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.
Year
DOI
Venue
2019
10.1186/s12859-019-3068-y
BMC Bioinformatics
Keywords
DocType
Volume
CheckM, RAST, Genome annotation, Random forest, Machine learning, Metagenomics, Genome quality, Supervised learning
Journal
20
Issue
ISSN
Citations 
1
1471-2105
0
PageRank 
References 
Authors
0.34
0
8
Name
Order
Citations
PageRank
Bruce Parrello15910.25
Rory Butler200.34
Philippe Chlenski310.72
Robert Olson450838.89
Jamie Overbeek500.34
Gordon D Pusch621732.32
Veronika Vonstein714510.65
Ross Overbeek830.85