpast and future projects in the field, catalyse deep biologicaladvances and open the way to establishing community-wide data and interoperability standards.Materials & methodsThe graph modelBasic conceptsProperty graph: a mathematical graph where each nodesand vertices can also hold tuples of data.Objects of interest: any part of an experiment to beincluded in Mineotaur to be either directly queriedor stored as metadata. Example: Strain, Gene, Cell,Experiment.Grouping object: the main object of interest, the topobject in the graph. Example: Gene, Strain.Descriptive objects: the objects carrying the detaileddata associated with the grouping object. Example: Cell.Please note that in cases where only one layer of experi-mental data is available, the grouping objects can be de-scriptive objects as well.Import dataThe graph model for each Mineotaur instance can begenerated by providing a CSV (comma separated value)file in the following format:First line: column headers. These will serve as theproperty names for their respective object types in thedatabase.Second line: object names. These describe the names ofthe objects of interest to be stored in the database. Theproperty in each column described in the first line willbe associated with the object described here.Third line: property types. These describe the datatypes of the properties for each column. The possiblevalues are: TEXT: the column contains a text. Stored as ametadata. NUMBER: the column contains a number, thus itwill be become a queryable information if stored ina descriptive object. ID: identifier of the object. Multiple ID propertiescan be set to an object. URL: the URL of the resource to be linked to object.Each line after the third provides a descriptive objectinstance.For an example input file, see Additional file 1: Figure S4.To provide annotation for the grouping objects, anadditional input file containing the labels is needed. Thefirst n column describe the IDs for the grouping object.All other columns provide a binary value for a label(1=the grouping object possesses the annotation,0=otherwise). An example label input file can be seenin Additional file 1: Figure S5.Detailed architectureServer side: The server side application is written in Javaand based on the Spring framework. On the server side,the data handling and the business logic behind the ap-plication is separated by design. As a graph database,Neo4J is used in embedded mode. The server handlesincoming HTTP requests for the data, translate it to thedata model and fetches data from the graph database.Antal et al. Genome Biology  (2015) 16:283 Page 5 of 5The data is sent back either as a HTTP response or aJSON file.Client side: The server can be accessed in two ways:by the user interface of the Mineotaur and by REST.The user interface is based on HTML 5 (generatedfrom Thymeleaf templates by the server), Bootstrap andJavascript. The user interface provides 1) an interface forad-hoc querying the data 2) visualizes the response ofthe server 3) provides tools to analyse the data. When aquery is submitted, an asynchronous call to the server issent using AJAX. The server provides a response inJSON format, from which the appropriate plot is ren-dered using the D3 javascript library. The analytic toolkitof the user interface is built using the jQuery library anddesigned as a modular Model-View-Controller (MVC) ap-plication (see Additional file 1: Figure S3 for the architec-ture) using the require.js AMD (Anyschronous ModuleDefinition) framework. The client side consists of the fol-lowing Javascript modules:Controller: handling all events triggered in