Search

BackgroundDifferential correlation networks are increasingly used to delineate changes in interactions among biomolecules. They characterize differences between omics networks under two different conditions, and can be used to delineate mechanisms of disease initiation and progression.ResultsWe present a new R package, , that facilitates the estimation and visualization of differential correlation networks using multiple correlation measures and inference methods. The software is implemented in , and , and is available athttps://github.com/sqyu/CorDiffViz. Visualization has been tested for the Chrome and Firefox web browsers. A demo is available athttps://diffcornet.github.io/CorDiffViz/demo.html.ConclusionsOur software offers considerable flexibility by allowing the user to interact with the visualization and choose from different estimation methods and visualizations. It also allows the user to easily toggle between correlation networks for samples under one condition and differential correlations between samples under two conditions. Moreover, the software facilitates integrative analysis of cross-correlation networks between two omics data sets.

Background Biomedical translational science is increasingly using computational reasoning on repositories of structured knowledge (such as UMLS, SemMedDB, ChEMBL, Reactome, DrugBank, and SMPDB in order to facilitate discovery of new therapeutic targets and modalities. The NCATS Biomedical Data Translator project is working to federate autonomous reasoning agents and knowledge providers within a distributed system for answering translational questions. Within that project and the broader field, there is a need for a framework that can efficiently and reproducibly build an integrated, standards-compliant, and comprehensive biomedical knowledge graph that can be downloaded in standard serialized form or queried via a public application programming interface (API). Results To create a knowledge provider system within the Translator project, we have developed RTX-KG2, an open-source software system for building—and hosting a web API for querying—a biomedical knowledge graph that uses an Extract-Transform-Load approach to integrate 70 knowledge sources (including the aforementioned core six sources) into a knowledge graph with provenance information including (where available) citations. The semantic layer and schema for RTX-KG2 follow the standard Biolink model to maximize interoperability. RTX-KG2 is currently being used by multiple Translator reasoning agents, both in its downloadable form and via its SmartAPI-registered interface. Serializations of RTX-KG2 are available for download in both the pre-canonicalized form and in canonicalized form (in which synonyms are merged). The current canonicalized version (KG2.7.3) of RTX-KG2 contains 6.4M nodes and 39.3M edges with a hierarchy of 77 relationship types from Biolink. Conclusion RTX-KG2 is the first knowledge graph that integrates UMLS, SemMedDB, ChEMBL, DrugBank, Reactome, SMPDB, and 64 additional knowledge sources within a knowledge graph that conforms to the Biolink standard for its semantic layer and schema. RTX-KG2 is publicly available for querying via its API at arax.rtx.ai/api/rtxkg2/v1.2/openapi.json. The code to build RTX-KG2 is publicly available at github:RTXteam/RTX-KG2.

Background Genome-wide association studies (GWAS) are typically visualized using a two-dimensional Manhattan plot, displaying chromosomal location of SNPs along the x-axis and the negative log-10 of their p-value on the y-axis. This traditional plot provides a broad overview of the results, but offers little opportunity for interaction or expansion of specific regions, and is unable to show additional dimensions of the dataset. Results We created BigTop, a visualization framework in virtual reality (VR), designed to render a Manhattan plot in three dimensions, wrapping the graph around the user in a simulated cylindrical room. BigTop uses the z-axis to display minor allele frequency of each SNP, allowing for the identification of allelic variants of genes. BigTop also offers additional interactivity, allowing users to select any individual SNP and receive expanded information, including SNP name, exact values, and gene location, if applicable. BigTop is built in JavaScript using the React and A-Frame frameworks, and can be rendered using commercially available VR headsets or in a two-dimensional web browser such as Google Chrome. Data is read into BigTop in JSON format, and can be provided as either JSON or a tab-separated text file. Conclusions Using additional dimensions and interactivity options offered through VR, we provide a new, interactive, three-dimensional representation of the traditional Manhattan plot for displaying and exploring GWAS data.

