Web-based metabolic network visualization with a zooming user interface

<p>Abstract</p> <p>Background</p> <p>Displaying complex metabolic-map diagrams, for Web browsers, and allowing users to interact with them for querying and overlaying expression data over them is challenging.</p> <p>Description</p> <p>We present a Web-based metabolic-map diagram, which can be interactively explored by the user, called the <it>Cellular Overview</it>. The main characteristic of this application is the zooming user interface enabling the user to focus on appropriate granularities of the network at will. Various searching commands are available to visually highlight sets of reactions, pathways, enzymes, metabolites, and so on. Expression data from single or multiple experiments can be overlaid on the diagram, which we call the Omics Viewer capability. The application provides Web services to highlight the diagram and to invoke the <it>Omics Viewer</it>. This application is entirely written in JavaScript for the client browsers and connect to a Pathway Tools Web server to retrieve data and diagrams. It uses the OpenLayers library to display tiled diagrams.</p> <p>Conclusions</p> <p>This new online tool is capable of displaying large and complex metabolic-map diagrams in a very interactive manner. This application is available as part of the Pathway Tools software that powers multiple metabolic databases including <monospace>Biocyc.org</monospace>: The Cellular Overview is accessible under the <monospace>Tools</monospace> menu.</p

NaviCell: a web-based environment for navigation, curation and maintenance of large molecular interaction maps

Molecular biology knowledge can be systematically represented in a computer-readable form as a comprehensive map of molecular interactions. There exist a number of maps of molecular interactions containing detailed description of various cell mechanisms. It is difficult to explore these large maps, to comment their content and to maintain them. Though there exist several tools addressing these problems individually, the scientific community still lacks an environment that combines these three capabilities together. NaviCell is a web-based environment for exploiting large maps of molecular interactions, created in CellDesigner, allowing their easy exploration, curation and maintenance. NaviCell combines three features: (1) efficient map browsing based on Google Maps engine; (2) semantic zooming for viewing different levels of details or of abstraction of the map and (3) integrated web-based blog for collecting the community feedback. NaviCell can be easily used by experts in the field of molecular biology for studying molecular entities of their interest in the context of signaling pathways and cross-talks between pathways within a global signaling network. NaviCell allows both exploration of detailed molecular mechanisms represented on the map and a more abstract view of the map up to a top-level modular representation. NaviCell facilitates curation, maintenance and updating the comprehensive maps of molecular interactions in an interactive fashion due to an imbedded blogging system. NaviCell provides an easy way to explore large-scale maps of molecular interactions, thanks to the Google Maps and WordPress interfaces, already familiar to many users. Semantic zooming used for navigating geographical maps is adopted for molecular maps in NaviCell, making any level of visualization meaningful to the user. In addition, NaviCell provides a framework for community-based map curation.Comment: 20 pages, 5 figures, submitte

Visualization of metabolic interaction networks in microbial communities using VisANT 5.0