the userinterface.Context: a data access object containing all relevantinformation for the current sessionUI: all functions to provide an interactive user interfacePlot: functions required for generating the plots usingthe D3 framework.Utilities: common mathematical functions usedthroughout the applicationRESTThe server side can be accessed programmatically fromany programming language or framework capable of hand-ling HTTP requests and responses and JSON (i.e. Java,Python, Matlab, Bash, etc.).Additional filesAdditional file 1: Supplemental Figures S1–S6. (DOCX 1246 kb)Additional file 2: Video S1. Pre-query selection options of Mineotaur.(MOV 1868 kb)Additional file 3: Video S2. Post-query selection options of Mineotaur:a) Plot transformations b) Filtering c) Connecting plots d) Connecting toexternal software and website. (MOV 13448 kb)Additional file 4: Video S3. Reconstruction of Figure 7a from [5].(MOV 1136 kb)Additional file 5: Mineotaur documentation for Release 1.0.1.(PDF 1303 kb)Competing interestsThe authors declare no competing financial interests.Authors’ contributionsRECS, AC and BA conceived/led the project designed the general computationalstrategy and wrote the text. BA carried out all computational work. All authorsread and approved the final manuscript.AcknowledgementsWe thank J. Swedlow, G. Rustici, F. Vaggi, A. Csikasz-Nagy, V. Wood, G.Micklem, and the Carazo-Salas group for help and comments, J. Lawson fordesign of the Mineotaur logo, and J. Swedlow, G. Rustici, T. Walter, and A.Csikász-Nagy for critical reading of the manuscript. This work was supportedby an European Research Council (ERC) Starting Researcher InvestigatorGrant (R.E.C.-S.; SYSGRO), a Biological Sciences Research Council (BBSRC)Responsive Mode grant (R.E.C.-S.; BB/K006320/1) and an Isaac Newton Trustresearch grant (R.E.C.-S.; 10.44(n)).References1. Chia J, Goh G, Racine V, Ng S, Kumar P, Bard F. RNAi screening reveals alarge signaling network controlling the Golgi apparatus in human cells. MolSyst Biol. 2012;8:629.2. Cotta-Ramusino C, McDonald 3rd ER, Hurov K, Sowa ME, Harper JW, ElledgeSJ. A DNA damage response screen identifies RHINO, a 9-1-1 and TopBP1interacting protein required for ATR signaling. Science. 2011;332:1313–7.3. Collinet C, Stöter M, Bradshaw CR, Samusik N, Rink JC, Kenski D, et al.Systems survey of endocytosis by multiparametric image analysis. Nature.2010;464:243.4. Neumann B, Walter T, Hériché JK, Bulkescher J, Erfle H, Conrad C, et al.Phenotypic profiling of the human genome by time-lapse microscopyreveals cell division genes. Nature. 2010;464:721.5. Graml V, Studera X, Lawson JL, Chessel A, Geymonat M, Bortfeld-Miller M,et al. A genomic Multiprocess survey of machineries that control and linkcell shape, microtubule organization, and cell-cycle progression. Dev Cell.2014;31(2):227–39.6. Earnshaw WC. Deducing protein function by forensic integrative cellbiology. PLoS Biology. 2013;11(12):e1001742.7. Jones TR, Kang IH, Wheeler DB, Lindquist RA, Papallo A, Sabatini DM, et al.CellProfiler Analyst: data exploration and analysis software for compleximage-based screens. BMC Bioinformatics. 2008;9:482.8. Robinson I, Webber J, Eifrem E. Graph Databases. O’Reilly Media; USA 2012.9. Allan C, Burel JM, Moore J, Blackburn C, Linkert M, Loynton S, et al. OMERO:flexible, model-driven data management for experimental biology. NatMethods. 2012;9:245–253.10. Chia J, Goh G, Racine V, Ng S, Kumar P, Bard F. Data from: RNAi screeningreveals a large signaling network controlling the Golgi apparatus in humancells. Dryad Digital Repository. 2012. http://dx.doi.org/10.5061/dryad.1m2p3.