Background The Ageing Factor Database AgeFactDB contains a large number of lifespan observations for ageing-related factors like genes, chemical compounds, and other factors such as dietary restriction in different organisms. These data provide quantitative information on the effect of ageing factors from genetic interventions or manipulations of lifespan. Analysis strategies beyond common static database queries are highly desirable for the inspection of complex relationships between AgeFactDB data sets. 3D visualisation can be extremely valuable for advanced data exploration. Results Different types of networks and visualisation strategies are proposed, ranging from basic networks of individual ageing factors for a single species to complex multi-species networks. The augmentation of lifespan observation networks by annotation nodes, like gene ontology terms, is shown to facilitate and speed up data analysis. We developed a new Javascript 3D network viewer JANet that provides the proposed visualisation strategies and has a customised interface for AgeFactDB data. It enables the analysis of gene lists in combination with AgeFactDB data and the interactive visualisation of the results. Conclusion Interactive 3D network visualisation allows to supplement complex database queries by a visually guided exploration process. The JANet interface allows gaining deeper insights into lifespan data patterns not accessible by common database queries alone. These concepts can be utilised in many other research fields. Electronic supplementary material The online version of this article (10.1186/s12859-018-2393-x) contains supplementary material, which is available to authorized users.

Background Despite the ubiquity of mass spectrometry (MS), data processing tools can be surprisingly limited. To date, there is no stand-alone, cross-platform 3-D visualizer for MS data. Available visualization toolkits require large libraries with multiple dependencies and are not well suited for custom MS data processing modules, such as MS storage systems or data processing algorithms. Results We present JS-MS, a 3-D, modular JavaScript client application for viewing MS data. JS-MS provides several advantages over existing MS viewers, such as a dependency-free, browser-based, one click, cross-platform install and better navigation interfaces. The client includes a modular Java backend with a novel streaming.mzML parser to demonstrate the API-based serving of MS data to the viewer. Conclusions JS-MS enables custom MS data processing and evaluation by providing fast, 3-D visualization using improved navigation without dependencies. JS-MS is publicly available with a GPLv2 license at github.com/optimusmoose/jsms.

Background A major challenge of high throughput transcriptome studies is presenting the data to researchers in an interpretable format. In many cases, the outputs of such studies are gene lists which are then examined for enriched biological concepts. One approach to help the researcher interpret large gene datasets is to associate genes and informative terms (iTerm) that are obtained from the biomedical literature using the eGIFT text-mining system. However, examining large lists of iTerm and gene pairs is a daunting task. Results We have developed WebGIVI, an interactive web-based visualization tool (http://raven.anr.udel.edu/webgivi/) to explore gene:iTerm pairs. WebGIVI was built via Cytoscape and Data Driven Document JavaScript libraries and can be used to relate genes to iTerms and then visualize gene and iTerm pairs. WebGIVI can accept a gene list that is used to retrieve the gene symbols and corresponding iTerm list. This list can be submitted to visualize the gene iTerm pairs using two distinct methods: a Concept Map or a Cytoscape Network Map. In addition, WebGIVI also supports uploading and visualization of any two-column tab separated data. Conclusions WebGIVI provides an interactive and integrated network graph of gene and iTerms that allows filtering, sorting, and grouping, which can aid biologists in developing hypothesis based on the input gene lists. In addition, WebGIVI can visualize hundreds of nodes and generate a high-resolution image that is important for most of research publications. The source code can be freely downloaded at https://github.com/sunliang3361/WebGIVI. The WebGIVI tutorial is available at http://raven.anr.udel.edu/webgivi/tutorial.php. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1664-2) contains supplementary material, which is available to authorized users.

BackgroundIt is well-known that genome sequencing technologies are becoming significantly cheaper and faster. As a result of this, the exponential growth in sequencing data in public databases allows us to explore ever growing large collections of genome sequences. However, it is less known that the majority of available sequenced genome sequences in public databases are not complete, drafts of varying qualities. We have calculated quality scores for around 100,000 bacterial genomes from all major genome repositories and put them in a fast and easy-to-use database.ResultsProkaryotic genomic data from all sources were collected and combined to make a non-redundant set of bacterial genomes. The genome quality score for each was calculated by four different measurements: assembly quality, number of rRNA and tRNA genes, and the occurrence of conserved functional domains. The dataBase of Bacterial Quality scores (dBBQs) was designed to store and retrieve quality scores. It offers fast searching and download features which the result can be used for further analysis. In addition, the search results are shown in interactive JavaScript chart framework using DC.js. The analysis of quality scores across major public genome databases find that around 68% of the genomes are of acceptable quality for many uses.ConclusionsdBBQs (available at http://arc-gem.uams.edu/dbbqs) provides genome quality scores for all available prokaryotic genome sequences with a user-friendly Web-interface. These scores can be used as cut-offs to get a high-quality set of genomes for testing bioinformatics tools or improving the analysis. Moreover, all data of the four measurements that were combined to make the quality score for each genome, which can potentially be used for further analysis. dBBQs will be updated regularly and is freely use for non-commercial purpose.

