The authors wish to thank the Canadian Institutes of Health Research (CIHR) and Genome Alberta, a division of Genome Canada, for financial support.

<ol>
	<li>Michal, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=On+representation+of+metabolic+pathways.">On representation of metabolic pathways.</a> Biosystems. 47 (1-2), 1-7 (1998).</li><li>Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M., Tanabe, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=KEGG+as+a+reference+resource+for+gene+and+protein+annotation.">KEGG as a reference resource for gene and protein annotation.</a> Nucleic Acids Res. 44 (D1), D457-D462 (2016).</li><li>Karp, P., Riley, M., Paley, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+MetaCyc+Database.">The MetaCyc Database.</a> Nucleic Acids Res. 30 (1), 59-61 (2002).</li><li>Kelder, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=WikiPathways:+building+research+communities+on+biological+pathways.">WikiPathways: building research communities on biological pathways.</a> Nucleic Acids Res. 40 (Database issue), D1301-D1307 (2011).</li><li>Croft, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Reactome+pathway+knowledgebase).">The Reactome pathway knowledgebase).</a> Nucleic Acids Res. 42 (Database issue), D472-D477 (2014).</li><li>Karp, P. 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P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Presenting+and+exploring+biological+pathways+with+PathVisio.">Presenting and exploring biological pathways with PathVisio.</a> BMC Bioinformatics. 9, 399 (2008).</li><li>Demir, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+BioPAX+community+standard+for+pathway+data+sharing.">The BioPAX community standard for pathway data sharing.</a> Nat. Biotechnol. 28 (9), 935-942 (2010).</li><li>Shannon, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cytoscape:+A+Software+Environment+for+Integrated+Models+of+Biomolecular+Interaction+Networks.">Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks.</a> Genome Res. 13 (11), 2498-2504 (2003).</li><li>Salomonis, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=GenMAPP+2:+new+features+and+resources+for+pathway+analysis.">GenMAPP 2: new features and resources for pathway analysis.</a> BMC Bioinformatics. 8, 217 (2007).</li><li>Elliott, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=PathCase:+pathways+database+system.">PathCase: pathways database system.</a> Bioinformatics. 24 (21), 2526-2533 (2008).</li><li>Hu, Z., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=VisANT+3.0:+new+modules+for+pathway+visualization,+editing,+prediction+and+construction.">VisANT 3.0: new modules for pathway visualization, editing, prediction and construction.</a> Nucleic Acids Res. 35 (Web Server), W625-W632 (2007).</li><li>Jewison, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SMPDB+2.0:+Big+Improvements+to+the+Small+Molecule+Pathway+Database.">SMPDB 2.0: Big Improvements to the Small Molecule Pathway Database.</a> Nucleic Acids Res. 42 (D1), D478-D484 (2013).</li><li>Pon, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pathways+with+PathWhiz.">Pathways with PathWhiz.</a> Nucleic Acids Res. 43 (W1), W552-W559 (2015).</li><li>Sajed, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ECMDB+2.0:+A+richer+resource+for+understanding+the+biochemistry+of+E.+coli.">ECMDB 2.0: A richer resource for understanding the biochemistry of E. coli.</a> Nucleic Acids Res. 44 (D1), D495-D501 (2015).</li><li>Wishart, D., Mandal, R., Stanislaus, A., Ramirez-Gaona, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cancer+Metabolomics+and+the+Human+Metabolome+Database.">Cancer Metabolomics and the Human Metabolome Database.</a> Metabolites. 6 (1), 10 (2016).</li><li>Altschul, S. 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The authors have nothing to disclose.