Mineotaur:a tool for high-content microscopy screen sharing and visual analytics

Bálint Antal

Anatole Chessel

Rafael E. Carazo Salas

Springer - Publisher Connector

Mineotaur: a tool for high-content microscopy screen sharing and visual analytics

Crossref

Antal et al. Genome Biology  (2015) 16:283 DOI 10.1186/s13059-015-0836-5OPEN LETTER Open AccessMineotaur: a tool for high-contentmicroscopy screen sharing and visualanalyticsBálint Antal, Anatole Chessel and Rafael E. Carazo Salas*AbstractHigh-throughput/high-content microscopy-based screens are powerful tools for functional genomics, yieldingintracellular information down to the level of single-cells for thousands of genotypic conditions. However, accessingtheir data requires specialized knowledge and most often that data is no longer analyzed after initial publication. Wedescribe Mineotaur (http://www.mineotaur.org), a open-source, downloadable web application that allows easyonline sharing and interactive visualisation of large screen datasets, facilitating their dissemination and furtheranalysis, and enhancing their impact.Main textDespite groundbreaking discoveries in genomics, the ge-nomes of most organisms remain black boxes with thefunction of the majority of genes and gene products stillunknown. High-throughput/high-content microscopy-based screening (HT/HCS) provides an increasinglypowerful tool to discover and functionally annotategenes and biological pathways, and in less than a decadehas led to several important discoveries, like the systematicgenome-wide identification of genes important for mitosis,endocytosis, the cytoskeleton, and other fundamentalprocesses (Chia et al. [1], Cotta-Ramusino et al. [2],Collinet, C. et al. [3], Neumann et al. [4] Graml et al. [5]).A current limitation of HT/HCS projects is that evenafter they are finalised, just accessing and visualisingtheir output requires specialist expertise in image anddata analysis, limiting their use and accessibility. This,along with the lack of a standardized way to manage andshare the biological big data generated by HT/HCSscreens with the wider community, limit the commu-nity’s capacity to fully exploit the rich, quantitative func-tional genomic information contained in those projectsand thus the return on the investment made in them(Earnshaw [6]).* Correspondence: cre20@cam.ac.ukGenetics Department, University of Cambridge, Downing Street, CambridgeCB2 3EH, UK© 2015 Antal et al. Open Access This article iInternational License (http://creativecommonsreproduction in any medium, provided you gthe Creative Commons license, and indicate if(http://creativecommons.org/publicdomain/zeHere we introduce Mineotaur, a web-based interactivevisual analytics tool we developed to provide an efficientway to share the large (>106 points) amounts of image-derived feature data acquired by HT/HCS, and to allow thescientific community complete access to easily visualizeand inspect HT/HCS data, linking with images when avail-able (detailed documentation of Mineotaur can be accessedat http://docs.mineotaur.org).While there are open source [7] and commercial soft-ware (http://www.tableau.com/, http://www.moleculardevices.com/systems/high-content-imaging/acuityxpress-high-content-informatics-software, http://spotfire.tibco.com/)which allow visual analysis of HT/HCS data, Mineotaur isunique in that it lets scientists publish their screens in astandard way together with a free pre-packaged visual ana-lytical toolkit. Once an instance has been set up the aim isfor end users, for example biologists without deep compu-tational knowledge, to be able to access data with a min-imal investment of time and effort.The pipeline for data sharing and visual analytics usingMineotaur can be seen in Fig. 1. Mineotaur is based ona property graph model (Robinson et al. [8]) to handleHT/HCS screens, which is implemented using Java(http://java.oracle.com), and a graph database, Neo4j(http://www.neo4j.com) (see the Additional file 1: FigureS1, Figure S2, Figure 3 and the Online Methods for de-tails of the data model and implementation). Neo4j is adatabase system built upon the concept of propertys distributed under the terms of the Creative Commons Attribution 4.0.org/licenses/by/4.0/), which permits unrestricted use, distribution, andive appropriate credit to the original author(s) and the source, provide a link tochanges were made. The Creative Commons Public Domain Dedication waiverro/1.0/) applies to the data made available in this article, unless otherwise stated.Fig. 1 Pipeline to share high-throughput/high-content microscopy screens. The acquired images and feature data extracted from them can beused to generate a Mineotaur instance, allowing