Journal:Intervene: A tool for intersection and visualization of multiple gene or genomic region sets

Full article title	Intervene: A tool for intersection and visualization of multiple gene or genomic region sets
Journal	BMC Bioinformatics
Author(s)	Khan, Aziz; Mathelier, Anthony
Author affiliation(s)	Centre for Molecular Medicine Norway, Norwegian Radium Hospital
Primary contact	Email: aziz dot khan at ncmm dot uio dot no and anthony dot mathelier at ncmm dot uio dot no
Year published	2017
Volume and issue	18
Page(s)	287
DOI	10.1186/s12859-017-1708-7
ISSN	1471-2105
Distribution license	Creative Commons Attribution 4.0 International
Website	https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-017-1708-7
Download	https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-017-1708-7 (PDF)

Abstract

Background: A common task for scientists relies on comparing lists of genes or genomic regions derived from high-throughput sequencing experiments. While several tools exist to intersect and visualize sets of genes, similar tools dedicated to the visualization of genomic region sets are currently limited.

Results: To address this gap, we have developed the Intervene tool, which provides an easy and automated interface for the effective intersection and visualization of genomic region or list sets, thus facilitating their analysis and interpretation. Intervene contains three modules: venn to generate Venn diagrams of up to six sets, upset to generate UpSet plots of multiple sets, and pairwise to compute and visualize intersections of multiple sets as clustered heat maps. Intervene, and its interactive web ShinyApp companion, generate publication-quality figures for the interpretation of genomic region and list sets.

Conclusions: Intervene and its web application companion provide an easy command line and an interactive web interface to compute intersections of multiple genomic and list sets. They have the capacity to plot intersections using easy-to-interpret visual approaches. Intervene is developed and designed to meet the needs of both computer scientists and biologists. The source code is freely available at https://bitbucket.org/CBGR/intervene, with the web application available at https://asntech.shinyapps.io/intervene.

Keywords: visualization, Venn diagrams, UpSet plots, heat maps, genome analysis

Background

Effective visualization of transcriptomic, genomic, and epigenomic data generated by next-generation sequencing-based high-throughput assays have become an area of great interest. Most of the data sets generated by such assays are lists of genes or variants, and genomic region sets. The genomic region sets represent genomic locations for specific features, such as transcription factor – DNA interactions, transcription start sites, histone modifications, and DNase hypersensitivity sites. A common task in the interpretation of these features is to find similarities, differences, and enrichments between such sets, which come from different samples, experimental conditions, or cell and tissue types.

Classically, the intersection or overlap between different sets, such as gene lists, is represented by Venn diagrams or Edwards-Venn. If the number of sets exceeds four, such diagrams become complex and difficult to interpret. The key challenge is that there are 2ⁿ combinations to visually represent when considering n sets. An alternative approach, the UpSet plots, was introduced to depict the intersection of more than three sets. The advantage of UpSet plots is their capacity to rank the intersections and alternatively hide combinations without intersection, which is not possible using a Venn diagram. However, with a large number of sets, UpSet plots become an ineffective way of illustrating set intersections. To visualize a large number of sets, one can represent pairwise intersections using a clustered heat map as suggested by Lex and Gehlenborg.

There are several web applications and [[R (programming language)|R packages] available to compute intersection and visualization of up to six list sets by using Venn diagrams. Although tools exist to perform genomic region set intersections, there is a limited number of tools available to visualize them. To our knowledge no tool exists to generate UpSet plots for genomic region sets. Consequently, there is a great need for integrative tools to compute and visualize intersection of multiple sets of both genomic regions and gene/list sets.

To address this need, we developed Intervene, an easy-to-use command line tool to compute and visualize intersections of genomic regions with Venn diagrams, UpSet plots, or clustered heat maps. Moreover, we provide an interactive web application companion to upload list sets or the output of Intervene to further customize plots.

Implementation

Intervene comes as a command line tool, along with an interactive Shiny web application to customize the visual representation of intersections. The command line tool is implemented in Python (version 2.7) and R programming language (version 3.3.2). The build also works with Python versions 3.4, 3.5, and 3.6. The accompanying web interface is developed using Shiny (version 1.0.0), a web application framework for R. Intervene uses pybedtools to perform genomic region set intersections and Seaborn (https://seaborn.pydata.org/), Matplotlib, UpSetR, and Corrplot to generate figures. The web application uses the R package Venerable for different types of Venn diagrams, UpSetR for UpSet plots, and heatmap.2 and Corrplot for pairwise intersection clustered heat maps. The UpSet module of the web ShinyApp was derived from the UpSetR ShinyApp, which was extended by adding more options and features to customize the UpSet plots.

Intervene can be installed by using pip install intervene or using the source code available on bitbucket https://bitbucket.org/CBGR/intervene. The tool has been tested on Linux and MAC systems. The Shiny web application is hosted with shinyapps.io by RStudio, and is compatible with all modern web browsers. A detailed documentation including installation instructions and how to use the tool is provided in Additional file 1 and is available at http://intervene.readthedocs.io.