The protocol described here for creating a simple metabolic pathway (the TCA cycle) can be adapted to create a wide variety of machine-readable, biologically complex pathways for any species. Furthermore, this protocol also describes how one can replicate or propagate existing pathways created by other users. Constructing a pathway using this tool requires repeated step-by-step additions of reactions, interactions, transport processes, and sub-pathways, each of which are connected by overlapping elements. Putting all of these together allows one to create colorful, visually pleasing pathway diagrams that provide considerable biological detail and useful biological context. The steps described in this protocol are relatively simple, and the time it takes to build a pathway diagram depends on the size and complexity of the pathway. With a bit of practice, most individuals can render a high quality pathway diagram consisting of about 15-20 reactions or processes and several cellular components in about 15 min. A brand new user may take up to 30-40 min to generate a pathway of similar size and complexity. The time needed to generate a pathway is roughly linear with the number of reactions/processes that need to be rendered.
Creating a high quality pathway through this web-based tool depends on the quality and detail of the source material (pathways from books, online databases, experimental data, hand-drawn sketches) and the fastidiousness of the pathway &#34;artist&#34;. Those wishing to generate higher quality pathway diagrams should pay particular attention to sections 1.11, 1.15 and 1.20 of the protocol, since these sections describe the creation and editing of reaction elements (reactants/products, enzymes, edges, images, zoom boxes, labels, and membranes). The best pathway diagrams will intelligently amalgamate information from as many existing representations of the pathway as possible including those found in books, posters, papers, and online databases. Another key to generating high quality pathways is carefully checking the correctness of the reactions before creating a reaction (through section 1.11 of the protocol). Taking the time and effort to ensure the reactants, products, and enzymes involved (many of which already exist in PathWhiz&#39;s large database) are correct for each species is very important. It is also important to be aware of the cellular location of the reactions and to include key cellular or subcellular components in order to provide the correct biological context. This can be done by checking and corroborating the reaction through online databases such as UniProt. Having all the required information at hand, along with a rough, hand-drawn sketch of the pathway to be created will greatly reduce errors and the overall time spent on drawing or rendering.
As might be expected, larger and more complex pathways will take longer to render, particularly if the desired elements and processes are not already in PathWhiz&#39;s database. When working with larger pathways, it is usually wise to switch from the Autosave mode to the Manual save mode, in order to prevent a long lag between actions. When replicating a pathway, the amount of time the user might wait for the pathway to be generated depends on the number of elements in the pathway. Most pathways can be replicated in about 1-2 min. When propagating a pathway, successful rendering of the newly propagated pathway depends on how similar the two species are, as PathWhiz uses BLAST17 sequence searches to find homologous enzymes between species. Larger pathways will be slower to propagate because BLAST will need to be run on a greater number of enzymes. Attempting to propagate pathways between significantly dissimilar species (say between yeast and humans) will result in pathways being rendered with a number of unknown proteins. These &#34;distantly&#34; propagated pathways will usually require additional manual editing. Due to the highly visual nature of pathway diagrams and the detail that can be brought to a pathway, it is always a good idea to work on a computer with a reasonably large screen (&#62;20 inches or &#62;50 cm) and a good internet connection (&#62;5 mbps).
If problems with rendering or screen refreshing are encountered, the user may have to do a small amount of troubleshooting. If a large, complex pathway takes too long to update, the user may have to refresh the page. If a pathway does not propagate as expected, the user may have to do some manual editing to ensure that all elements are displayed correctly. Also, as a more specific example, if elements of a reaction are not displayed, the user may have to make sure that all elements or enzymes are properly selected and the edges are not hidden. The &#34;Help&#34; link on the main header may be useful if a problem is encountered. A tutorial is available under the &#34;Tutorial&#34; tab and a user manual is available under &#34;User Guides&#34; tab. Both explain many of the tool&#39;s features in detail. The User Guide may be used to troubleshoot or explain potential limitations for a particular feature, such as when a user locks a pathway and later wishes to edit it.
As highlighted through this protocol and through the examples provided in the accompanying figures, this tool offers a number of unique features not found in any (or most) other pathway drawing tools (see Table 1). First, it is fully web-based and completely platform-independent. Second, it supports the rendering and facile generation of multicolored, biologically complex, visually pleasing, fully hyperlinked pathway diagrams that can also be converted to machine-readable formats (BioPAX, SBGN-ML18, SBML19, PWML14). Third, pathway diagrams generated by this tool can be browsed, searched, selected and easily explored through an easy-to-use online database and viewing interface. Fourth, the web tool is designed to support community pathway contributions, allowing for &#34;pathway crowd sourcing&#34; that encourages the sharing and generation of new pathways and new pathway elements.
Pathways generated by this web based tool can be used for a variety of applications. Richly detailed, fully hyperlinked pathways can be readily integrated into organism-specific databases for proteomics, metabolomics or systems biology applications. Internet-accessible pathways are especially useful for education and training purposes, as the details available through web-based images are often much greater than what can be displayed via a static image or through a single textbook or journal page. This web based tool also supports the generation of pathway representations that are more suited for printing and publication. As a result, many images generated by this web based tool are appearing in papers, posters and slide presentations. Exporting pathways into text-based data exchange file formats (such as BioPAX and SBML) allows for pathways generated using this web server to be used directly in computational analysis for systems biology or metabolic modeling applications. Propagating pathways between species allows inferences to be made about biological processes, especially among those species that have been very recently sequenced. While not all existing pathways currently exist in PathWhiz, its public pathway database continues to grow, leading to the emergence of new, crowd-sourced pathway collections. These collections will not only be readily extendable to new species, they will hopefully lead to a deeper understanding of their unique biology and biochemistry.

Biological pathway diagrams are like blueprints for life scientists. They are perhaps the most concise and informative routes for depicting biological processes and the contextual connections between genes, proteins, and metabolites. This is because images are much more efficiently processed and often much more easily understood by humans than text1. The quality, detail, and content of pathway diagrams can vary considerably. These differences often depend on the intended purpose of the pathway and the skills of the pathway artist. Pathways created for educational purposes such as wall charts or textbooks are often created by professional artists. As a result, these pathway diagrams are much more visually pleasing and offer considerably more biological detail with full depictions of metabolite structures, subcellular components, cell structures, tissues, and organs. These &#34;textbook&#34; representations often include detailed notes and commentaries. On the other hand, pathway diagrams designed for internet applications often have to sacrifice artistry and visual richness in favor of simplified machine readable &#34;wiring&#34; diagrams. These wireframe diagrams are more easily image-mapped and hyperlinked. Simplified pathway diagrams are the basis to such popular online pathway databases as KEGG2, MetaCyc3, Wikipathways4, and Reactome5. The emergence of computer-compatible pathway databases has also led to the appearance of computer-compatible pathway drawing tools. In other words, one does not have to be a professional artist or a professional programmer to generate useable pathway diagrams. For instance, BioCyc&#39;s Pathway tools6 and Wikipathway&#39;s PathVisio software7allow users to freely generate and share machine-readable pathways in BioPAX8 and/or HTML format. Additionally, there are a number of other stand-alone freeware packages as well as commercial packages that support the generation of various machine-readable, wire-frame pathways, such as Cytoscape9, GenMAPP10, PathCase11, and VisANT12.
The simplification of internet pathway diagrams largely grew from the historical limitations found in many web browsers and web-based rendering tools. However, significant advances in web technologies have been made in the past few years. This suggested that it could be possible to generate interactive, internet-compatible pathway diagrams that are just as colorful, just as aesthetically pleasing and just as biologically complete as those found in textbooks. This work led to the development of PathWhiz. PathWhiz was implemented using a Ruby on Rails (http://rubyonrails.org, version 4.2.0) web framework incorporating a MySQL relational database (https://www.mysql.com, version 5.1.50) to manage all of the pathway data, including entity relationships, external references, descriptions, visualization specifications and chemical structures. The front-end web client is controlled by Ruby on Rails combined with Backbone.js (http://backbonejs.org, version 1.0.0) as the front-end web framework for the editor.
Originally developed for the curation of the human-only Small Molecule Pathway Database (SMPDB)13, PathWhiz14has since been extended to support pathway generation for many other organisms and to function as a general pathway image and knowledge database. In particular, this web tool allows for the creation of the full range of biochemical/biological pathways including metabolic, protein interaction, molecular signaling, physiological, and drug/disease pathways. This pathway drawing tool differs from most other pathway generating tools in three major ways: 1) it is a web server rather than a stand-alone, installable software package; 2) it supports the facile generation and interactive visualization of chemical compounds, proteins, nucleic acids, cellular membranes, subcellular structures, tissues, and organs; and 3) it allows users to easily borrow, build or improve upon the work of other users, thereby allowing &#34;crowd-sourced&#34; pathway generation. As a web server, it has several advantages over downloadable, platform-specific software tools. In particular, it is compatible with any platform, operating system and modern web browser. Furthermore, it does not require the user to register in order to begin creating a pathway (though users can freely create a &#34;private account&#34; in order to track and control the accessibility of the pathways they create). Perhaps the most appealing feature of this tool is the amount of biological and biochemical detail that can be easily added into any pathway through a palette of pre-rendered images and an extensive database of protein and biochemical data. This allows both &#34;non-artists&#34; and &#34;non-programmers&#34; to easily create colorful, aesthetically pleasing and richly detailed pathways that are web-compatible and fully machine-readable. A more detailed comparison between PathWhiz and other pathway drawing tools is provided in Table 1.
A number of popular life science databases have already used this pathway drawing tool to create database-specific, online, interactive pathway diagrams. For instance, the Escherichia coli Metabolome Database (ECMDB)15recently updated its pathway library with more than 1,650 pathways drawn using web based tool. Each pathway in the ECMDB is now displayed as a richly colored, fully hyperlinked image map with detailed metabolite and protein structure depictions, as well as a simplified black and white KEGG-like wire diagram. This large-scale pathway update led to the discovery of many intermediate metabolites that had not been previously included in other Escherichia coli metabolic databases. Other databases, such as the Human Metabolome Database (HMDB)15, not only rely on PathWhiz pathways to depict and describe metabolic and signaling pathways, but also to depict the metabolic changes involved in diseases such as cancer. HMDB currently includes 101 metabolic pathways, 376 drug action pathways, 233 disease-associated pathways and 16 signaling pathways, all generated through this web tool.
The following protocol describes in detail how PathWhiz can be used to easily create, replicate, and propagate biochemical pathways for a variety of purposes and applications.