research groups to share their data in a standard way. The public Mineotaur servers let theinterested members of the scientific community to gain an understanding and confidence of the data by accessing features and images in a fewclicks, generating plots on-the-fly and provides a toolkit for researchers discover new hypotheses using the data and export and sharesupporting findingsAntal et al. Genome Biology  (2015) 16:283 Page 2 of 5graphs, which models all objects of interest (e.g. geno-types/cell lines, genes, images, cells, etc.) as nodes in agraph, and edges in the graph represent a connectionbetween them (e.g. an edge between ‘cell’ nodes and an‘image’ node signify ‘cells extracted from that image’).Each node also stores an arbitrary list of property values(e.g. for each cell, the value of each of the quantitativefeature measurements extracted from it by the computa-tional pipeline) and labels (e.g. a gene identified as a ‘hit’for a given process in a HT/HCS screen can be anno-tated as implicated in that process), providing flexibilityto different data sources and to updates/changes in thedata, which is almost impossible in standard relationaldata models. For convenience, we provide a standardway to generate a graph model and a Mineotaur instancefor any HT/HCS screen simply from a CSV (commaFig. 2 Architecture of the Mineotaur system. The Mineotaur web server canREST. The web server handles the interaction with the graph database conseparated values) file. An example data model for the [5]screen can be seen in Additional file 1: Figure S1, whileAdditional file 1: Figure S4 and Additional file 1: Figure S5shows how example input files for the Mineotaur instancegeneration need to be formulated.The data stored in Mineotaur can be accessed by bothan interactive web-based Graphical User Interface(GUI), which enables intuitive ad-hoc querying and plot-ting of the descriptive information extracted from theHT/HCS microscopy screens in real-time, and program-matically, using a REST interface (see Fig. 2 for thearchitecture of Mineotaur). Thus, the screen data can beexploited by both experimental and computational biol-ogists. An example screen from Mineotaur’s web inter-face is shown in Fig. 3. As a design principle, we wantedto allow users to easily construct complex queries andbe accessed from both a web interface and programatically usingtaining the HT/HCS dataFig. 3 Unique distinguishing features of Mineotaur. a The web interface allows on-the-fly analysis of the data. b Queries are visualized asinteractive plots. c Analytics tools like regression lines can be used to generate new hypotheses about the data. d Subsets of data can be selectedfor in depth analysis. e From a gene-level plot, cell-level information can be reached from a context menu in one click. f Multiple plots can becompared to one another using the plot comparison toolAntal et al. Genome Biology  (2015) 16:283 Page 3 of 5traverse through to raw images or well-establishedgenomic databases in a few clicks. Feature data can beaccessed at multiple levels (e.g. genes and cells), if suit-able data are available. The user can discover multidi-mensional associations between different descriptorsby generating scatter plots with adjustable axes, oranalyze the distribution of single features for certainconditions in the form of (multi)histograms and kerneldensity estimation plots. From scatter plots, the userscan access the raw images if they are available online(for example using Omero [9]) with a single click onthe data points. Furthermore, users can jump from gene-level scatter plots to extracting cell-level knowledge with asingle click.The queries can be transformed by filtering the datapoints at the different levels of the investigated condi-tions (e.g. gene or cells annotations). The queried datacan be further analysed by allowing area selection withina plot, regression line fitting, plot transformation andplot comparison.To ensure the reusability and reproducibility of thedata analysis, Mineotaur users can generate a link allow-ing them to share the plots they generate (including anyfilters applied), as well as export their data to differentformats such as vector graphical format SVG (to exportplots) or a comma separated CSV text file containingthe raw data values, for use with standard spreadsheettools (for an explanation of how to export data seeAntal et al. Genome Biology  (2015) 16:283 Page 4 of 5http://docs.mineotaur.org/en/latest/plot_tools.html#down-load). We also provide a way to enrich publications byallowing users to embed interactive charts from Mineo-taur to web pages (to be used e.g. as an interactive figurein the HTML version of an online material in