viromeBrowser: A Shiny App for Browsing Virome Sequencing Analysis Results

Abstract

Experiments in which complex virome sequencing data is generated remain difficult to explore and unpack for scientists without a background in data science. The processing of raw sequencing data by high throughput sequencing workflows usually results in contigs in FASTA format coupled to an annotation file linking the contigs to a reference sequence or taxonomic identifier. The next step is to compare the virome of different samples based on the metadata of the experimental setup and extract sequences of interest that can be used in subsequent analyses. The viromeBrowser is an application written in the opensource R shiny framework that was developed in collaboration with end-users and is focused on three common data analysis steps. First, the application allows interactive filtering of annotations by default or custom quality thresholds. Next, multiple samples can be visualized to facilitate comparison of contig annotations based on sample specific metadata values. Last, the application makes it easy for users to extract sequences of interest in FASTA format. With the interactive features in the viromeBrowser we aim to enable scientists without a data science background to compare and extract annotation data and sequences from virome sequencing analysis results.

Keywords: NGS analysis; bioinformatics; data analysis; metagenomics; shiny; virome; visualization.

Figure 1

Example screenshot of the file import screen. Metadata, annotation files, and contig files are uploaded and processed in the file import screen. An excerpt of the metadata, the annotation files, and the contig files can be visualized under the corresponding tabs.

Figure 2

Screenshot of the interactive data browsing heatmap. Stratification variable and filter criteria can be selected in the browser settings window. Annotation quality filter settings are available as a drop-down menu in the bottom of the page. Hovering over the heatmap shows the contig annotations and the number of contigs or read counts. Selecting tiles of the heatmap results in selection of only those contigs for further analysis and downloading.

Figure 3

Example screenshot of the sequence information tab. Contigs can be selected from a table of previously selected annotations. Further inspection can be by visualization of the open reading frame (ORF) structure and downloading of individual ORFs or the complete contig.