A machine learning-based service for estimating quality of genomes using PATRIC

Parrello, Bruce; Butler, Rory; Chlenski, Philippe; Olson, Robert; Overbeek, Jamie C.; Pusch, Gordon D.; Vonstein, Veronika; Overbeek, Ross

doi:10.1186/s12859-019-3068-y

Title: A machine learning-based service for estimating quality of genomes using PATRIC

Abstract

Recent advances in high-volume sequencing technology and mining of genomes from metagenomic samples call for rapid and reliable genome quality evaluation. The current release of the PATRIC database contains over 220,000 genomes, and current metagenomic technology supports assemblies of many draft-quality genomes from a single sample, most of which will be novel. We have added two quality assessment tools to the PATRIC annotation pipeline. EvalCon uses supervised machine learning to calculate an annotation consistency score. EvalG implements a variant of the CheckM algorithm to estimate contamination and completeness of an annotated genome.We report on the performance of these tools and the potential utility of the consistency score. Additionally, we provide contamination, completeness, and consistency measures for all genomes in PATRIC and in a recent set of metagenomic assemblies. EvalG and EvalCon facilitate the rapid quality control and exploration of PATRIC-annotated draft genomes.

Authors:

Parrello, Bruce ^[1]; Butler, Rory ^[2];

^[3]; Olson, Robert ^[2]; Overbeek, Jamie C. ^[4]; Pusch, Gordon D. ^[3]; Vonstein, Veronika ^[3]; Overbeek, Ross ^[1]

Fellowship for Interpretation of Genomes, Burr Ridge, IL (United States); Univ. of Chicago, Chicago, IL (United States)
Argonne National Lab. (ANL), Lemont, IL (United States)
Fellowship for Interpretation of Genomes, Burr Ridge, IL (United States)
Fellowship for Interpretation of Genomes, Burr Ridge, IL (United States); Argonne National Lab. (ANL), Lemont, IL (United States)

Publication Date:: 2019-10-03

Research Org.:: Argonne National Lab. (ANL), Argonne, IL (United States)

Sponsoring Org.:: USDOE Office of Science (SC); National Institutes of Health (NIH) - National Institute of Allergy and Infectious Diseases (NIAID)

OSTI Identifier:: 1579345

Grant/Contract Number:: AC02-06CH11357; HHSN272201400027C

Resource Type:: Accepted Manuscript

Journal Name:: BMC Bioinformatics

Additional Journal Information:: Journal Volume: 20; Journal Issue: 1; Journal ID: ISSN 1471-2105

Publisher:: BioMed Central

Country of Publication:: United States

Language:: English

Subject:: 59 BASIC BIOLOGICAL SCIENCES; CheckM; RAST; genome annotation; genome quality; machine learning; metagenomics; random forest; supervised learning

Citation Formats


                    Parrello, Bruce, Butler, Rory, Chlenski, Philippe, Olson, Robert, Overbeek, Jamie C., Pusch, Gordon D., Vonstein, Veronika, and Overbeek, Ross. A machine learning-based service for estimating quality of genomes using PATRIC.  United States: N. p., 2019. 
Web.  doi:10.1186/s12859-019-3068-y.

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                    Parrello, Bruce, Butler, Rory, Chlenski, Philippe, Olson, Robert, Overbeek, Jamie C., Pusch, Gordon D., Vonstein, Veronika, & Overbeek, Ross. A machine learning-based service for estimating quality of genomes using PATRIC.  United States.  https://doi.org/10.1186/s12859-019-3068-y

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                    Parrello, Bruce, Butler, Rory, Chlenski, Philippe, Olson, Robert, Overbeek, Jamie C., Pusch, Gordon D., Vonstein, Veronika, and Overbeek, Ross. Thu .  
"A machine learning-based service for estimating quality of genomes using PATRIC".  United States.  https://doi.org/10.1186/s12859-019-3068-y.  https://www.osti.gov/servlets/purl/1579345.

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@article{osti_1579345,

  title        = {A machine learning-based service for estimating quality of genomes using PATRIC},

  author       = {Parrello, Bruce and Butler, Rory and Chlenski, Philippe and Olson, Robert and Overbeek, Jamie C. and Pusch, Gordon D. and Vonstein, Veronika and Overbeek, Ross},

  abstractNote = {Recent advances in high-volume sequencing technology and mining of genomes from metagenomic samples call for rapid and reliable genome quality evaluation. The current release of the PATRIC database contains over 220,000 genomes, and current metagenomic technology supports assemblies of many draft-quality genomes from a single sample, most of which will be novel. We have added two quality assessment tools to the PATRIC annotation pipeline. EvalCon uses supervised machine learning to calculate an annotation consistency score. EvalG implements a variant of the CheckM algorithm to estimate contamination and completeness of an annotated genome.We report on the performance of these tools and the potential utility of the consistency score. Additionally, we provide contamination, completeness, and consistency measures for all genomes in PATRIC and in a recent set of metagenomic assemblies. EvalG and EvalCon facilitate the rapid quality control and exploration of PATRIC-annotated draft genomes.},

  doi          = {10.1186/s12859-019-3068-y},

  journal      = {BMC Bioinformatics},

  number       = 1,

  volume       = 20,

  place        = {United States},

  year         = {2019},

  month        = {10}

}

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Works referenced in this record:

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journal, November 2016

Wattam, Alice R.; Davis, James J.; Assaf, Rida
Nucleic Acids Research, Vol. 45, Issue D1
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Snyder, E. E.; Kampanya, N.; Lu, J.
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Pasolli, Edoardo; Asnicar, Francesco; Manara, Serena
Cell, Vol. 176, Issue 3
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Simão, Felipe A.; Waterhouse, Robert M.; Ioannidis, Panagiotis
Bioinformatics, Vol. 31, Issue 19
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journal, May 2015

Parks, Donovan H.; Imelfort, Michael; Skennerton, Connor T.
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Kang, Dongwan D.; Froula, Jeff; Egan, Rob
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Overbeek, Ross; Olson, Robert; Pusch, Gordon D.
Nucleic Acids Research, Vol. 42, Issue D1
DOI: 10.1093/nar/gkt1226

Works referencing / citing this record:

The PATRIC Bioinformatics Resource Center: expanding data and analysis capabilities
journal, October 2019

Davis, James J.; Wattam, Alice R.; Aziz, Ramy K.
Nucleic Acids Research
DOI: 10.1093/nar/gkz943

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