journals).The querying capabilities of Mineotaur can be seen inAdditional file 2: video S1, while an example post-querycan be seen in Additional file 3: video S2. The code isopen-source and is accessible at https://github.com/antal-balint/Mineotaur/ under GPL license.To demonstrate the capabilities and versatility of thetool, we used data from two published screens. First, agenomic multi-process HT/HCS screen recently pub-lished by our group [5], containing quantitative pheno-typic annotations of hundreds of genes influencing cellshape, microtubules and cell cycle progression in fissionyeast (Schizosaccharomyces pombe). The screen consistsof images from tens of 96-well plates, with each wellcontaining cell populations knocked out for a specificnon-essential S. pombe gene (except for a few wells con-taining positive/negative controls). The data consists of138 000 images, from which 1.7 million cells and 5.5million microtubules were computationally identifiedand quantitated, leading to 131 features extracted fromeach cell. For details on the experimental pipeline andits connection to Mineotaur see Methods and Additionalfiles 1, 2, 3, 4 and 5. Secondly, we also generated a Mine-otaur instance from a subset of an HT/HCS screen in-vestigating the signalling network controlling the Golgiapparatus in human cells [10], which contains 624 featuresfor 1 580 242 cells, imaged from 353 different condi-tions. Additional file 4: video S3 shows the step-by-stepreconstruction of a figure from [5], while the recon-structed figure can be seen in Additional file 1: FigureS6. A demonstration instance for the latter dataset canbe accessed at http://demo.mineotaur.org/.In summary, we have developed Mineotaur, a graphmodel based web application, allowing visual analyticsand sharing of HT/HCS projects amongst the entirecommunity, computational and non-computational alike.We believe the intuitive interface, versatility and scalabil-ity will greatly potentiate the return-on-investment ofpast and future projects in the field, catalyse deep biologicaladvances and open the way to establishing community-wide data and interoperability standards.Materials & methodsThe graph modelBasic conceptsProperty graph: a mathematical graph where each nodesand vertices can also hold tuples of data.Objects of interest: any part of an experiment to beincluded in Mineotaur to be either directly queriedor stored as metadata. Example: Strain, Gene, Cell,Experiment.Grouping object: the main object of interest, the topobject in the graph. Example: Gene, Strain.Descriptive objects: the objects carrying the detaileddata associated with the grouping object. Example: Cell.Please note that in cases where only one layer of experi-mental data is available, the grouping objects can be de-scriptive objects as well.Import dataThe graph model for each Mineotaur instance can begenerated by providing a CSV (comma separated value)file in the following format:First line: column headers. These will serve as theproperty names for their respective object types in thedatabase.Second line: object names. These describe the names ofthe objects of interest to be stored in the database. Theproperty in each column described in the first line willbe associated with the object described here.Third line: property types. These describe the datatypes of the properties for each column. The possiblevalues are: TEXT: the column contains a text. Stored as ametadata. NUMBER: the column contains a number, thus itwill be become a queryable information if stored ina descriptive object. ID: identifier of the object. Multiple ID propertiescan be set to an object. URL: the URL of the resource to be linked to object.Each line after the third provides a descriptive objectinstance.For an example input file, see Additional file 1: Figure S4.To provide annotation for the grouping objects, anadditional input file containing the labels is needed. Thefirst n column describe the IDs for the grouping object.All other columns provide a binary value for a label(1=the grouping object possesses the annotation,0=otherwise). An example label input file can be seenin Additional file 1: Figure S5.Detailed architectureServer side: The server side application is written in Javaand based on the Spring framework. On the server side,the data handling and the business logic behind the ap-plication is separated by design. As a graph database,Neo4J is used in embedded mode. The server handlesincoming HTTP requests for the data, translate it to thedata model and fetches data from the graph database.Antal et al. Genome Biology  (2015) 16:283 Page 5 of 5The data is sent back either as a HTTP response or aJSON file.Client side: The server can be accessed in two