<table><tbody><tr><td>Computer with colour screen</td><td>N/A</td><td>N/A</td><td>&amp;nbsp;&gt;20 inches or &gt;50 cm</td></tr><tr><td>Internet connection&amp;nbsp;</td><td>N/A</td><td>N/A</td><td>&gt;5 mbps</td></tr><tr><td>Modern web browser&amp;nbsp;</td><td>N/A</td><td>N/A</td><td>Google Chrome (v. 31 and above), Internet Explorer (v. 9 and above), Safari (v. 7 and above), Opera (v. 15 and above) and Firefox (v. 23 and above)</td></tr></tbody></table>

a web tool for generating high quality machine-readable biological pathways

PathWhiz is a web server built to facilitate the creation of colorful, interactive, visually pleasing pathway diagrams that are rich in biological information. The pathways generated by this online application are machine-readable and fully compatible with essentially all web-browsers and computer operating systems. It uses a specially developed, web-enabled pathway drawing interface that permits the selection and placement of different combinations of pre-drawn biological or biochemical entities to depict reactions, interactions, transport processes and binding events. This palette of entities consists of chemical compounds, proteins, nucleic acids, cellular membranes, subcellular structures, tissues, and organs. All of the visual elements in it can be interactively adjusted and customized. Furthermore, because this tool is a web server, all pathways and pathway elements are publicly accessible. This kind of pathway &#34;crowd sourcing&#34; means that PathWhiz already contains a large and rapidly growing collection of previously drawn pathways and pathway elements. Here we describe a protocol for the quick and easy creation of new pathways and the alteration of existing pathways. To further facilitate pathway editing and creation, the tool contains replication and propagation functions. The replication function allows existing pathways to be used as templates to create or edit new pathways. The propagation function allows one to take an existing pathway and automatically propagate it across different species. Pathways created with this tool can be &#34;re-styled&#34; into different formats (KEGG-like or text-book like), colored with different backgrounds, exported to BioPAX, SBGN-ML, SBML, or PWML data exchange formats, and downloaded as PNG or SVG images. The pathways can easily be incorporated into online databases, integrated into presentations, posters or publications, or used exclusively for online visualization and exploration. This protocol has been successfully applied to generate over 2,000 pathway diagrams, which are now found in many online databases including HMDB, DrugBank, SMPDB, and ECMDB.

