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A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

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ggcoverage: an R package to visualize and annotate genome coverage for various NGS data.

Yabing Song¹, Jianbin Wang²

¹School of Life Sciences, Tsinghua University, Beijing, China. songyb18@mails.tsinghua.edu.cn.

BMC Bioinformatics

|August 9, 2023

Summary

ggcoverage is a new R package for visualizing and annotating next-generation sequencing (NGS) data. It offers flexibility and customization for genome coverage plots across multiple groups and omics, improving data interpretation.

Keywords:

Genome annotation Genome coverage Multi-omics Next-generation sequencing Visualization

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Visualizing genome coverage is essential for interpreting next-generation sequencing (NGS) data.
Integrating genome annotations with coverage data presents challenges due to diverse NGS data types and annotation requirements.
Existing visualization tools often lack flexibility, user-friendliness, and comprehensive annotation capabilities.

Purpose of the Study:

To introduce ggcoverage, an R package designed for flexible and efficient visualization and annotation of multi-group and multi-omics genome coverage.
To provide a user-friendly solution that overcomes limitations of existing tools by offering robust preprocessing and extensive annotation options.

Main Methods:

Developed ggcoverage as an R package supporting BAM, BigWig, BedGraph, and TSV input formats.
Integrated state-of-the-art tools for read normalization, consensus peak generation, and track data loading.
Enabled easy superimposition of diverse annotations (e.g., for WGS/WES, RNA-seq, ChIP-seq) and protein coverage visualization.

Main Results:

ggcoverage facilitates the visualization and annotation of genome coverage for multi-groups and multi-omics data.
The package offers efficient preprocessing capabilities, including read normalization and consensus peak generation.
Generated plots are publication-quality and customizable using ggplot2, supporting various NGS data types and protein coverage.

Conclusions:

ggcoverage provides a flexible, programmable, efficient, and user-friendly solution for visualizing and annotating genome coverage.
The package enhances the interpretation of complex genomic and multi-omics datasets.
ggcoverage is available on GitHub with comprehensive vignettes for user guidance.