Background Metabolic pathway diagrams are a classical way of visualizing a linked cascade of biochemical reactions. However, to understand some biochemical situations, viewing a single pathway is insufficient, whereas viewing the entire metabolic network results in information overload. How do we enable scientists to rapidly construct personalized multi-pathway diagrams that depict a desired collection of interacting pathways that emphasize particular pathway interactions? Results We define software for constructing personalized multi-pathway diagrams called pathway-collages using a combination of manual and automatic layouts. The user specifies a set of pathways of interest for the collage from a Pathway/Genome Database. Layouts for the individual pathways are generated by the Pathway Tools software, and are sent to a Javascript Pathway Collage application implemented using Cytoscape.js. That application allows the user to re-position pathways; define connections between pathways; change visual style parameters; and paint metabolomics, gene expression, and reaction flux data onto the collage to obtain a desired multi-pathway diagram. We demonstrate the use of pathway collages in two application areas: a metabolomics study of pathogen drug response, and an Escherichia coli metabolic model. Conclusions Pathway collages enable facile construction of personalized multi-pathway diagrams.

Background Technological advances have enabled the analysis of very small amounts of DNA in forensic cases. However, the DNA profiles from such evidence are frequently incomplete and can contain contributions from multiple individuals. The complexity of such samples confounds the assessment of the statistical weight of such evidence. One approach to account for this uncertainty is to use a likelihood ratio framework to compare the probability of the evidence profile under different scenarios. While researchers favor the likelihood ratio framework, few open-source software solutions with a graphical user interface implementing these calculations are available for practicing forensic scientists. Results To address this need, we developed Lab Retriever, an open-source, freely available program that forensic scientists can use to calculate likelihood ratios for complex DNA profiles. Lab Retriever adds a graphical user interface, written primarily in JavaScript, on top of a C++ implementation of the previously published R code of Balding. We redesigned parts of the original Balding algorithm to improve computational speed. In addition to incorporating a probability of allelic drop-out and other critical parameters, Lab Retriever computes likelihood ratios for hypotheses that can include up to four unknown contributors to a mixed sample. These computations are completed nearly instantaneously on a modern PC or Mac computer. Conclusions Lab Retriever provides a practical software solution to forensic scientists who wish to assess the statistical weight of evidence for complex DNA profiles. Executable versions of the program are freely available for Mac OSX and Windows operating systems. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0740-8) contains supplementary material, which is available to authorized users.

Background A critical output of metagenomic studies is the estimation of abundances of taxonomical or functional groups. The inherent uncertainty in assignments to these groups makes it important to consider both their hierarchical contexts and their prediction confidence. The current tools for visualizing metagenomic data, however, omit or distort quantitative hierarchical relationships and lack the facility for displaying secondary variables. Results Here we present Krona, a new visualization tool that allows intuitive exploration of relative abundances and confidences within the complex hierarchies of metagenomic classifications. Krona combines a variant of radial, space-filling displays with parametric coloring and interactive polar-coordinate zooming. The HTML5 and JavaScript implementation enables fully interactive charts that can be explored with any modern Web browser, without the need for installed software or plug-ins. This Web-based architecture also allows each chart to be an independent document, making them easy to share via e-mail or post to a standard Web server. To illustrate Krona's utility, we describe its application to various metagenomic data sets and its compatibility with popular metagenomic analysis tools. Conclusions Krona is both a powerful metagenomic visualization tool and a demonstration of the potential of HTML5 for highly accessible bioinformatic visualizations. Its rich and interactive displays facilitate more informed interpretations of metagenomic analyses, while its implementation as a browser-based application makes it extremely portable and easily adopted into existing analysis packages. Both the Krona rendering code and conversion tools are freely available under a BSD open-source license, and available from: http://krona.sourceforge.net.