The complexity of metabolic networks in microbial communities poses an unresolved visualization and interpretation challenge. We address this challenge in the newly expanded version of a software tool for the analysis of biological networks, VisANT 5.0. We focus in particular on facilitating the visual exploration of metabolic interaction between microbes in a community, e.g. as predicted by COMETS (Computation of Microbial Ecosystems in Time and Space), a dynamic stoichiometric modeling framework. Using VisANT's unique metagraph implementation, we show how one can use VisANT 5.0 to explore different time-dependent ecosystem-level metabolic networks. In particular, we analyze the metabolic interaction network between two bacteria previously shown to display an obligate cross-feeding interdependency. In addition, we illustrate how a putative minimal gut microbiome community could be represented in our framework, making it possible to highlight interactions across multiple coexisting species. We envisage that the "symbiotic layout" of VisANT can be employed as a general tool for the analysis of metabolism in complex microbial communities as well as heterogeneous human tissues.This work was supported by the National Institutes of Health, R01GM103502-05 to CD, ZH and DS. Partial support was also provided by grants from the Office of Science (BER), U.S. Department of Energy (DE-SC0004962), the Joslin Diabetes Center (Pilot & Feasibility grant P30 DK036836), the Army Research Office under MURI award W911NF-12-1-0390, National Institutes of Health (1RC2GM092602-01, R01GM089978 and 5R01DE024468), NSF (1457695), and Defense Advanced Research Projects Agency Biological Technologies Office (BTO), Program: Biological Robustness In Complex Settings (BRICS), Purchase Request No. HR0011515303, Program Code: TRS-0 Issued by DARPA/CMO under Contract No. HR0011-15-C-0091. Funding for open access charge: National Institutes of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. (R01GM103502-05 - National Institutes of Health; 1RC2GM092602-01 - National Institutes of Health; R01GM089978 - National Institutes of Health; 5R01DE024468 - National Institutes of Health; DE-SC0004962 - Office of Science (BER), U.S. Department of Energy; P30 DK036836 - Joslin Diabetes Center; W911NF-12-1-0390 - Army Research Office under MURI; 1457695 - NSF; HR0011515303 - Defense Advanced Research Projects Agency Biological Technologies Office (BTO), Program: Biological Robustness In Complex Settings (BRICS); HR0011-15-C-0091 - DARPA/CMO; National Institutes of Health)Published versio

BirdsEyeView (BEV): graphical overviews of experimental data

Background: Analyzing global experimental data can be tedious and time-consuming. Thus, helping biologists see results as quickly and easily as possible can facilitate biological research, and is the purpose of the software we describe. Results: We present BirdsEyeView, a software system for visualizing experimental transcriptomic data using different views that users can switch among and compare. BirdsEyeView graphically maps data to three views: Cellular Map (currently a plant cell), Pathway Tree with dynamic mapping, and Gene Ontology http://www. geneontology.org Biological Processes and Molecular Functions. By displaying color-coded values for transcript levels across different views, BirdsEyeView can assist users in developing hypotheses about their experiment results. Conclusions: BirdsEyeView is a software system available as a Java Webstart package for visualizing transcriptomic data in the context of different biological views to assist biologists in investigating experimental results. BirdsEyeView can be obtained from http://metnetdb.org/MetNet_BirdsEyeView.htm

The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases

The MetaCyc database (http://metacyc.org/) provides a comprehensive and freely accessible resource for metabolic pathways and enzymes from all domains of life. The pathways in MetaCyc are experimentally determined, small-molecule metabolic pathways and are curated from the primary scientific literature. MetaCyc contains more than 1800 pathways derived from more than 30 000 publications, and is the largest curated collection of metabolic pathways currently available. Most reactions in MetaCyc pathways are linked to one or more well-characterized enzymes, and both pathways and enzymes are annotated with reviews, evidence codes and literature citations. BioCyc (http://biocyc.org/) is a collection of more than 1700 organism-specific Pathway/Genome Databases (PGDBs). Each BioCyc PGDB contains the full genome and predicted metabolic network of one organism. The network, which is predicted by the Pathway Tools software using MetaCyc as a reference database, consists of metabolites, enzymes, reactions and metabolic pathways. BioCyc PGDBs contain additional features, including predicted operons, transport systems and pathway-hole fillers. The BioCyc website and Pathway Tools software offer many tools for querying and analysis of PGDBs, including Omics Viewers and comparative analysis. New developments include a zoomable web interface for diagrams; flux-balance analysis model generation from PGDBs; web services; and a new tool called Web Groups

Construction and completion of flux balance models from pathway databases

Motivation: Flux balance analysis (FBA) is a well-known technique for genome-scale modeling of metabolic flux. Typically, an FBA formulation requires the accurate specification of four sets: biochemical reactions, biomass metabolites, nutrients and secreted metabolites. The development of FBA models can be time consuming and tedious because of the difficulty in assembling completely accurate descriptions of these sets, and in identifying errors in the composition of these sets. For example, the presence of a single non-producible metabolite in the biomass will make the entire model infeasible. Other difficulties in FBA modeling are that model distributions, and predicted fluxes, can be cryptic and difficult to understand

