EvalDNA: a machine learning-based tool for the comprehensive evaluation of mammalian genome assembly quality

Abstract Background 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 qual...

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Autores principales:	Madolyn L. MacDonald, Kelvin H. Lee
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Genome assembly quality Genome assembly Machine learning Chinese hamster CHO cells Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/9149ddb6466743b6a1005ecd98f92813
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spelling	oai:doaj.org-article:9149ddb6466743b6a1005ecd98f928132021-11-28T12:11:15ZEvalDNA: a machine learning-based tool for the comprehensive evaluation of mammalian genome assembly quality10.1186/s12859-021-04480-21471-2105https://doaj.org/article/9149ddb6466743b6a1005ecd98f928132021-11-01T00:00:00Zhttps://doi.org/10.1186/s12859-021-04480-2https://doaj.org/toc/1471-2105Abstract Background 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. Results EvalDNA 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. Conclusions EvalDNA 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.Madolyn L. MacDonaldKelvin H. LeeBMCarticleGenome assembly qualityGenome assemblyMachine learningChinese hamsterCHO cellsComputer applications to medicine. Medical informaticsR858-859.7Biology (General)QH301-705.5ENBMC Bioinformatics, Vol 22, Iss 1, Pp 1-26 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Genome assembly quality Genome assembly Machine learning Chinese hamster CHO cells Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
spellingShingle	Genome assembly quality Genome assembly Machine learning Chinese hamster CHO cells Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5 Madolyn L. MacDonald Kelvin H. Lee EvalDNA: a machine learning-based tool for the comprehensive evaluation of mammalian genome assembly quality
description	Abstract Background 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. Results EvalDNA 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. Conclusions EvalDNA 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.
format	article
author	Madolyn L. MacDonald Kelvin H. Lee
author_facet	Madolyn L. MacDonald Kelvin H. Lee
author_sort	Madolyn L. MacDonald
title	EvalDNA: a machine learning-based tool for the comprehensive evaluation of mammalian genome assembly quality
title_short	EvalDNA: a machine learning-based tool for the comprehensive evaluation of mammalian genome assembly quality
title_full	EvalDNA: a machine learning-based tool for the comprehensive evaluation of mammalian genome assembly quality
title_fullStr	EvalDNA: a machine learning-based tool for the comprehensive evaluation of mammalian genome assembly quality
title_full_unstemmed	EvalDNA: a machine learning-based tool for the comprehensive evaluation of mammalian genome assembly quality
title_sort	evaldna: a machine learning-based tool for the comprehensive evaluation of mammalian genome assembly quality
publisher	BMC
publishDate	2021
url	https://doaj.org/article/9149ddb6466743b6a1005ecd98f92813
work_keys_str_mv	AT madolynlmacdonald evaldnaamachinelearningbasedtoolforthecomprehensiveevaluationofmammaliangenomeassemblyquality AT kelvinhlee evaldnaamachinelearningbasedtoolforthecomprehensiveevaluationofmammaliangenomeassemblyquality
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EvalDNA: a machine learning-based tool for the comprehensive evaluation of mammalian genome assembly quality

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