Abstract

Many fit-for-purpose bioinformatics tools generate plots to interpret complex biological data and illustrate findings. However, assembling individual plots in different formats from various sources into one high-resolution figure in the desired layout requires mastery of commercial tools or even programming skills. In addition, it is a time-consuming and sometimes frustrating process even for a computationally savvy scientist who frequently takes a trial-and-error iterative approach to get satisfactory results. To address the challenge, we developed bioInfograph, a web-based tool that allows users to interactively arrange high-resolution images in diversified formats, mainly Scalable Vector Graphics (SVG), to produce one multi-panel publication-quality composite figure in both PDF and HTML formats in a user-friendly manner, requiring no programming skills. It solves stylesheet conflicts of coexisting SVG plots, integrates a rich-text editor, and allows creative design by providing advanced functionalities like image transparency, controlled vertical stacking of plots, versatile image formats, and layout templates. To highlight, the sharable interactive HTML output with zoom-in function is a unique feature not seen in any other similar tools. In the end, we make the online tool publicly available at https://baohongz.github.io/bioInfograph while releasing the source code at https://github.com/baohongz/bioInfograph under MIT open-source license.

Highlights

  • Popular computational biology databases such as Reactome (Jassal et al, 2020), WikiPathways (Martens et al, 2021), and visualization tools such as Coral (Metz et al, 2018) and ComplexHeatmap (Gu et al, 2016) often produce biological images in Scalable Vector Graphics (SVG) format

  • SVG is an Extensible Markup Language (XML)-based vector image format, scalable to any resolution without blurry pixelization that happens in other popular image formats such as png, gif, and jpg

  • We developed bioInfograph, an interactive web-based tool with a focus on computational biology, which arranges high-resolution images in various formats, mainly SVG, to produce one multipanel publication-quality composite figure in both PDF and interactive HTML formats in a user-friendly manner, requiring no programming skills

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Summary

INTRODUCTION

Popular computational biology databases such as Reactome (Jassal et al, 2020), WikiPathways (Martens et al, 2021), and visualization tools such as Coral (Metz et al, 2018) and ComplexHeatmap (Gu et al, 2016) often produce biological images in Scalable Vector Graphics (SVG) format. SVG is an Extensible Markup Language (XML)-based vector image format, scalable to any resolution without blurry pixelization that happens in other popular image formats such as png, gif, and jpg. This format has become one of the most broadly used image outputs adopted by many data analysis tools used by computational biologists, notably R (Venables et al, 2002), ggplot (Wickham, 2016), and numerous R and Bioconductor (Gentleman et al, 2004) packages.

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