The web server&#39;s main pathway generation tool described in this manuscript is shown in Figure 1 and Figure 2. Menu options provided by each tab are also shown. Figures 3 and 4 provide a set of screenshots of the pathway creation process. Figure 5 provides a set of screenshots of the reaction creation process. Figure 6 shows the online pathway viewer and its menu.
PathWhiz can be used to generate pathways with various content types and styles. These include &#34;traditional&#34; metabolic pathways (Figure 7), disease and drug pathways showing side effects (Figure 8) and drug responses (Figure 9), as well as protein signaling pathways (Figure 10). Pathways may be richly colored with considerable biological detail or they may be converted to simple black and white representations (Figure 11). Once completed, these pathways may be viewed in the interactive pathway viewer (Figure 6), downloaded as images, or exported in several different machine-readable data exchange formats for further analysis. Note that the quality of the different data exchange formats depends on the quality of data inputted when originally drawing the pathway. For example, adding more reaction detail (i.e. stoichiometry, biological states) will produce more comprehensive BioPAX. On the other hand, pathways drawn with overlapping elements (for visual reasons, such as showing bound elements or protein complexes) may also produce overlapping glyphs in SBGN-ML.
<img alt="Figure 1" src="/files/ftp_upload/54869/54869fig1.jpg" /> 
Figure 1: Pathway Editor Interface. The Editor Interface is composed of 3 main sections: a top main menu bar, a secondary menu and a gridded canvas. The top main menu bar (purple) provides links to view, edit, and create pathway elements. The lower secondary menu bar (grey) provides links to add and edit visual pathway elements in the current pathway diagram, such as reactions, interactions, transport processes, sub-pathways, compounds, proteins, nucleic acids, as well as membranes, cellular/subcellular images, zoom boxes, and labels. This menu also includes two tabs that permit the editing of selected elements or the editing of the canvas. The gridded white canvas below menu bars is where the reaction pathways and processes will be added. The zoom box works as a visual cue to indicate the magnification of a selected area in an image. It consists of a small square that is connected to a re-sizeable quadrilateral. The small square is placed on the area that is to be expanded or zoomed, while the quadrilateral works as a canvas in which one can add the reactions that happen in the selected area (by the smaller square). Edit the zoom box by double clicking it to access its sidebar. Editing options include drop down lists for template, color, and z-index. The rendering orientation of the zoom box can be changed by selecting top, right, left or bottom in the template tab. When the zoom box is selected, the black circles can be dragged to resize and reformat the different zoom box components. <a href="http://cloudflare.jove.com/files/ftp_upload/54869/54869fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/54869/54869fig2.jpg" /> 
Figure 2: Pathway Editor Menus. The editor menus provide options to add processes and elements, as well as to edit the existing elements and the canvas. (a) The &#34;Pathway&#34; link offers options to &#34;Edit Details&#34; and &#34;Export and View&#34;. The &#34;Edit Details&#34; option permits the editing of the pathway description and references while the &#34;Export and View&#34; option permits the generation or regeneration of the image files. (b) The &#34;Add Process&#34; link offers options for adding a reaction, interaction, binding event, transport event, reaction coupled transport, or sub-pathway to the canvas. (c) The &#34;Add Vacuous Element&#34; link offers options for adding a compound, a protein, a nucleic acid, an element collection, or an edge to the canvas. These elements will show up in the upper left corner of the canvas. An arbitrary element will appear in the canvas alongside the pop-up sidebar, where the user can search for the desired element or change the details of said element. The element should be incorporated into the pathway before adding any new vacuous elements, in order to maintain pathway neatness. <a href="http://cloudflare.jove.com/files/ftp_upload/54869/54869fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/54869/54869fig3.jpg" /> 
Figure 3: Pathway Index Form. The pathway index offers a collection of currently existing pathways and a search box to query for specific pathways. Pathways can be filtered by name, type, species, and creator using the filter bar on the top of the index table. They can also be searched by name using the search bar at the top of the page. Opening up the &#34;Advanced Search&#34; allows more specific searches by combinations of biological state, type, species, compound, and protein. The advanced search allows the use of AND, OR, and NOT logical operators to create complex queries. Each pathway includes 5 buttons: &#34;Show&#34;, &#34;Edit&#34;, &#34;Draw&#34;, &#34;Destroy&#34; and &#34;Replicate&#34;. The &#34;Show&#34; button allows viewing of the pathway using the Viewer. The &#34;Edit&#34; button allows editing of the pathway metadata, including name, type, species, description and reference. The &#34;Draw&#34; button allows editing of the canvas containing the pathway. The &#34;Destroy&#34; button allows removal of the pathway from the database (if the user has permission). The &#34;Replicate&#34; button allows replication of the selected pathway. The &#34;Previous&#34; and &#34;Next&#34; buttons allow the user to navigate between pages of pathways. <a href="http://cloudflare.jove.com/files/ftp_upload/54869/54869fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" src="/files/ftp_upload/54869/54869fig4.jpg" /> 
Figure 4: Create New Pathway Form. The &#34;New Pathway&#34; button (see Figure 3) leads to the pathway form shown here. This form contains fields for the pathway&#39;s name, type, species, and description. The &#34;New Pathway&#34; button also allows one to start from an existing pathway and add references. <a href="http://cloudflare.jove.com/files/ftp_upload/54869/54869fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" src="/files/ftp_upload/54869/54869fig5.jpg" /> 
Figure 5: Create New Reaction Form. The &#34;Add Process&#34; link allows users to add a new process, such as a reaction or binding event. Adding a Process creates a reaction model, from which reaction visualizations can be generated and added to pathway diagrams. The reaction model and visualization are separate entities. (a) The reaction field permits searching for an existing reaction, by reactant, product, or enzyme. The biological state field permits searching and selecting an existing biological state. Once a reaction is chosen, the corresponding enzymes can be added using the &#34;Add Enzyme&#34; button, which will bring up an enzyme autocomplete box. The render options allow for the user to choose the direction they wish the reaction to be rendered. A new reaction can be created through the (b) &#34;New Reaction&#34; button, which leads to a New Reaction form where elements and enzymes can be added. Once all the fields are filled in, the reaction can be created through the &#34;Create Reaction&#34; button. To edit the underlying reaction model one should exit the pathway illustrator, go to the reaction index, and find and edit the reaction there. In order to prevent data discrepancies and unintentionally altering existing pathways, it is not possible to change the reactants/products or remove enzymes from a reaction model if it already has visualizations in existing pathways. Thus, editing the reaction model does not automatically update existing reaction visualizations. In order to change a reaction model one must re-add the corresponding visualization to the pathway diagram for the changes to appear. <a href="http://cloudflare.jove.com/files/ftp_upload/54869/54869fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" src="/files/ftp_upload/54869/54869fig6.jpg" /> 