Meeting abstract The Biomedical Information Sciences Unit (BISU) of the UT-CTSI developed a secure, centralized information management system that is accessed through web applications. This system, called Slim-Prim [1,2], is mounted on an Oracle server housed within the UT-ITS and currently hosts three multi-site clinical trials and several institutional trials. In addition, Slim-Prim houses databases of clinical and laboratory information and provides a centralized location for several physician-generated surveys. In the majority of these cases users have approached the BISU and asked for assistance with their data management needs. Slim-Prim [1,2] and the BISU provide a cost effective mechanism for funded groups to handle their data management needs. However, we are also aware that many smaller groups are in desperate need of secure yet flexible database solutions; thus there is the need for a portable, secure and open source instance of Slim-Prim. To this end we have developed and released an open source instance of the Slim-Prim system under the Affero GPL v3 (http://www.gnu.org/) called “Protected Research Information Management Environment (PRIME)”. The PRIME instance is written in PHP, based on an objected-oriented programming (OOP) architecture, and JavaScript with inverted file indexing search algorithms to render the graphical user interface (GUI) (http://www.php.net/). PRIME has a user-friendly interface that is menu driven with help features that assist the user in navigating through the application. PRIME exploits the MySQL database system and allows powerful SQL queries across tasks through using fully normalized table structures (http://www. mysql.com/). It is a web-enabled application that runs on most popular browsers. It is provided to the clinical and translational science community as a free and easily customized database solution.

When publishing large-scale microarray datasets, it is of great value to create supplemental websites where either the full data, or selected subsets corresponding to figures within the paper, can be browsed. We set out to create a CGI application containing many of the features of some of the existing standalone software for the visualization of clustered microarray data. We present GeneXplorer, a web application for interactive microarray data visualization and analysis in a web environment. GeneXplorer allows users to browse a microarray dataset in an intuitive fashion. It provides simple access to microarray data over the Internet and uses only HTML and JavaScript to display graphic and annotation information. It provides radar and zoom views of the data, allows display of the nearest neighbors to a gene expression vector based on their Pearson correlations and provides the ability to search gene annotation fields. The software is released under the permissive MIT Open Source license, and the complete documentation and the entire source code are freely available for download from CPAN http://search.cpan.org/dist/Microarray-GeneXplorer/ .

Background Computational biologists daily face the need to explore massive amounts of genomic data. New visualization techniques can help researchers navigate and understand these big data. Horizon Charts are a relatively new visualization method that, under the right circumstances, maximizes data density without losing graphical perception. Results Horizon Charts have been successfully applied to understand multi-metric time series data. We have adapted an existing JavaScript library (Cubism) that implements Horizon Charts for the time series domain so that it works effectively with genomic datasets. We call this new library DNAism. Conclusions Horizon Charts can be an effective visual tool to explore complex and large genomic datasets. Researchers can use our library to leverage these techniques to extract additional insights from their own datasets. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-0891-2) contains supplementary material, which is available to authorized users.

Background: The RCSB Protein Data Bank (PDB) provides public access to experimentally determined 3D-structures of biological macromolecules (proteins, peptides and nucleic acids). While various tools are available to explore the PDB, options to access the global structural diversity of the entire PDB and to perceive relationships between PDB structures remain very limited. Methods: A 136-dimensional atom pair 3D-fingerprint for proteins (3DP) counting categorized atom pairs at increasing through-space distances was designed to represent the molecular shape of PDB-entries. Nearest neighbor searches examples were reported exemplifying the ability of 3DP-similarity to identify closely related biomolecules from small peptides to enzyme and large multiprotein complexes such as virus particles. The principle component analysis was used to obtain the visualization of PDB in 3DP-space. Results: The 3DP property space groups proteins and protein assemblies according to their 3D-shape similarity, yet shows exquisite ability to distinguish between closely related structures. An interactive website called PDB-Explorer is presented featuring a color-coded interactive map of PDB in 3DP-space. Each pixel of the map contains one or more PDB-entries which are directly visualized as ribbon diagrams when the pixel is selected. The PDB-Explorer website allows performing 3DP-nearest neighbor searches of any PDB-entry or of any structure uploaded as protein-type PDB file. All functionalities on the website are implemented in JavaScript in a platform-independent manner and draw data from a server that is updated daily with the latest PDB additions, ensuring complete and up-to-date coverage. The essentially instantaneous 3DP-similarity search with the PDB-Explorer provides results comparable to those of much slower 3D-alignment algorithms, and automatically clusters proteins from the same superfamilies in tight groups. Conclusion: A chemical space classification of PDB based on molecular shape was obtained using a new atom-pair 3D-fingerprint for proteins and implemented in a web-based database exploration tool comprising an interactive color-coded map of the PDB chemical space and a nearest neighbor search tool. The PDB-Explorer website is freely available at www.cheminfo.org/pdbexplorer and represents an unprecedented opportunity to interactively visualize and explore the structural diversity of the PDB.

