Synthaser:支持 CD 搜索的 Python 工具包,用于分析真菌次级代谢物巨合成物的结构域。

Q1 Agricultural and Biological Sciences
Cameron L M Gilchrist, Yit-Heng Chooi
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引用次数: 0

摘要

背景:真菌是多产次级代谢产物(SMs)的生产者,次级代谢产物是具有生物活性的小分子,在医药、农业和其他工业领域具有重要应用。大部分真菌次生代谢物的骨架是通过大型多域巨合成酶(如聚酮酸合成酶(PKSs)和非核糖体肽合成酶(NRPSs))的作用生成的。这些骨架的结构由相应巨合成酶的结构域决定,因此对这些结构进行准确的注释和分类是将 SM 与它们在基因组中的生物合成起源联系起来的重要一步:在此,我们报告了一个 Python 软件包 synthaser,该软件包利用 NCBI 的保守结构域搜索工具对真菌巨合成结构域进行远程预测和分类。Synthaser 能够进行批量序列分析,并产生丰富的文本输出和交互式可视化效果,从而快速评估真菌基因组的巨合成域多样性。Synthaser 使用基于规则的分级分类系统,用户可通过网络应用程序 ( http://gamcil.github.io/synthaser ) 对该系统进行广泛定制。我们的研究表明,Synthaser 能提供比基于pHMM 方法的同类工具更准确的结构域预测;与 pHMM 方法相比,Synthaser 利用了 NCBI 保守结构域数据库,因此具有更大的灵活性。此外,我们还通过构建曲霉 PKS 相似性网络,展示了 Synthaser 如何应用于大规模基因组挖掘管道:Synthaser 是一款易于使用的工具,是对以往领域架构分析工具的重大升级。它在 MIT 许可下可从 PyPI ( https://pypi.org/project/synthaser ) 和 GitHub ( https://github.com/gamcil/synthaser ) 免费获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Synthaser: a CD-Search enabled Python toolkit for analysing domain architecture of fungal secondary metabolite megasynth(et)ases.

Synthaser: a CD-Search enabled Python toolkit for analysing domain architecture of fungal secondary metabolite megasynth(et)ases.

Synthaser: a CD-Search enabled Python toolkit for analysing domain architecture of fungal secondary metabolite megasynth(et)ases.

Synthaser: a CD-Search enabled Python toolkit for analysing domain architecture of fungal secondary metabolite megasynth(et)ases.

Background: Fungi are prolific producers of secondary metabolites (SMs), which are bioactive small molecules with important applications in medicine, agriculture and other industries. The backbones of a large proportion of fungal SMs are generated through the action of large, multi-domain megasynth(et)ases such as polyketide synthases (PKSs) and nonribosomal peptide synthetases (NRPSs). The structure of these backbones is determined by the domain architecture of the corresponding megasynth(et)ase, and thus accurate annotation and classification of these architectures is an important step in linking SMs to their biosynthetic origins in the genome.

Results: Here we report synthaser, a Python package leveraging the NCBI's conserved domain search tool for remote prediction and classification of fungal megasynth(et)ase domain architectures. Synthaser is capable of batch sequence analysis, and produces rich textual output and interactive visualisations which allow for quick assessment of the megasynth(et)ase diversity of a fungal genome. Synthaser uses a hierarchical rule-based classification system, which can be extensively customised by the user through a web application ( http://gamcil.github.io/synthaser ). We show that synthaser provides more accurate domain architecture predictions than comparable tools which rely on curated profile hidden Markov model (pHMM)-based approaches; the utilisation of the NCBI conserved domain database also allows for significantly greater flexibility compared to pHMM approaches. In addition, we demonstrate how synthaser can be applied to large scale genome mining pipelines through the construction of an Aspergillus PKS similarity network.

Conclusions: Synthaser is an easy to use tool that represents a significant upgrade to previous domain architecture analysis tools. It is freely available under a MIT license from PyPI ( https://pypi.org/project/synthaser ) and GitHub ( https://github.com/gamcil/synthaser ).

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来源期刊
Fungal Biology and Biotechnology
Fungal Biology and Biotechnology Agricultural and Biological Sciences-Ecology, Evolution, Behavior and Systematics
CiteScore
10.20
自引率
0.00%
发文量
17
审稿时长
9 weeks
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