Figure 6: Pathway Viewer. The top right viewer interface buttons provide basic navigation, zooming, and screen toggling actions. The central viewport displays the pathway that can be navigated by clicking and dragging, or zooming using a mouse. The pathway elements displayed are hyperlinked to other pathways and databases (e.g. HMDB, DrugBank, UniProt). The side menu bar displays a description of the pathway with references supplied by the user. The side menu also displays the tabs &#34;Highlight&#34;, &#34;Analyze&#34;, &#34;Downloads&#34;, and &#34;Settings&#34;. The &#34;Highlight&#34; tab allows compounds and enzymes to be selected and highlighted in red. The &#34;Analyze&#34; tab allows experimental concentration data to be entered, which is then mapped to the pathway using a color gradient. The &#34;Downloads&#34; tab offers links to the corresponding downloadable image files and data exchange files. The PNG file is a smaller non-vector image file. The SVG + BioPAX links provide larger vector image files with embedded BioPAX, for machine-readability. The BioPAX, SBML, SBGN, and PWML links provide different machine-readable formats. The &#34;Settings&#34; tab allows for visual customization of the displayed pathway image. <a href="http://cloudflare.jove.com/files/ftp_upload/54869/54869fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" src="/files/ftp_upload/54869/54869fig7.jpg" /> 
Figure 7: Metabolic Pathway Image. This is an example of a &#34;traditional&#34; metabolic pathway that describes the biosynthesis and degradation of a particular compound (D-serine). The main metabolite is positioned in the center of the canvas and the reaction and transport arrows (edges) show the flow of the pathway. Edges and elements can automatically be &#34;snapped&#34; together, i.e. connected. Element snap points are indicated by transparent red circles on the element sides, and edge start/end points are represented by transparent grey circles on edge ends. Snap points turn a transparent green when hovered over, and a solid green when selected. In order to snap an edge to an element, first click on either the edge start/end or the element snap point (it will turn solid green). Then click on the edge start/end or the element snap point that needs to be connected. The edge will automatically connect itself to the snap point, and remain connected until it&#39;s removed (by double clicking the edge and dragging the end point away, or connecting it to a different snap point). It is important to be mindful of accidentally selecting snap points, since this can have unintended consequences when attempting to move edges around. The solid green color of selected snap points is meant to alert the user to snap points they have currently selected. Snap points can be deselected by clicking on them a second time. Edges can also be reconnected to their original elements. When an edge is selected, visiting the &#34;Edit Selected&#34; menu link and then the &#34;Edit Edge&#34; link. This will bring up options to automatically connect the edge in different directions. <a href="http://cloudflare.jove.com/files/ftp_upload/54869/54869fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" src="/files/ftp_upload/54869/54869fig8.jpg" /> 
Figure 8: Disease Pathway Image. This is an example of a disease pathway that shows the organs affected by the disease (Sarcosine Oncometabolite pathway). Additional image elements are used to depict the increase or decrease in the metabolite concentrations and their accumulation or dissipation. <a href="http://cloudflare.jove.com/files/ftp_upload/54869/54869fig8large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 9" src="/files/ftp_upload/54869/54869fig9.jpg" /> 
Figure 9: Drug Pathway Image. This is an example of a drug pathway that shows the organs where the drug is metabolized (Ibuprofen Pathway). The color surrounding the drug metabolite is usually depicted as pink. <a href="http://cloudflare.jove.com/files/ftp_upload/54869/54869fig9large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 10" src="/files/ftp_upload/54869/54869fig10.jpg" /> 
Figure 10: Protein Signaling Pathway Image. This is an example of a signaling pathway that shows a collection of signaling reactions between different proteins (EGFR Pathway). Proteins can be depicted with multiple colors and they can be represented either by the protein name or the subunit name. <a href="http://cloudflare.jove.com/files/ftp_upload/54869/54869fig10large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 11" src="/files/ftp_upload/54869/54869fig11.jpg" /> 
Figure 11: Colorful vs Simple Pathway Images. Colorful pathways can be generated with rich biological context on either a white or blue background (a). Folate metabolism is depicted here. Simple, KEGG-like pathways can also be generated using a simple black and white representation (b). <a href="http://cloudflare.jove.com/files/ftp_upload/54869/54869fig11large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 12" src="/files/ftp_upload/54869/54869fig12.jpg" /> 
Figure 12: Suboptimal Pathway Image. An image depicting what a suboptimal pathway (TCA Cycle) looks like. Overlapping elements and crossing edges make the pathway incomprehensible. This can happen if the reaction elements are not carefully or correctly manipulated on the canvas. Manipulating the elements to have more than two different template types for the compounds (Large, Medium, Small Compound Visualization or Drug Visualization, Cofactor Visualization, Simple Bottom, Left Right or Top Visualization) leads to several image inconsistencies. The template types are shown in step 1.15.2. Not connecting the edges affects the flow of the image leading to poor interpretations of the pathway. <a href="http://cloudflare.jove.com/files/ftp_upload/54869/54869fig12large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td></td>
			<td>PathWhiz</td>
			<td>VANTED</td>
			<td>PathVisio</td>
			<td>Pathway Tools</td>
			<td>VisANT</td>
		</tr>
		<tr>
			<td>Web Server</td>
			<td>Yes</td>
			<td>No</td>
			<td>No</td>
			<td>No</td>
			<td>No</td>
		</tr>
		<tr>
			<td>Installable Program</td>
			<td>No</td>
			<td>Yes</td>
			<td>Yes</td>
			<td>Yes</td>
			<td>Yes</td>
		</tr>
		<tr>
			<td>Protein Pathways</td>
			<td>Yes</td>
			<td>Yes</td>
			<td>Yes</td>
			<td>No</td>
			<td>Yes</td>
		</tr>
		<tr>
			<td>Metabolic Pathways</td>
			<td>Yes</td>
			<td>Yes</td>
			<td>Yes</td>
			<td>Yes</td>
			<td>Yes</td>
		</tr>
		<tr>
			<td>Save as PNG/JPG</td>
			<td>Yes</td>
			<td>Yes</td>
			<td>Yes</td>
			<td>No</td>
			<td>No</td>
		</tr>
		<tr>
			<td>Save as HTML</td>
			<td>Yes</td>
			<td>No</td>
			<td>No</td>
			<td>Yes</td>
			<td>No</td>
		</tr>
		<tr>
			<td>Save as SVG</td>
			<td>Yes</td>
			<td>Yes</td>
			<td>Yes</td>
			<td>No</td>
			<td>Yes</td>
		</tr>
		<tr>
			<td>Save as PDF</td>
			<td>Yes</td>
			<td>Yes</td>
			<td>Yes</td>
			<td>Yes</td>
			<td>Yes</td>
		</tr>
		<tr>
			<td>Save as BioPAX</td>
			<td>Yes</td>
			<td>Yes</td>
			<td>Yes</td>
			<td>Yes</td>
			<td>Yes</td>
		</tr>
		<tr>
			<td>Save as SBML</td>
			<td>Yes</td>
			<td>Yes</td>
			<td>Yes</td>
			<td>Yes</td>
			<td>Yes</td>
		</tr>
		<tr>
			<td>Save as SBGN-ML</td>
			<td>Yes</td>
			<td>Yes</td>
			<td>Yes</td>
			<td>No</td>
			<td>No</td>
		</tr>
		<tr>
			<td>Identifier Mapping</td>
			<td>Yes</td>
			<td>Yes</td>
			<td>Yes</td>
			<td>Yes</td>
			<td>Yes</td>
		</tr>
		<tr>
			<td>Membrane Rendering</td>
			<td>Yes</td>
			<td>No</td>
			<td>No</td>
			<td>No</td>
			<td>No</td>
		</tr>
		<tr>
			<td>Organelle Rendering</td>
			<td>Yes</td>
			<td>No</td>
			<td>No</td>
			<td>No</td>
			<td>No</td>
		</tr>
		<tr>
			<td>Organ Rendering</td>
			<td>Yes</td>
			<td>No</td>
			<td>No</td>
			<td>No</td>
			<td>No</td>
		</tr>
		<tr>
			<td>Color Rich Images</td>
			<td>Yes</td>
			<td>No</td>
			<td>No</td>
			<td>No</td>
			<td>No</td>
		</tr>
		<tr>
			<td>Pathway Description</td>
			<td>Yes</td>
			<td>No</td>
			<td>No</td>
			<td>Yes</td>
			<td>No</td>
		</tr>
		<tr>
			<td>Pathway DB Link</td>
			<td>Yes</td>
			<td>No</td>
			<td>Yes</td>
			<td>Yes</td>
			<td>No</td>
		</tr>
		<tr>
			<td>Pathway Inference</td>
			<td>Yes</td>
			<td>No</td>
			<td>No</td>
			<td>Yes</td>
			<td>No</td>
		</tr>
		<tr>
			<td>Expt. Data Overlay</td>
			<td>No</td>
			<td>Yes</td>
			<td>No</td>
			<td>Yes</td>
			<td>Yes</td>
		</tr>
		<tr>
			<td>Pathway Analysis</td>
			<td>No</td>
			<td>Yes</td>
			<td>Yes</td>
			<td>Yes</td>
			<td>Yes</td>
		</tr>
	</tbody>
</table>
Table 1: Feature Comparison. A feature comparison of several common pathway editing/rendering tools.
Supplementary File 1: Example of TCA Cycle Description for PathWhiz Pathway. <a href="https://www.jove.com/files/ftp_upload/54869/Supplementary_material_-_The_citric_acid_cycle.doc">Click here to download the Supplementary File.</a>

