AutoMLST: an automated web server for generating multi-locus species trees highlighting natural product potential
AutoMLST: an automated web server for generating multi-locus species trees highlighting natural product potential
复制标题
DOI：
10.1093/nar/gkz282
复制
发表时间：
2019-07-02
影响因子：
14.9
通讯作者：
Ziemert, Nadine
中科院分区：
文献类型：
--
作者：
Alanjary, Mohammad;Steinke, Katharina;Ziemert, Nadine
了解细菌分离物的进化背景对于广泛的研究具有应用价值。然而，产生准确的物种亲缘关系仍然具有挑战性。目前，依赖16 S rDNA进行物种鉴定仍然很流行。不幸的是，这种广泛使用的方法由于高度的序列保守性而在物种水平上具有低分辨率。目前，有大量的基因组数据可用于通过现代系统发育方法和多个遗传位点产生更准确的物种命名。然而，这些往往需要大量的专业知识和时间。因此，开发了自动化多位点物种树（autoMLST），以提供快速的“一次点击”管道来简化该工作流程，网址为：https://automlst.ziemertlab.com。该服务器利用多位点序列分析（MLSA）来生成高分辨率的物种树;这不会执行多位点序列分型（MLST），这是一种相关的分类方法。由此产生的系统发育树还包括有用的注释，如物种分支名称和次级代谢物计数，以帮助天然产物勘探。与目前可用的网络界面不同，autoMLST可以基于一个或多个查询基因组自动选择参考基因组和外群生物。这使得广泛的研究人员能够比手动MLSA工作流程更快地执行严格的系统发育分析。
Understanding the evolutionary background of a bacterial isolate has applications for a wide range of research. However generating an accurate species phylogeny remains challenging. Reliance on 16S rDNA for species identification currently remains popular. Unfortunately, this widespread method suffers from low resolution at the species level due to high sequence conservation. Currently, there is now a wealth of genomic data that can be used to yield more accurate species designations via modern phylogenetic methods and multiple genetic loci. However, these often require extensive expertise and time. The Automated Multi-Locus Species Tree (autoMLST) was thus developed to provide a rapid 'oneclick' pipeline to simplify this workflow at: https://automlst.ziemertlab.com. This server utilizes MultiLocus Sequence Analysis (MLSA) to produce high-resolution species trees; this does not preform multilocus sequence typing (MLST), a related classification method. The resulting phylogenetic tree also includes helpful annotations, such as species clade designations and secondary metabolite counts to aid natural product prospecting. Distinct from currently available web-interfaces, autoMLST can automate selection of reference genomes and out-group organisms based on one or more query genomes. This enables a wide range of researchers to perform rigorous phylogenetic analyses more rapidly compared to manual MLSA workflows.