Dataset Information

WigExplorer, a BioJS component to visualise wig data.

ABSTRACT: wigExplorer is a BioJS component whose main purpose is to provide a platform for visualisation of wig-formatted data. Wig files are extensively used by genome browsers such as the UCSC Genome Browser. wigExplorer follows the BioJS standard specification, requiring a simple configuration and installation. wigExplorer provides an easy way to navigate the visible region of the canvas and allows interaction with other components via predefined events. Availability: http://biojs.io/d/biojs-vis-wigexplorer ; http://dx.doi.org/10.5281/zenodo.8516.

SUBMITTER: Thanki AS

PROVIDER: S-EPMC5054804 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Publications

wigExplorer, a BioJS component to visualise wig data.

Thanki Anil S AS Jimenez Rafael C RC Kaithakottil Gemy G GG Corpas Manuel M Davey Robert P RP

F1000Research 20140213

wigExplorer is a BioJS component whose main purpose is to provide a platform for visualisation of wig-formatted data. Wig files are extensively used by genome browsers such as the UCSC Genome Browser. wigExplorer follows the BioJS standard specification, requiring a simple configuration and installation. wigExplorer provides an easy way to navigate the visible region of the canvas and allows interaction with other components via predefined events. Availability: http:/ ...[more]

PMID: 27781080

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Project description:BACKGROUND:Biological pathways are descriptive diagrams of biological processes widely used for functional analysis of differentially expressed genes or proteins. Primary data analysis, such as quality control, normalisation, and statistical analysis, is often performed in scripting languages like R, Perl, and Python. Subsequent pathway analysis is usually performed using dedicated external applications. Workflows involving manual use of multiple environments are time consuming and error prone. Therefore, tools are needed that enable pathway analysis directly within the same scripting languages used for primary data analyses. Existing tools have limited capability in terms of available pathway content, pathway editing and visualisation options, and export file formats. Consequently, making the full-fledged pathway analysis tool PathVisio available from various scripting languages will benefit researchers. RESULTS:We developed PathVisioRPC, an XMLRPC interface for the pathway analysis software PathVisio. PathVisioRPC enables creating and editing biological pathways, visualising data on pathways, performing pathway statistics, and exporting results in several image formats in multiple programming environments. We demonstrate PathVisioRPC functionalities using examples in Python. Subsequently, we analyse a publicly available NCBI GEO gene expression dataset studying tumour bearing mice treated with cyclophosphamide in R. The R scripts demonstrate how calls to existing R packages for data processing and calls to PathVisioRPC can directly work together. To further support R users, we have created RPathVisio simplifying the use of PathVisioRPC in this environment. We have also created a pathway module for the microarray data analysis portal ArrayAnalysis.org that calls the PathVisioRPC interface to perform pathway analysis. This module allows users to use PathVisio functionality online without having to download and install the software and exemplifies how the PathVisioRPC interface can be used by data analysis pipelines for functional analysis of processed genomics data. CONCLUSIONS:PathVisioRPC enables data visualisation and pathway analysis directly from within various analytical environments used for preliminary analyses. It supports the use of existing pathways from WikiPathways or pathways created using the RPC itself. It also enables automation of tasks performed using PathVisio, making it useful to PathVisio users performing repeated visualisation and analysis tasks. PathVisioRPC is freely available for academic and commercial use at http://projects.bigcat.unimaas.nl/pathvisiorpc.

Project description:Background:Metabolomics is gaining popularity as a standard tool for the investigation of biological systems. Yet, parsing metabolomics data in the absence of in-house computational scientists can be overwhelming and time-consuming. As a consequence of manual data processing, the results are often not analysed in full depth, so potential novel findings might get lost. Methods:To tackle this problem, we developed Metabolite AutoPlotter, a tool to process and visualise quantified metabolite data. Other than with bulk data visualisations, such as heat maps, the aim of the tool is to generate single plots for each metabolite. For this purpose, it reads as input pre-processed metabolite-intensity tables and accepts different experimental designs, with respect to the number of metabolites, conditions and replicates. The code was written in the R-scripting language and wrapped into a shiny application that can be run online in a web browser on https://mpietzke.shinyapps.io/autoplotter. Results:We demonstrate the main features and the ease of use with two different metabolite datasets, for quantitative experiments and for stable isotope tracing experiments. We show how the plots generated by the tool can be interactively modified with respect to plot type, colours, text labels and the shown statistics. We also demonstrate the application towards 13C-tracing experiments and the seamless integration of natural abundance correction, which facilitates the better interpretation of stable isotope tracing experiments. The output of the tool is a zip-file containing one single plot for each metabolite as well as restructured tables that can be used for further analysis. Conclusion:With the help of Metabolite AutoPlotter, it is now possible to simplify data processing and visualisation for a wide audience. High-quality plots from complex data can be generated in a short time by pressing a few buttons. This offers dramatic improvements over manual analysis. It is significantly faster and allows researchers to spend more time interpreting the results or to perform follow-up experiments. Further, this eliminates potential copy-and-paste errors or tedious repetitions when things need to be changed. We are sure that this tool will help to improve and speed up scientific discoveries.

Dataset Information

WigExplorer, a BioJS component to visualise wig data.

Publications

<i>wigExplorer</i>, a BioJS component to visualise wig data.

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