基因组
计算机科学
康蒂格
数据挖掘
集合(抽象数据类型)
生物
遗传学
基因组
基因
程序设计语言
作者
Jennifer Mattock,Mick Watson
出处
期刊:Nature Methods
[Springer Nature]
日期:2023-06-29
卷期号:20 (8): 1170-1173
被引量:28
标识
DOI:10.1038/s41592-023-01934-8
摘要
Metagenomic binning has revolutionized the study of uncultured microorganisms. Here we compare single- and multi-coverage binning on the same set of samples, and demonstrate that multi-coverage binning produces better results than single-coverage binning and identifies contaminant contigs and chimeric bins that other approaches miss. While resource expensive, multi-coverage binning is a superior approach and should always be performed over single-coverage binning.
科研通智能强力驱动
Strongly Powered by AbleSci AI