Watch this Scientific Journal Video about A Web Tool for Generating High Quality Machine-readable Biological Pathways at JoVE.com

A Web Tool for Generating High Quality Machine-readable Biological Pathways

PathWhiz is a comprehensive, online pathway drawing tool for generating biochemical and biological pathways. It uses publicly accessible databases and easily expandable palettes consisting of pre-drawn pathway components. This protocol describes how to easily build new pathways, replicate and edit existing pathways, and propagate previously drawn pathways to different organisms.

PathWhiz is a web server built to facilitate the creation of colorful, interactive, visually pleasing pathway diagrams that are rich in biological ...

a-web-tool-for-generating-high-quality-machine-readable-biological

Research

JoVE Journal

Biology

17.4K Views.  University of Alberta.  PathWhiz is een uitgebreide, online traject tekenprogramma voor het genereren van biochemische en biologische pathways. Het maakt gebruik van publiek toegankelijke databanken en gemakkelijk uit te breiden paletten bestaande uit pre-getekende pathway componenten. Dit protocol beschrijft hoe u nieuwe wegen gemakkelijk op te bouwen, te repliceren en bestaande paden bewerken en verspreiden eerder getekende paden naar verschillende organismen. 

Video: A Web Tool for Generating High Quality Machine-readable Biological Pathways

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

This workflow allows novice researchers to leverage advanced computational resources such as cloud computing to carry out pairwise comparative transcriptomics. It also serves as a primer for biologists to develop data scientist computational skills, e.g. executing bash commands, visualization and management of large data sets. All command line code and further explanations of each command or step can be found on the wiki (<a href="https://wiki.cyverse.org/wiki/x/dgGtAQ">https://wiki.cyverse.org/wiki/x/dgGtAQ</a>). The Discovery Environment and Atmosphere platforms are connected together through the CyVerse Data Store. As such, once the initial raw sequencing data has been uploaded there is no more need to transfer large data files over an Internet connection, minimizing the amount of time needed to conduct analyses. This protocol is designed to analyze only two experimental treatments or conditions. Differential gene expression analysis is conducted through pairwise comparisons, and will not be suitable to test multiple factors. This workflow is also designed to be manual rather than automated. Each step must be executed and investigated by the user, yielding a better understanding of data and analytical outputs, and therefore better results for the user. Once complete, this protocol will yield de novo assembled transcriptome(s) for underserved (non-model) organisms without the need to map to previously assembled reference genomes (which are usually not available in underserved organism). These de novo transcriptomes are further used in pairwise differential gene expression analysis to investigate genes differing between two experimental conditions. Differentially expressed genes are then functionally annotated to understand the genetic response organisms have to experimental conditions. In total, the data derived from this protocol is used to test hypotheses about biological responses of underserved organisms.

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

This protocol outlines a comparative de novo transcriptome assembly and annotation workflow for novice bioinformaticians. The workflow is available for free entirely through CyVerse and connected by the Data Store. Command line and graphical user interfaces are used, but all code needed is available to copy and paste.

This workflow allows novice researchers to leverage advanced computational resources such as cloud computing to carry out pairwise comparative ...

Genetics

Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish

The analysis of global gene expression changes is a valuable tool for identifying novel pathways underlying observed phenotypes. The zebrafish is an excellent model for rapid assessment of whole transcriptome from whole animal or individual cell populations due to the ease of isolation of RNA from large numbers of animals. Here a protocol for global gene expression analysis in zebrafish embryos using RNA sequencing (RNASeq) is presented. We describe preparation of RNA from whole embryos or from cell populations obtained using cell sorting in transgenic animals. We also describe an approach for analysis of RNASeq data to identify enriched pathways and Gene Ontology (GO) terms in global gene expression data sets. Finally, we provide a protocol for validation of gene expression changes using quantitative reverse transcriptase PCR (qRT-PCR). These protocols can be used for comparative analysis of control and experimental sets of zebrafish to identify novel gene expression changes, and provide molecular insight into phenotypes of interest.

This protocol presents an approach for whole transcriptome analysis from zebrafish embryos, larvae, or sorted cells. We include isolation of RNA, pathway analysis of RNASeq data, and qRT-PCR-based validation of gene expression changes.

