Background: Visualizing genome coverage is of vital importance to inspect and interpret various next-generation sequencing (NGS) data. Besides genome coverage, genome annotations are also crucial in the visualization. While different NGS data require different annotations, how to visualize genome coverage and add the annotations appropriately and conveniently is challenging. Many tools have been developed to address this issue. However, existing tools are often inflexible, complicated, lack necessary preprocessing steps and annotations, and the figures generated support limited customization.
Results: Here, we introduce ggcoverage, an R package to visualize and annotate genome coverage of multi-groups and multi-omics. The input files for ggcoverage can be in BAM, BigWig, BedGraph and TSV formats. For better usability, ggcoverage provides reliable and efficient ways to perform read normalization, consensus peaks generation and track data loading with state-of-the-art tools. ggcoverage provides various available annotations to adapt to different NGS data (e.g. WGS/WES, RNA-seq, ChIP-seq) and all the available annotations can be easily superimposed with ' + '. ggcoverage can generate publication-quality plots and users can customize the plots with ggplot2. In addition, ggcoverage supports the visualization and annotation of protein coverage.
Conclusions: ggcoverage provides a flexible, programmable, efficient and user-friendly way to visualize and annotate genome coverage of multi-groups and multi-omics. The ggcoverage package is available at https://github.com/showteeth/ggcoverage under the MIT license, and the vignettes are available at https://showteeth.github.io/ggcoverage/ .
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http://dx.doi.org/10.1186/s12859-023-05438-2 | DOI Listing |
Poult Sci
January 2025
Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou, China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou, China. Electronic address:
Low-coverage whole genome sequencing (lcWGS) is an effective low-cost genotyping technology when combined with genotype imputation approaches. It facilitates cost-effective genomic selection (GS) programs in agricultural animal populations. GS based on lcWGS data has been successfully applied to livestock such as pigs and donkeys.
View Article and Find Full Text PDFJACC Adv
December 2024
Alliance for Medical Research in Africa, Dakar, Senegal.
This proposed scientific statement is focused on providing new insights regarding challenges and opportunities for cardiovascular health (CVH) promotion in Africa. The statement includes an overview of the current state of CVH in Africa, with a particular interest in the cardiometabolic risk factors and their evaluation through metrics. The statement also explains the main principles of primordial prevention, its relevance in reducing noncommunicable disease and the different strategies that have been effective worldwide.
View Article and Find Full Text PDFBMC Res Notes
January 2025
Leibniz Institute for the Analysis of Biodiversity Change, Museum Koenig Bonn, Centre for Molecular Biodiversity Research, Bonn, Germany.
Objective: Fin clipping is the standard DNA sampling technique for whole genome sequencing (WGS) of small fish. The collection of fin clips requires anaesthesia or even euthanisation of the individual. Swabbing may be a less invasive, non-lethal alternative to fin-clipping.
View Article and Find Full Text PDFElife
January 2025
Institute for Infectious Diseases and Infection Control, Jena University Hospital, Jena, Germany.
Given the rapid cross-country spread of SARS-CoV-2 and the resulting difficulty in tracking lineage spread, we investigated the potential of combining mobile service data and fine-granular metadata (such as postal codes and genomic data) to advance integrated genomic surveillance of the pandemic in the federal state of Thuringia, Germany. We sequenced over 6500 SARS-CoV-2 Alpha genomes (B.1.
View Article and Find Full Text PDFAm J Hum Genet
January 2025
Department of Human Genetics, Radboud University Medical Center, Nijmegen, the Netherlands; Radboudumc Research Institute for Medical Innovation, Radboud University Medical Center, Nijmegen, the Netherlands. Electronic address:
Clinical short-read exome and genome sequencing approaches have positively impacted diagnostic testing for rare diseases. Yet, technical limitations associated with short reads challenge their use for the detection of disease-associated variation in complex regions of the genome. Long-read sequencing (LRS) technologies may overcome these challenges, potentially qualifying as a first-tier test for all rare diseases.
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