Performance optimisation of biological pathway data storage, retrieval, analysis and its interactive visualisation

The aim of this research was to optimise the performance of the storage, retrieval, analysis and interactive visualisation of biomolecular pathways data. This was achieved by the adoption of new technologies and a variety of highly optimised data structures, algorithms and strategies across the different layers of the software. The first challenge to overcome was the creation of a long-lasting, large-scale web application to enable pathways navigation; the Pathway Browser. This tool had to aggregate different modules to allow users to browse pathway content and use their own data to perform pathway analysis. Another challenge was the development of a high-performance pathway analysis tool to enable the analysis of genome-wide datasets within seconds. Once developed, it was also integrated into the Pathway Browser allowing interactive exploration and analysis of high throughput data. The Pathways Overview layout and widget were created to enable the representation of the complex parent-child relationships present in the pathways hierarchical organisation. This module provides a means to overlay analysis results in such a way that the user can easily distinguish the most significant areas of biology represented in their data. Although an existing force-directed layout algorithm was initially utilised for the graphical representation, it did not achieve the expected results and a custom radial layout algorithm was developed instead. A new version of the pathway Diagram Viewer was engineered to achieve loading and rendering of 97% of the target diagrams in less than 1 second. Combining the multi-layer HTML5 Canvas strategy with a space partitioning data structure minimised CPU workload, enabling the introduction of new features that further enhance user experience. On the server side, the work focused on the adoption of a graph database (Neo4j) and the creation of the new Content Service (REST API) that provides access to these data. The Neo4j graph database and its query language, Cypher, enabled efficient access to the complex pathway data model, facilitating easy traversal and knowledge discovery. The adoption of this technology greatly improved query efficiency, reducing the average query time by 93%

Linking phenotype to genotype in pseudonas aeruginosa

The global transcriptional regulator mexT, is a mutational hotspot; the sequence variants commonly seen to co-exist within the P. aeruginosa population are: drug susceptible (e.g. PAO1) and chloramphenicol and norfloxacin non-susceptible (nfxC mutant). The nfxC phenotype, selected for on chloramphenicol agar is characterised by reduced virulence. The conversion between PAO1 and nfxC phenotypes is associated with an 8-bp repeat sequence in mexT. To investigate the effects of the 8-bp repeat on the adaptive mode of survival of P. aeruginosa, isogenic mutants were generated: PA (8-bp, two copies) and PAdel (8-bp, one copy). The mutants were characterised using phenotypic microarrays (PM), motility, antibiotic susceptibility, Galleria virulence models and RNA-seq in defined media. PM revealed differences in central metabolism indicating that PAdel/PAnfxC were associated with a biological metabolic cost. Strains with the single copy of the 8-bp sequence showed reduced motility and virulence. Transcriptome analysis revealed that mexT, in PA, consists of two regulatory elements defined by an intact helix-turn-helix motif (across the repeat region) which is capable of regulating the downstream LysR region via repressor and autoregulative mechanisms. Whole genome sequencing identified regions of compensatory mutations that were associated with differences in phenotype between PAdel (genetically modified) and PAnfxC (selected). To link phenotype and genotype and to understand the metabolic effects of this mutation, a genome wide metabolic reconstruction was performed. This revealed differences in key metabolic pathways such as glycolysis, gluconeogenesis and oxidative phosphorylation. This study has shown that an 8-bp repeat in mexT is a driver of genetic diversity. Regulatory elements linked to the effect of the 8-bp sequence on antibiotic resistance, central metabolism, chemotaxis, motility and virulence have also been identified. These methods can be used to define phenotype in any pair of isogenic mutants, at the genome level, and to investigate the clinical risk of strains