The Legumes (Fabaceae) are an economically and ecologically important group of plant species with the conspicuous capacity for symbiotic nitrogen fixation in root nodules, specialized plant organs containing symbiotic microbes. With the aim of understanding the underlying molecular mechanisms leading to nodulation, many efforts are underway to identify nodulation-related genes and determine how these genes interact with each other. In order to accurately and efficiently reconstruct nodulation gene network, a crowdsourcing platform, CrowdNodNet, was created. The platform implements the jQuery and vis.js JavaScript libraries, so that users are able to interactively visualize and edit the gene network, and easily access the information about the network, e.g. gene lists, gene interactions and gene functional annotations. In addition, all the gene information is written on MediaWiki pages, enabling users to edit and contribute to the network curation. Utilizing the continuously updated, collaboratively written, and community-reviewed Wikipedia model, the platform could, in a short time, become a comprehensive knowledge base of nodulation-related pathways. The platform could also be used for other biological processes, and thus has great potential for integrating and advancing our understanding of the functional genomics and systems biology of any process for any species. The platform is available at http://crowd.bioops.info/ , and the source code can be openly accessed at https://github.com/bioops/crowdnodnet under MIT License.

Background Ongoing advancements in cloud computing provide novel opportunities in scientific computing, especially for distributed workflows. Modern web browsers can now be used as high-performance workstations for querying, processing, and visualizing genomics’ “Big Data” from sources like The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) without local software installation or configuration. The design of QMachine (QM) was driven by the opportunity to use this pervasive computing model in the context of the Web of Linked Data in Biomedicine. Results QM is an open-sourced, publicly available web service that acts as a messaging system for posting tasks and retrieving results over HTTP. The illustrative application described here distributes the analyses of 20 Streptococcus pneumoniae genomes for shared suffixes. Because all analytical and data retrieval tasks are executed by volunteer machines, few server resources are required. Any modern web browser can submit those tasks and/or volunteer to execute them without installing any extra plugins or programs. A client library provides high-level distribution templates including MapReduce. This stark departure from the current reliance on expensive server hardware running “download and install” software has already gathered substantial community interest, as QM received more than 2.2 million API calls from 87 countries in 12 months. Conclusions QM was found adequate to deliver the sort of scalable bioinformatics solutions that computation- and data-intensive workflows require. Paradoxically, the sandboxed execution of code by web browsers was also found to enable them, as compute nodes, to address critical privacy concerns that characterize biomedical environments.

Background The web has seen an explosion of chemistry and biology related resources in the last 15 years: thousands of scientific journals, databases, wikis, blogs and resources are available with a wide variety of types of information. There is a huge need to aggregate and organise this information. However, the sheer number of resources makes it unrealistic to link them all in a centralised manner. Instead, search engines to find information in those resources flourish, and formal languages like Resource Description Framework and Web Ontology Language are increasingly used to allow linking of resources. A recent development is the use of userscripts to change the appearance of web pages, by on-the-fly modification of the web content. This opens possibilities to aggregate information and computational results from different web resources into the web page of one of those resources. Results Several userscripts are presented that enrich biology and chemistry related web resources by incorporating or linking to other computational or data sources on the web. The scripts make use of Greasemonkey-like plugins for web browsers and are written in JavaScript. Information from third-party resources are extracted using open Application Programming Interfaces, while common Universal Resource Locator schemes are used to make deep links to related information in that external resource. The userscripts presented here use a variety of techniques and resources, and show the potential of such scripts. Conclusion This paper discusses a number of userscripts that aggregate information from two or more web resources. Examples are shown that enrich web pages with information from other resources, and show how information from web pages can be used to link to, search, and process information in other resources. Due to the nature of userscripts, scientists are able to select those scripts they find useful on a daily basis, as the scripts run directly in their own web browser rather than on the web server. This flexibility allows the scientists to tune the features of web resources to optimise their productivity.