The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular ...profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics.
Visualization is a key recurring requirement for effective analysis of relational data. Biology is no exception. It is imperative to annotate and render biological models in standard, widely accepted ...formats. Finding graph-theoretical properties of pathways as well as identifying certain paths or subgraphs of interest in a pathway are also essential for effective analysis of pathway data. Given the size of available biological pathway data nowadays, automatic layout is crucial in understanding the graphical representations of such data. Even though there are many available software tools that support graphical display of biological pathways in various formats, there is none available as a service for on-demand or batch processing of biological pathways for automatic layout, customized rendering and mining paths or subgraphs of interest. In addition, there are many tools with fine rendering capabilities lacking decent automatic layout support. To fill this void, we developed a web service named SyBLaRS (Systems Biology Layout and Rendering Service) for automatic layout of biological data in various standard formats as well as construction of customized images in both raster image and scalable vector formats of these maps. Some of the supported standards are more generic such as GraphML and JSON, whereas others are specialized to biology such as SBGNML (The Systems Biology Graphical Notation Markup Language) and SBML (The Systems Biology Markup Language). In addition, SyBLaRS supports calculation and highlighting of a number of well-known graph-theoretical properties as well as some novel graph algorithms turning a specified set of objects of interest to a minimal pathway of interest. We demonstrate that SyBLaRS can be used both as an offline layout and rendering service to construct customized and annotated pictures of pathway models and as an online service to provide layout and rendering capabilities for systems biology software tools. SyBLaRS is open source and publicly available on GitHub and freely distributed under the MIT license. In addition, a sample deployment is available here for public consumption.
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Abstract
Pathway Commons (https://www.pathwaycommons.org) is an integrated resource of publicly available information about biological pathways including biochemical reactions, assembly of ...biomolecular complexes, transport and catalysis events and physical interactions involving proteins, DNA, RNA, and small molecules (e.g. metabolites and drug compounds). Data is collected from multiple providers in standard formats, including the Biological Pathway Exchange (BioPAX) language and the Proteomics Standards Initiative Molecular Interactions format, and then integrated. Pathway Commons provides biologists with (i) tools to search this comprehensive resource, (ii) a download site offering integrated bulk sets of pathway data (e.g. tables of interactions and gene sets), (iii) reusable software libraries for working with pathway information in several programming languages (Java, R, Python and Javascript) and (iv) a web service for programmatically querying the entire dataset. Visualization of pathways is supported using the Systems Biological Graphical Notation (SBGN). Pathway Commons currently contains data from 22 databases with 4794 detailed human biochemical processes (i.e. pathways) and ∼2.3 million interactions. To enhance the usability of this large resource for end-users, we develop and maintain interactive web applications and training materials that enable pathway exploration and advanced analysis.
The cBio Cancer Genomics Portal (http://cbioportal.org) is an open-access resource for interactive exploration of multidimensional cancer genomics data sets, currently providing access to data from ...more than 5,000 tumor samples from 20 cancer studies. The cBio Cancer Genomics Portal significantly lowers the barriers between complex genomic data and cancer researchers who want rapid, intuitive, and high-quality access to molecular profiles and clinical attributes from large-scale cancer genomics projects and empowers researchers to translate these rich data sets into biologic insights and clinical applications.
Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. The rapid growth of ...the volume of pathway data has spurred the development of databases and computational tools to aid interpretation; however, use of these data is hampered by the current fragmentation of pathway information across many databases with incompatible formats. BioPAX, which was created through a community process, solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. Using BioPAX, millions of interactions, organized into thousands of pathways, from many organisms are available from a growing number of databases. This large amount of pathway data in a computable form will support visualization, analysis and biological discovery.
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This paper describes a new automatic layout algorithm named CoSEP for compound graphs with port constraints. The algorithm works by extending the physical model of a previous algorithm named CoSE by ...defining additional force types and heuristics for constraining edges to connect to certain user-defined locations on end nodes. Similar to its predecessor, CoSEP also accounts for non-uniform node dimensions and arbitrary levels of nesting via compound nodes. Our experiments show that CoSEP significantly improves the quality of the layouts for compound graphs with port constraints with respect to commonly accepted graph drawing criteria while running reasonably fast, suitable for use in interactive applications for small to medium-sized (up to 500 nodes) graphs. A complete JavaScript implementation of CoSEP as a Cytoscape.js extension along with a demo page is freely available at https://github.com/iVis-at-Bilkent/cytoscape.js-cosep.
