Institute of Clinical Molecular Biology, Kiel University, 24105 Kiel, Germany.
Institute for Medical Microbiology and Hospital Epidemiology, Hannover Medical School, 30625 Hannover, Germany.
Bioinformatics. 2022 Dec 13;38(24):5430-5433. doi: 10.1093/bioinformatics/btac694.
Recovery of metagenome-assembled genomes (MAGs) from shotgun metagenomic data is an important task for the comprehensive analysis of microbial communities from variable sources. Single binning tools differ in their ability to leverage specific aspects in MAG reconstruction, the use of ensemble binning refinement tools is often time consuming and computational demand increases with community complexity. We introduce MAGScoT, a fast, lightweight and accurate implementation for the reconstruction of highest-quality MAGs from the output of multiple genome-binning tools.
MAGScoT outperforms popular bin-refinement solutions in terms of quality and quantity of MAGs as well as computation time and resource consumption.
MAGScoT is available via GitHub (https://github.com/ikmb/MAGScoT) and as an easy-to-use Docker container (https://hub.docker.com/repository/docker/ikmb/magscot).
Supplementary data are available at Bioinformatics online.
从鸟枪法宏基因组数据中恢复宏基因组组装基因组（MAG）是对来自不同来源的微生物群落进行综合分析的重要任务。单 bin 工具在利用 MAG 重建特定方面的能力上存在差异，使用集合 bin 细化工具通常很耗时，并且随着群落复杂性的增加计算需求也会增加。我们引入了 MAGScoT，这是一种快速、轻量级且准确的实现方法，可从多个基因组 bin 工具的输出中重建最高质量的 MAG。
MAGScoT 在 MAG 的质量和数量以及计算时间和资源消耗方面均优于流行的 bin 细化解决方案。
MAGScoT 可通过 GitHub（https://github.com/ikmb/MAGScoT）和易于使用的 Docker 容器（https://hub.docker.com/repository/docker/ikmb/magscot）获得。
补充数据可在《Bioinformatics》在线获得。
