小柯机器人

近日，澳大利亚昆士兰理工大学Gene W. Tyson团队开发出利用机器学习评估微生物基因组质量的新工具。相关论文于2023年7月27日在线发表在《自然—方法学》杂志上。

研究人员介绍一种利用机器学习预测元基因组组装基因组（MAG）基因组质量的改进方法CheckM2。研究人员利用合成和实验数据证明，CheckM2在准确性和计算速度上都优于现有工具。此外，CheckM2的数据库可以通过新的高质量参考基因组快速更新，包括仅由单个基因组代表的类群。

研究人员还发现，CheckM2能准确预测新品系MAG的基因组质量，即使是那些基因组大小较小的品系（如Patescibacteria和DPANN超门）也不例外。CheckM2可以准确预测细菌和古细菌系的基因组质量，从而增强了从MAG推断生物学结论的信心。

据悉，测序技术和生物信息学工具的进步大大提高了元基因组数据中微生物基因组的恢复率。评估MAG的质量是下游分析前的关键一步。

附：英文原文

Title: CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning

Author: Chklovski, Alex, Parks, Donovan H., Woodcroft, Ben J., Tyson, Gene W.

Issue&Volume: 2023-07-27

Abstract: Advances in sequencing technologies and bioinformatics tools have dramatically increased the recovery rate of microbial genomes from metagenomic data. Assessing the quality of metagenome-assembled genomes (MAGs) is a critical step before downstream analysis. Here, we present CheckM2, an improved method of predicting genome quality of MAGs using machine learning. Using synthetic and experimental data, we demonstrate that CheckM2 outperforms existing tools in both accuracy and computational speed. In addition, CheckM2’s database can be rapidly updated with new high-quality reference genomes, including taxa represented only by a single genome. We also show that CheckM2 accurately predicts genome quality for MAGs from novel lineages, even for those with reduced genome size (for example, Patescibacteria and the DPANN superphylum). CheckM2 provides accurate genome quality predictions across bacterial and archaeal lineages, giving increased confidence when inferring biological conclusions from MAGs.

DOI: 10.1038/s41592-023-01940-w

Source: https://www.nature.com/articles/s41592-023-01940-w

Nature Methods：《自然—方法学》，创刊于2004年。隶属于施普林格·自然出版集团，最新IF：47.99
官方网址：https://www.nature.com/nmeth/
投稿链接：https://mts-nmeth.nature.com/cgi-bin/main.plex