Results

An integrated tool for effective visualization of multiple set intersections

As visualization of sets and their intersections is becoming more and more challenging due to the increasing number of generated data sets, there is a strong need to have an integrated tool to compute and visualize intersections effectively. To address this challenge, we have developed Intervene, which is composed of three different modules, accessible through the subcommands venn, upset, and pairwise. Intervene accepts two types of input files: genomic regions in BED, GFF, or VCF format and gene/name lists in plain text format. A detailed sketch of Intervene’s command line interface and web application utility with types of inputs is provided in Fig. 1.

Figure 1. A sketch of Intervene’s command line interface and web application, and input data type

Intervene provides flexibility to the user to choose figure colors, label text, size, resolution, and type to make them publication-standard quality. To read the help about any module, the user can type intervene < subcommand > −-help on the command line. Furthermore, Intervene produces results as text files, which can be easily imported to the web application for interactive visualization and customization of plots (see “An interactive web application” section).

Venn diagrams module

Venn diagrams are the classical approach to show intersections between sets. There are several web-based applications and R packages available to visualize intersections of up-to six list sets in classical Venn, Euler, or Edward’s diagrams. However, a very limited number of tools are available to visualize genomic region intersections using classical Venn diagrams.

Intervene provides up-to six-way classical Venn diagrams for gene lists or genomic region sets. The associated web interface can also be used to compute the intersection of multiple gene sets and visualize it using different flavors of weighted and unweighted Venn and Euler diagrams. These different types include: classical Venn diagrams (up-to five sets), Chow-Ruskey (up-to five sets), Edwards’ diagrams (up-to five sets), and Battle (up-to nine sets).

As an example, one might be interested to calculate the number of overlapping ChIP-seq (chromatin immunoprecipitation followed by sequencing) peaks between different types of histone modification marks (H3K27ac, H3K4me3, and H3K27me3) in human embryonic stem cells (hESC) (Fig. 2a , can be generated with the command intervene venn --test).

Figure 2. Example of Intervene’s command line interface outputs. a A three-way Venn diagram of ChIP-seq peaks of histone modifications (H3K27ac, H3Kme3, and H3K27me3) in hESC obtained from ENCODE b UpSet plot of the intersection of four histone modification peaks in hESC c A heat map of pairwise intersections in terms of Jaccard statistics of super-enhancers in 24 mouse cell and tissue types downloaded from dbSUPER

UpSet plots module

When the number of sets exceeds four, Venn diagrams become difficult to read and interpret. An alternative and more effective approach is to use UpSet plots to visualize the intersections. An R package with a ShinyApp (https://gehlenborglab.shinyapps.io/upsetr/) and an interactive web-based tool are available at http://vcg.github.io/upset to visualize multiple list sets. However, to our knowledge, there is no tool available to draw the UpSet plots for genomic region set intersections. Intervene’s upset subcommand can be used to visualize the intersection of multiple genomic region sets using UpSet plots.

As an example, we show the intersections of ChIP-seq peaks for histone modifications (H3K27ac, H3K4me3, H3K27me3, and H3K4me2) in hESC using an UpSet plot, where interactions were ranked by frequency (Fig. 2b, can be generated with the command intervene upset --test). This plot is easier to understand than the four-way Venn diagram (Additional file 1).

Pairwise intersection heat maps module

With an increasing number of data sets, visualizing all possible intersections becomes unfeasible by using Venn diagrams or UpSet plots. One possibility is to compute pairwise intersections and plot-associated metrics as a clustered heat map. Intervene’s pairwise module provides several metrics to assess intersections, including number of overlaps, fraction of overlap, Jaccard statistics, Fisher’s exact test, and distribution of relative distances. Moreover, the user can choose from different styles of heat maps and clustering approaches.

As an example, we obtained the genomic regions of super enhancers in 24 mouse cell type and tissues from dbSUPER and computed the pairwise intersections in terms of Jaccard statistics (Fig. 2c). The triangular heat map shows the pairwise Jaccard index, which is between 0 and 1, where 0 means no overlap and 1 means full overlap. The bar plot shows the number of regions in each cell-type or tissue. This plot can be generated using the command intervene pairwise --test).

An interactive web application

Intervene comes with a web application companion to further explore and filter the results in an interactive way. Indeed, intersections between large data sets can be computed locally using Intervene’s command line interface, then the output files can be uploaded to the ShinyApp for further exploration and customization of the figures (Fig. 1 ).

The ShinyApp web interface takes four types of inputs: (i) a text/csv file where each column represents a set, (ii) a binary representation of intersections, (iii) a pairwise matrix of intersections, and (iv) a matrix of overlap counts. The web application provides several easy and intuitive customization options for responsive adjustments of the figures (Figs. 1 and 3). Users can change colors, fonts and plot sizes, change labels, and select and deselect specific sets. These customized and publication-ready figures can be downloaded in PDF, SVG, TIFF, and PNG formats. The pairwise modules also provides three types of correlation coefficients and hierarchical clustering with eight clustering methods and four distance measurement methods. It further provides interactive features to explore data values; this is done by hovering the mouse cursor over each heat map cell, or by using a searchable and sortable data table. The data table can be downloaded as a CSV file and interactive heat maps can be downloaded as HTML. The Shiny-based web application is freely available at https://asntech.shinyapps.io/intervene.

Figure 3. Screenshots of web application user interface

Notes

This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added.