The analysis of global gene expression changes is a valuable tool for identifying novel pathways underlying observed phenotypes. The zebrafish is an ...

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Biomarker detection is one of the more important biomedical questions for high-throughput &#39;omics&#39; researchers, and almost all existing biomarker detection algorithms generate one biomarker subset with the optimized performance measurement for a given dataset. However, a recent study demonstrated the existence of multiple biomarker subsets with similarly effective or even identical classification performances. This protocol presents a simple and straightforward methodology for detecting biomarker subsets with binary classification performances, better than a user-defined cutoff. The protocol consists of data preparation and loading, baseline information summarization, parameter tuning, biomarker screening, result visualization and interpretation, biomarker gene annotations, and result and visualization exportation at publication quality. The proposed biomarker screening strategy is intuitive and demonstrates a general rule for developing biomarker detection algorithms. A user-friendly graphical user interface (GUI) was developed using the programming language Python, allowing biomedical researchers to have direct access to their results. The source code and manual of kSolutionVis can be downloaded from http://www.healthinformaticslab.org/supp/resources.php.

Existing algorithms generate one solution for a biomarker detection dataset. This protocol demonstrates the existence of multiple similarly effective solutions and presents a user-friendly software to help biomedical researchers investigate their datasets for the proposed challenge. Computer scientists may also provide this feature in their biomarker detection algorithms.

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Cancer Research

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

Through whole-exome/genome sequencing, human geneticists identify rare variants that segregate with disease phenotypes. To assess if a specific variant is pathogenic, one must query many databases to determine whether the gene of interest is linked to a genetic disease, whether the specific variant has been reported before, and what functional data is available in model organism databases that may provide clues about the gene&#8217;s function in human. MARRVEL (Model organism Aggregated Resources for Rare Variant ExpLoration) is a one-stop data collection tool for human genes and variants and their orthologous genes in seven model organisms including in mouse, rat, zebrafish, fruit fly, nematode worm, fission yeast, and budding yeast. In this Protocol, we provide an overview of what MARRVEL can be used for and discuss how different datasets can be used to assess whether a variant of unknown significance (VUS) in a known disease-causing gene or a variant in a gene of uncertain significance (GUS) may be pathogenic. This protocol will guide a user through searching multiple human databases simultaneously starting with a human gene with or without a variant of interest. We also discuss how to utilize data from OMIM, ExAC/gnomAD, ClinVar, Geno2MP, DGV and DECHIPHER. Moreover, we illustrate how to interpret a list of ortholog candidate genes, expression patterns, and GO terms in model organisms associated with each human gene. Furthermore, we discuss the value protein structural domain annotations provided and explain how to use the multiple species protein alignment feature to assess whether a variant of interest affects an evolutionarily conserved domain or amino acid. Finally, we will discuss three different use-cases of this website. MARRVEL is an easily accessible open access website designed for both clinical and basic researchers and serves as a starting point to design experiments for functional studies.

Here, we present a protocol to access and analyze many human and model organism databases efficiently. This protocol demonstrates the use of MARRVEL to analyze candidate disease-causing variants identified from next-generation sequencing efforts.

Through whole-exome/genome sequencing, human geneticists identify rare variants that segregate with disease phenotypes. To assess if a specific variant is ...

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Recently, a new implementation of a previously described method for interpreting genome-wide association study (GWAS) data using metabolic pathway analysis has been developed and released. The Pathway Association Study Tool (PAST) was developed to address concerns with user-friendliness and slow-running analyses. This new user-friendly tool has been released on Bioconductor and Github. In testing, PAST ran analyses in less than one hour that previously required twenty-four or more hours. In this article, we present the protocol for using either the Shiny application or the R console to run PAST.

By running the Pathway Association Study Tool (PAST), either through the Shiny application or through the R console, researchers can gain a deeper understanding of the biological meaning of their genome-wide association study (GWAS) results by investigating the metabolic pathways involved.

Recently, a new implementation of a previously described method for interpreting genome-wide association study (GWAS) data using metabolic pathway ...

Beginner-Friendly Single-Language Pipeline for CUT&RUN Data Analysis

Introductory Analysis and Validation of CUT&#38;RUN Sequencing Data

The CUT&amp;RUN technique facilitates detection of protein-DNA interactions across the genome. Typical applications of CUT&amp;RUN include profiling changes in histone tail modifications or mapping transcription factor chromatin occupancy. Widespread adoption of CUT&amp;RUN is driven, in part, by technical advantages over conventional ChIP-seq that include lower cell input requirements, lower sequencing depth requirements, and increased sensitivity with reduced background signal due to a lack of cross-linking agents that otherwise mask antibody epitopes. Widespread adoption of CUT&amp;RUN has also been achieved through the generous sharing of reagents by the Henikoff lab and the development of commercial kits to accelerate adoption for beginners. As technical adoption of CUT&amp;RUN increases, CUT&amp;RUN sequencing analysis and validation become critical bottlenecks that must be surmounted to enable complete adoption by predominantly wet lab teams. CUT&amp;RUN analysis typically begins with quality control checks on raw sequencing reads to assess sequencing depth, read quality, and potential biases. Reads are then aligned to a reference genome sequence assembly, and several bioinformatics tools are subsequently employed to annotate genomic regions of protein enrichment, confirm data interpretability, and draw biological conclusions. Although multiple in silico analysis pipelines have been developed to support CUT&amp;RUN data analysis, their complex multi-module structure and usage of multiple programming languages render the platforms difficult for bioinformatics beginners who may lack familiarity with multiple programming languages but wish to understand the CUT&amp;RUN analysis procedure and customize their analysis pipelines. Here, we provide a single-language step-by-step CUT&amp;RUN analysis pipeline protocol designed for users with any level of bioinformatics experience. This protocol includes completing critical quality checks to validate that the sequencing data is suitable for biological interpretation. We expect that following the introductory protocol provided in this article combined with downstream peak annotation will allow users to draw biological insights from their own CUT&amp;RUN datasets.

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

This protocol guides bioinformatics beginners through an introductory CUT&amp;RUN analysis pipeline that enables users to complete an initial analysis and validation of CUT&amp;RUN sequencing data. Completing the analysis steps described here, combined with downstream peak annotation, will allow users to draw mechanistic insights into chromatin regulation.