ways:by the user interface of the Mineotaur and by REST.The user interface is based on HTML 5 (generatedfrom Thymeleaf templates by the server), Bootstrap andJavascript. The user interface provides 1) an interface forad-hoc querying the data 2) visualizes the response ofthe server 3) provides tools to analyse the data. When aquery is submitted, an asynchronous call to the server issent using AJAX. The server provides a response inJSON format, from which the appropriate plot is ren-dered using the D3 javascript library. The analytic toolkitof the user interface is built using the jQuery library anddesigned as a modular Model-View-Controller (MVC) ap-plication (see Additional file 1: Figure S3 for the architec-ture) using the require.js AMD (Anyschronous ModuleDefinition) framework. The client side consists of the fol-lowing Javascript modules:Controller: handling all events triggered in the userinterface.Context: a data access object containing all relevantinformation for the current sessionUI: all functions to provide an interactive user interfacePlot: functions required for generating the plots usingthe D3 framework.Utilities: common mathematical functions usedthroughout the applicationRESTThe server side can be accessed programmatically fromany programming language or framework capable of hand-ling HTTP requests and responses and JSON (i.e. Java,Python, Matlab, Bash, etc.).Additional filesAdditional file 1: Supplemental Figures S1–S6. (DOCX 1246 kb)Additional file 2: Video S1. Pre-query selection options of Mineotaur.(MOV 1868 kb)Additional file 3: Video S2. Post-query selection options of Mineotaur:a) Plot transformations b) Filtering c) Connecting plots d) Connecting toexternal software and website. (MOV 13448 kb)Additional file 4: Video S3. Reconstruction of Figure 7a from [5].(MOV 1136 kb)Additional file 5: Mineotaur documentation for Release 1.0.1.(PDF 1303 kb)Competing interestsThe authors declare no competing financial interests.Authors’ contributionsRECS, AC and BA conceived/led the project designed the general computationalstrategy and wrote the text. BA carried out all computational work. All authorsread and approved the final manuscript.AcknowledgementsWe thank J. Swedlow, G. Rustici, F. Vaggi, A. Csikasz-Nagy, V. Wood, G.Micklem, and the Carazo-Salas group for help and comments, J. Lawson fordesign of the Mineotaur logo, and J. Swedlow, G. Rustici, T. Walter, and A.Csikász-Nagy for critical reading of the manuscript. This work was supportedby an European Research Council (ERC) Starting Researcher InvestigatorGrant (R.E.C.-S.; SYSGRO), a Biological Sciences Research Council (BBSRC)Responsive Mode grant (R.E.C.-S.; BB/K006320/1) and an Isaac Newton Trustresearch grant (R.E.C.-S.; 10.44(n)).References1. Chia J, Goh G, Racine V, Ng S, Kumar P, Bard F. RNAi screening reveals alarge signaling network controlling the Golgi apparatus in human cells. MolSyst Biol. 2012;8:629.2. Cotta-Ramusino C, McDonald 3rd ER, Hurov K, Sowa ME, Harper JW, ElledgeSJ. A DNA damage response screen identifies RHINO, a 9-1-1 and TopBP1interacting protein required for ATR signaling. Science. 2011;332:1313–7.3. Collinet C, Stöter M, Bradshaw CR, Samusik N, Rink JC, Kenski D, et al.Systems survey of endocytosis by multiparametric image analysis. Nature.2010;464:243.4. Neumann B, Walter T, Hériché JK, Bulkescher J, Erfle H, Conrad C, et al.Phenotypic profiling of the human genome by time-lapse microscopyreveals cell division genes. Nature. 2010;464:721.5. Graml V, Studera X, Lawson JL, Chessel A, Geymonat M, Bortfeld-Miller M,et al. A genomic Multiprocess survey of machineries that control and linkcell shape, microtubule organization, and cell-cycle progression. Dev Cell.2014;31(2):227–39.6. Earnshaw WC. Deducing protein function by forensic integrative cellbiology. PLoS Biology. 2013;11(12):e1001742.7. Jones TR, Kang IH, Wheeler DB, Lindquist RA, Papallo A, Sabatini DM, et al.CellProfiler Analyst: data exploration and analysis software for compleximage-based screens. BMC Bioinformatics. 2008;9:482.8. Robinson I, Webber J, Eifrem E. Graph Databases. O’Reilly Media; USA 2012.9. Allan C, Burel JM, Moore J, Blackburn C, Linkert M, Loynton S, et al. OMERO:flexible, model-driven data management for experimental biology. NatMethods. 2012;9:245–253.10. Chia J, Goh G, Racine V, Ng S, Kumar P, Bard F. Data from: RNAi screeningreveals a large signaling network controlling the Golgi apparatus in humancells. Dryad Digital Repository. 2012. http://dx.doi.org/10.5061/dryad.1m2p3.

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Mineotaur:a tool for high-content microscopy screen sharing and visual analytics

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