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One common problem in visualizing real-life networks, including biological pathways, is the large size of these networks. Often times, users find themselves facing slow, non-scaling operations due to ...network size, if not a "hairball" network, hindering effective analysis. One extremely useful method for reducing complexity of large networks is the use of hierarchical clustering and nesting, and applying expand-collapse operations on demand during analysis. Another such method is hiding currently unnecessary details, to later gradually reveal on demand. Major challenges when applying complexity reduction operations on large networks include efficiency and maintaining the user's mental map of the drawing.
We developed specialized incremental layout methods for preserving a user's mental map while managing complexity of large networks through expand-collapse and hide-show operations. We also developed open-source JavaScript libraries as plug-ins to the web based graph visualization library named Cytsocape.js to implement these methods as complexity management operations. Through efficient specialized algorithms provided by these extensions, one can collapse or hide desired parts of a network, yielding potentially much smaller networks, making them more suitable for interactive visual analysis.
This work fills an important gap by making efficient implementations of some already known complexity management techniques freely available to tool developers through a couple of open source, customizable software libraries, and by introducing some heuristics which can be applied upon such complexity management techniques to ensure preserving mental map of users.
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Visual analysis of relational information is vital in most real-life analytics applications. Automatic layout is a key requirement for effective visual display of such information. This article ...introduces a new layout algorithm named fCoSE for compound graphs showing varying levels of groupings or abstractions with support for user-specified placement constraints. fCoSE builds on a previous compound spring embedder layout algorithm and makes use of the spectral graph drawing technique for producing a quick draft layout, followed by phases where constraints are enforced and compound structures are properly shown while polishing the layout with respect to commonly accepted graph layout criteria. Experimental evaluation verifies that fCoSE produces quality layouts and is fast enough for interactive applications with small to medium-sized graphs by combining the speed of spectral graph drawing technique with the quality of force-directed layout algorithms while satisfying specified constraints and properly displaying compound structures. An implementation of fCoSE along with documentation and a demo page is freely available on GitHub at https://github.com/iVis-at-Bilkent/cytoscape.js-fcose .
Graph-based pathway ontologies and databases are widely used to represent data about cellular processes. This representation makes it possible to programmatically integrate cellular networks and to ...investigate them using the well-understood concepts of graph theory in order to predict their structural and dynamic properties. An extension of this graph representation, namely hierarchically structured or compound graphs, in which a member of a biological network may recursively contain a sub-network of a somehow logically similar group of biological objects, provides many additional benefits for analysis of biological pathways, including reduction of complexity by decomposition into distinct components or modules. In this regard, it is essential to effectively query such integrated large compound networks to extract the sub-networks of interest with the help of efficient algorithms and software tools.
Towards this goal, we developed a querying framework, along with a number of graph-theoretic algorithms from simple neighborhood queries to shortest paths to feedback loops, that is applicable to all sorts of graph-based pathway databases, from PPIs (protein-protein interactions) to metabolic and signaling pathways. The framework is unique in that it can account for compound or nested structures and ubiquitous entities present in the pathway data. In addition, the queries may be related to each other through "AND" and "OR" operators, and can be recursively organized into a tree, in which the result of one query might be a source and/or target for another, to form more complex queries. The algorithms were implemented within the querying component of a new version of the software tool PATIKAweb (Pathway Analysis Tool for Integration and Knowledge Acquisition) and have proven useful for answering a number of biologically significant questions for large graph-based pathway databases.
The PATIKA Project Web site is http://www.patika.org. PATIKAweb version 2.1 is available at http://web.patika.org.
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LibSBGN is a software library for reading, writing and manipulating Systems Biology Graphical Notation (SBGN) maps stored using the recently developed SBGN-ML file format. The library (available in ...C++ and Java) makes it easy for developers to add SBGN support to their tools, whereas the file format facilitates the exchange of maps between compatible software applications. The library also supports validation of maps, which simplifies the task of ensuring compliance with the detailed SBGN specifications. With this effort we hope to increase the adoption of SBGN in bioinformatics tools, ultimately enabling more researchers to visualize biological knowledge in a precise and unambiguous manner.
Milestone 2 was released in December 2011. Source code, example files and binaries are freely available under the terms of either the LGPL v2.1+ or Apache v2.0 open source licenses from http://libsbgn.sourceforge.net.
sbgn-libsbgn@lists.sourceforge.net.
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