HiFiBGC:在PacBio HiFi读取的元基因组中改进生物合成基因集群检测的整体方法
Amit Yadav1,2, Srikrishna Subramanian3,4
1CSIR-Institute of Microbial Technology (IMTECH), Sector 39-A, Chandigarh, 160036, India. amityadav@imtech.res.in.
BMC genomics
|November 16, 2024
概括
HiFiBGC从长期阅读的元基因组中识别出更多的微生物生物合成基因集群 (BGCs). 这种计算工作流通过增强BGC从复杂的基因组数据的恢复来改善自然产品的发现.
科学领域:
- 微生物学 微生物学
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
背景情况:
- 微生物产生有价值的生物活性天然产品,其编码由生物合成基因集群 (BGCs).
- 从基因组和元基因组数据中识别高通量BGC对于发现天然产品至关重要.
- 短读测序技术难以组装完整的BGC,特别是来自复杂的元基因组.
研究的目的:
- 开发一种改进的计算方法,用于在长时间读取的元基因组数据中识别BGC.
- 为了利用PacBio HiFi测序的最新进展,提高BGC恢复.
- 为了克服从复杂的元基因组组装完整的BGCs的局限性.
主要方法:
- 开发了HiFiBGC,一个命令行工作流,利用来自三个HiFi定制的元基因组组件的组合.
- 嵌入的未映射读取包含碎片化的BGCs的分析.
- 将工作流应用于四个不同的HiFi元基因组数据集.
主要成果:
- 与单个装配器方法相比,HiFiBGC平均发现了78%更多的BGC.
- 整体组装方法使BGC回收率提高了25%.
- 包括未映射的读数进一步增强了碎片化的BGCs的识别.
结论:
- HiFiBGC是一种有效的计算工作流程,用于识别长时间读取的HiFi元基因组中的BGC.
- 工作流程显著改善了BGC的回收,有助于天然产品的发现.
- HiFiBGC是用Python实现的,并在GitHub上提供.
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