从测序数据中获取的微生物丰度自动NCBI分类学 (MARS):为微生物组建模型工具箱创建相对微生物丰度数据的管道,并利用同义词来有效地映射资源
Tim Hulshof1,2, Bram Nap1,2, Filippo Martinelli1,2
1School of Medicine, University of Galway, Galway H91 TK33, Ireland.
Bioinformatics advances
|June 24, 2024
概括
通过自动化相对丰富的提取和绘制,MARS管道简化了微生物组分析,克服了分类学名称变化的挑战. 该工具增强了微生物代谢重建和与微生物组建模资源的整合.
科学领域:
- 微生物组研究的研究.
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
背景情况:
- 准确的微生物组成和相对丰度对于计算微生物组分析至关重要.
- 同义词 (同一个分类群的不同名称) 在绘制物种图表时带来了挑战,并可能导致在代谢重建资源中错过识别.
研究的目的:
- 介绍MARS管道,这是一个基于Python的解决方案,用于自动化和改进微生物组数据处理.
- 为应对因同义词和分类学变异引起的物种绘制挑战.
主要方法:
- 马斯自动化了从元基因组读数中提取相对丰富的数据.
- 它将物种和属映射到微生物代谢重建,处理替代的分类学名称.
- 一个子组件使用web scraping和NCBI交叉引用来识别同性同义词.
主要成果:
- 火星将微生物读数正常化,并为低丰度的种群提供可选的切断线.
- 它生成与微生物组建模工具箱兼容的相对丰度表.
- 管道简化了从原始读数到可用的丰度数据的流程,节省了时间和精力.
结论:
- 马斯为微生物组数据分析提供了用户友好和高效的解决方案.
- 它提高了微生物代谢重建的准确性和完整性.
- 该工具有助于与现有的微生物组建模框架进行集成.
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