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.
Highlights
The use of metagenomic sequencing to explore the virome of complex samples such as stool, sewage, and environmental samples has increased over the years [1]
After processing of the raw sequencing data by next-generation sequencing (NGS) processing workflows, the usual output consists of contiguous sequences in FASTA format and an annotation file linking the contigs to a reference sequence or taxonomic identifier in tabular format [3,4,5,6]
The user interface (UI) elements of the viromeBrowser have been made modular to allow for easier expansion of the app, using the Shiny functionalities for module development
Summary
The use of metagenomic sequencing to explore the virome of complex samples such as stool, sewage, and environmental samples has increased over the years [1]. The results of experiments in which complex virome sequencing data are generated are difficult to visualize and unpack for a person without programming experience. Virusfinder outputs several text files containing annotations and contig sequences as results [3], and VirFind outputs tabular annotation and FASTA files for viral and non-viral annotations [5]. Further unpacking of results and getting an overview of which viruses are found in which samples is especially difficult when these data are spread over multiple tables containing many annotations. Many of the workflows generate a summary file and figures containing a selection of the annotated sequences from the sample based on predefined selection criteria. Manual inspection and manual filtering using custom quality criteria are often needed, which means that the workflow has to be rerun with different settings or, if possible, the unfiltered annotation results can be filtered using custom scripts and further analyzed using visualization tools
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