Abstract
BackgroundGenome assemblies are foundational for understanding the biology of a species. They provide a physical framework for mapping additional sequences, thereby enabling characterization of, for example, genomic diversity and differences in gene expression across individuals and tissue types. Quality metrics for genome assemblies gauge both the completeness and contiguity of an assembly and help provide confidence in downstream biological insights. To compare quality across multiple assemblies, a set of common metrics are typically calculated and then compared to one or more gold standard reference genomes. While several tools exist for calculating individual metrics, applications providing comprehensive evaluations of multiple assembly features are, perhaps surprisingly, lacking. Here, we describe a new toolkit that integrates multiple metrics to characterize both assembly and gene annotation quality in a way that enables comparison across multiple assemblies and assembly types.ResultsOur application, named GenomeQC, is an easy-to-use and interactive web framework that integrates various quantitative measures to characterize genome assemblies and annotations. GenomeQC provides researchers with a comprehensive summary of these statistics and allows for benchmarking against gold standard reference assemblies.ConclusionsThe GenomeQC web application is implemented in R/Shiny version 1.5.9 and Python 3.6 and is freely available at https://genomeqc.maizegdb.org/ under the GPL license. All source code and a containerized version of the GenomeQC pipeline is available in the GitHub repository https://github.com/HuffordLab/GenomeQC.
Highlights
Genome assemblies are foundational for understanding the biology of a species
We have developed a Docker container of the GenomeQC pipeline that calculates these metrics and supports analysis of large (> 2.5Gb) genomes
Input files Two files are required as input for GenomeQC analysis
Summary
Genome assemblies are foundational for understanding the biology of a species They provide a physical framework for mapping additional sequences, thereby enabling characterization of, for example, genomic diversity and differences in gene expression across individuals and tissue types. The growing number of assemblies and gene annotations has necessitated the development of metrics that can be used to compare their quality. Annotation quality metrics include number of gene models, exons per gene model, and the average lengths of genes, exons and transcripts [7]. Such length and count metrics are useful, but they do not fully capture the completeness of assemblies
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