Abstract
BackgroundTo select the most complete, continuous, and accurate assembly for an organism of interest, comprehensive quality assessment of assemblies is necessary. We present a novel tool, called Evaluation of De Novo Assemblies (EvalDNA), which uses supervised machine learning for the quality scoring of genome assemblies and does not require an existing reference genome for accuracy assessment.ResultsEvalDNA calculates a list of quality metrics from an assembled sequence and applies a model created from supervised machine learning methods to integrate various metrics into a comprehensive quality score. A well-tested, accurate model for scoring mammalian genome sequences is provided as part of EvalDNA. This random forest regression model evaluates an assembled sequence based on continuity, completeness, and accuracy, and was able to explain 86% of the variation in reference-based quality scores within the testing data. EvalDNA was applied to human chromosome 14 assemblies from the GAGE study to rank genome assemblers and to compare EvalDNA to two other quality evaluation tools. In addition, EvalDNA was used to evaluate several genome assemblies of the Chinese hamster genome to help establish a better reference genome for the biopharmaceutical manufacturing community. EvalDNA was also used to assess more recent human assemblies from the QUAST-LG study completed in 2018, and its ability to score bacterial genomes was examined through application on bacterial assemblies from the GAGE-B study.ConclusionsEvalDNA enables scientists to easily identify the best available genome assembly for their organism of interest without requiring a reference assembly. EvalDNA sets itself apart from other quality assessment tools by producing a quality score that enables direct comparison among assemblies from different species.
Highlights
To select the most complete, continuous, and accurate assembly for an organism of interest, comprehensive quality assessment of assemblies is necessary
Metrics that were not correlated with quality scores in the training data (-0.1 < r < 0.1) were removed from the model
These included metrics based on the contig number and REAPR’s values for ‘fragment coverage distribution (FCD) error within contigs’ and ‘collapsed repeats’
Summary
To select the most complete, continuous, and accurate assembly for an organism of interest, comprehensive quality assessment of assemblies is necessary. We present a novel tool, called Evaluation of De Novo Assemblies (EvalDNA), which uses supervised machine learning for the quality scoring of genome assemblies and does not require an existing reference genome for accuracy assessment. Whole genome assemblies are becoming available for an increasing number of organisms due to the reduced time and monetary costs of DNA sequencing. A single genome assembly is typically selected as a reference genome to guide wet-lab and bioinformatics studies. Researchers should be aware of any limitations posed by the level of completeness, continuity, and accuracy of their selected reference assembly
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