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Pipeline for Analyzing Microarray Data in Esophageal Cancer Research

Development of Compendium for Esophageal Squamous Cell Carcinoma

Esophageal cancer (EC) ranks as the 8th most aggressive malignancy, and its treatment remains challenging due to the lack of biomarkers facilitating early detection. EC manifests in two major histological forms - adenocarcinoma (EAD) and squamous cell carcinoma (ESCC) - both exhibiting variations in incidence across geographically distinct populations. High-throughput technologies are transforming the understanding of diseases, including cancer. A significant challenge for the scientific community is dealing with scattered data in the literature. To address this, a simple pipeline is proposed for the analysis of publicly available microarray datasets and the collection of differentially regulated molecules between cancer and normal conditions. The pipeline can serve as a standard approach for differential gene expression analysis, identifying genes differentially expressed between cancer and normal tissues or among different cancer subtypes. The pipeline involves several steps, including Data preprocessing (involving quality control and normalization of raw gene expression data to remove technical variations between samples), Differential expression analysis (identifying genes differentially expressed between two or more groups of samples using statistical tests such as t-tests, ANOVA, or linear models), Functional analysis (using bioinformatics tools to identify enriched biological pathways and functions in differentially expressed genes), and Validation (involving validation using independent datasets or experimental methods such as qPCR or immunohistochemistry). Using this pipeline, a collection of differentially expressed molecules (DEMs) can be generated for any type of cancer, including esophageal cancer. This compendium can be utilized to identify potential biomarkers and drug targets for cancer and enhance understanding of the molecular mechanisms underlying the disease. Additionally, population-specific screening of esophageal cancer using this pipeline will help identify specific drug targets for distinct populations, leading to personalized treatments for the disease.

This protocol describes a useful tool for identifying significant molecular changes in cancer and leads to the development of new diagnostic and therapeutic approaches for esophageal squamous cell carcinoma.

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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

The rapidly increasing and vast quantities of biomedical reports, each containing numerous entities and rich information, represent a rich resource for biomedical text-mining applications. These tools enable investigators to integrate, conceptualize, and translate these discoveries to uncover new insights into disease pathology and therapeutics. In this protocol, we present CaseOLAP LIFT, a new computational pipeline to investigate cellular components and their disease associations by extracting user-selected information from text datasets (e.g., biomedical literature). The software identifies sub-cellular proteins and their functional partners within disease-relevant documents. Additional disease-relevant documents are identified via the software's label imputation method. To contextualize the resulting protein-disease associations and to integrate information from multiple relevant biomedical resources, a knowledge graph is automatically constructed for further analyses. We present one use case with a corpus of ~34 million text documents downloaded online to provide an example of elucidating the role of mitochondrial proteins in distinct cardiovascular disease phenotypes using this method. Furthermore, a deep learning model was applied to the resulting knowledge graph to predict previously unreported relationships between proteins and disease, resulting in 1,583 associations with predicted probabilities &gt;0.90 and with an area under the receiver operating characteristic curve (AUROC) of 0.91 on the test set. This software features a highly customizable and automated workflow, with a broad scope of raw data available for analysis; therefore, using this method, protein-disease associations can be identified with enhanced reliability within a text corpus.

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A computational protocol, CaseOLAP LIFT, and a use case are presented for investigating mitochondrial proteins and their associations with cardiovascular disease as described in biomedical reports. This protocol can be easily adapted to study user-selected cellular components and diseases.

The rapidly increasing and vast quantities of biomedical reports, each containing numerous entities and rich information, represent a rich resource for ...

Biochemistry

A Practical Guide to Phylogenetics for Nonexperts

Many researchers, across incredibly diverse foci, are applying phylogenetics to their research question(s). However, many researchers are new to this topic and so it presents inherent problems. Here we compile a practical introduction to phylogenetics for nonexperts.&#160;We outline in a step-by-step manner, a pipeline for generating reliable phylogenies from gene sequence datasets. We begin with a user-guide for similarity search tools via online interfaces as well as local executables. Next, we explore programs for generating multiple sequence alignments followed by protocols for using software to determine best-fit models of evolution. We then outline protocols for reconstructing phylogenetic relationships via maximum likelihood and Bayesian criteria&#160;and finally describe tools for visualizing phylogenetic trees. While this is not by any means an exhaustive description of phylogenetic approaches, it does provide the reader with practical starting information on key software applications commonly utilized by phylogeneticists. The vision for this article would be that it could serve as a practical training tool for researchers embarking on phylogenetic studies&#160;and also serve as an educational resource that could be incorporated into a classroom or teaching-lab.

Here we describe a step-by-step pipeline for generating reliable phylogenies from nucleotide or amino acid sequence datasets. This guide aims to serve researchers or students new to phylogenetic analysis.&#160;

Many researchers, across incredibly diverse foci, are applying phylogenetics to their research question(s). However, many researchers are new to this ...

Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine

Classification and nomenclature of genes in a family can significantly contribute to the description of the diversity of encoded proteins and to the prediction of family functions based on several features, such as the presence of sequence motifs or of particular sites for post-translational modification and the expression profile of family members in different conditions. This work describes a detailed protocol for gene family characterization. Here, the procedure is applied to the characterization of the Arabidopsis T&#243;xicos in Levadura (ATL) E3 ubiquitin ligase family in grapevine. The methods include the genome-wide identification of family members, the characterization of gene localization, structure, and duplication, the analysis of conserved protein motifs, the prediction of protein localization and phosphorylation sites as well as gene expression profiling across the family in different datasets. Such procedure, which could be extended to further analyses depending on experimental purposes, could be applied to any gene family in any plant species for which genomic data are available, and it provides valuable information to identify interesting candidates for functional studies, giving insights into the molecular mechanisms of plant adaptation to their environment.

This article describes the procedure for the identification and characterization of a gene family in grapevine applied to the family of Arabidopsis T&#243;xicos in Levadura (ATL) E3 ubiquitin ligases.

Classification and nomenclature of genes in a family can significantly contribute to the description of the diversity of encoded proteins and to the ...

A Web Tool for Generating High Quality Machine-readable Biological Pathways

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