Xing Yue, Li Xiao, Gao Xiang, Dong Qunfeng
Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, United States.
Department of Molecular and Cellular Medicine, Texas A&M University, College Station, TX, United States.
Front Microbiol. 2020 Jun 25;11:1502. doi: 10.3389/fmicb.2020.01502. eCollection 2020.
With the continued spread of SARS-CoV-2 virus around the world, researchers often need to quickly identify novel mutations in newly sequenced SARS-CoV-2 genomes for studying the molecular evolution and epidemiology of the virus. We have developed a Python package, MicroGMT, which takes either raw sequence reads or assembled genome sequences as input and compares against database sequences to identify and characterize indels and point mutations. Although our default setting is optimized for SARS-CoV-2 virus, the package can be also applied to any other microbial genomes. The software is freely available at Github URL https://github.com/qunfengdong/MicroGMT.
随着严重急性呼吸综合征冠状病毒2(SARS-CoV-2)病毒在全球的持续传播,研究人员经常需要快速识别新测序的SARS-CoV-2基因组中的新突变,以研究该病毒的分子进化和流行病学。我们开发了一个Python软件包MicroGMT,它以原始序列读数或组装的基因组序列作为输入,并与数据库序列进行比较,以识别和表征插入缺失和点突变。虽然我们的默认设置是针对SARS-CoV-2病毒进行优化的,但该软件包也可应用于任何其他微生物基因组。该软件可在Github网址https://github.com/qunfengdong/MicroGMT上免费获取。