WIlsON: Web-based Interactive Omics VisualizatioN

Schultheis, Hendrik; Kuenne, Carsten; Preussner, Jens; Wiegandt, René; Fust, Annika; Bentsen, Mette; Looso, Mario

doi:10.1093/bioinformatics/bty711

Cited by 23 publications

(15 citation statements)

References 9 publications

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“…In addition to interactive notebooks, the Galaxy Interactive Environments also boasts a selection of other interactive tools such as the aforementioned cellxgene featured in Fig. 3 , as well as SPARQL, a query language interface; BAM/VCF IOBIO, a file format analysis viewer [ 49 ]; EtherCalc, a web spreadsheet [ 50 ]; PHINCH, a metagenome visualizer [ 51 ]; Wallace, a species modelling platform [ 52 ]; WILSON, an omics visualizer [ 53 ]; IDE for materials science; Panoply, a netCDF viewer [ 54 ]; HiGlass, a Hi-C data visualizer [ 55 ]; and even an XFCE Virtual Desktop environment [ 56 ].…”

Section: Methodsmentioning

confidence: 99%

A single-cell RNA-sequencing training and analysis suite using the Galaxy framework

et al. 2020

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Background The vast ecosystem of single-cell RNA-sequencing tools has until recently been plagued by an excess of diverging analysis strategies, inconsistent file formats, and compatibility issues between different software suites. The uptake of 10x Genomics datasets has begun to calm this diversity, and the bioinformatics community leans once more towards the large computing requirements and the statistically driven methods needed to process and understand these ever-growing datasets. Results Here we outline several Galaxy workflows and learning resources for single-cell RNA-sequencing, with the aim of providing a comprehensive analysis environment paired with a thorough user learning experience that bridges the knowledge gap between the computational methods and the underlying cell biology. The Galaxy reproducible bioinformatics framework provides tools, workflows, and trainings that not only enable users to perform 1-click 10x preprocessing but also empower them to demultiplex raw sequencing from custom tagged and full-length sequencing protocols. The downstream analysis supports a range of high-quality interoperable suites separated into common stages of analysis: inspection, filtering, normalization, confounder removal, and clustering. The teaching resources cover concepts from computer science to cell biology. Access to all resources is provided at the singlecell.usegalaxy.eu portal. Conclusions The reproducible and training-oriented Galaxy framework provides a sustainable high-performance computing environment for users to run flexible analyses on both 10x and alternative platforms. The tutorials from the Galaxy Training Network along with the frequent training workshops hosted by the Galaxy community provide a means for users to learn, publish, and teach single-cell RNA-sequencing analysis.

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Section: Methodsmentioning

confidence: 99%

A single-cell RNA-sequencing training and analysis suite using the Galaxy framework

et al. 2020

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“…A growing number of software packages (Younesy et al, 2015;Nelson et al, 2016;Su et al, 2017;Harshbarger et al, 2017;Gardeux et al, 2017;Lim et al, 2017;Li and Andrade, 2017;Zhu et al, 2018;Ge et al, 2018;Monier et al, 2018;McDermaid et al, 2018;Schultheis et al, 2018;Kucukural et al, 2019;Choi and Ratner, 2019;Price et al, 2019;Tintori et al, 2020;Su et al, 2019) have been developed to operate on tabular-like summarized expression data, or on formats which might derive from their results (see Additional file 1: Table S1 for a comprehensive list of details on their functionality and characteristics).…”

Section: Introductionmentioning

confidence: 99%

ideal: an R/Bioconductor package for Interactive Differential Expression Analysis

Marini

Linke

Binder

2020

Preprint

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Background: RNA sequencing (RNA-seq) is an ever increasingly popular tool for transcriptome profiling. A key point to make the best use of the available data is to provide software tools that are easy to use but still provide flexibility and transparency in the adopted methods. Despite the availability of many packages focused on detecting differential expression, a method to streamline this type of bioinformatics analysis in a comprehensive, accessible, and reproducible way is lacking.Results: We developed the ideal software package, which serves as a web application for interactive and reproducible RNA-seq analysis, while producing a wealth of visualizations to facilitate data interpretation. ideal is implemented in R using the Shiny framework, and is fully integrated with the existing core structures of the Bioconductor project. Users can perform the essential steps of the differential expression analysis workflow in an assisted way, and generate a broad spectrum of publication-ready outputs, including diagnostic and summary visualizations in each module, all the way down to functional analysis. ideal also offers the possibility to seamlessly generate a full HTML report for storing and sharing results together with code for reproducibility.Conclusion: ideal is distributed as an R package in the Bioconductor project (http: //bioconductor.org/packages/ideal/), and provides a solution for performing interactive and reproducible analyses of summarized RNA-seq expression data, empowering researchers with many different profiles (life scientists, clinicians, but also experienced bioinformaticians) to make the ideal use of the data at hand.

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“…Bioconductor software is written in the R programming language, which also provides statistical and visualization methods that can facilitate the development of robust graphical tools [7]. Several interactive visualization methods for genomic data have been developed using Shiny, which is also based on the R programming language [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

bigPint: A Bioconductor visualization package that makes big data pint-sized

Rutter¹,

Cook²

2020

PLoS Comput Biol

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Interactive data visualization is imperative in the biological sciences. The development of independent layers of interactivity has been in pursuit in the visualization community. We developed bigPint, a data visualization package available on Bioconductor under the GPL-3 license (https://bioconductor.org/packages/release/bioc/html/bigPint.html). Our software introduces new visualization technology that enables independent layers of interactivity using Plotly in R, which aids in the exploration of large biological datasets. The bigPint package presents modernized versions of scatterplot matrices, volcano plots, and litre plots through the implementation of layered interactivity. These graphics have detected normalization issues, differential expression designation problems, and common analysis errors in public RNA-sequencing datasets. Researchers can apply bigPint graphics to their data by following recommended pipelines written in reproducible code in the user manual. In this paper, we explain how we achieved the independent layers of interactivity that are behind bigPint graphics. Pseudocode and source code are provided. Computational scientists can leverage our open-source code to expand upon our layered interactive technology and/or apply it in new ways toward other computational biology tasks.

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WIlsON: Web-based Interactive Omics VisualizatioN

Cited by 23 publications

References 9 publications

A single-cell RNA-sequencing training and analysis suite using the Galaxy framework

A single-cell RNA-sequencing training and analysis suite using the Galaxy framework

ideal: an R/Bioconductor package for Interactive Differential Expression Analysis

bigPint: A Bioconductor visualization package that makes big data pint-sized

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