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SyFi:生成和使用序列指纹来区分SynCom隔离物

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概括

我们开发了一种生物信息学工具SynCom Fingerprinting (SyFi), SyFi通过利用基因组变异进行精确的成员识别来改善根微生物组分析.

关键词:
放大器序列化复制号的变化标记序列微生物组合成社区

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科学领域:

  • 微生物生态学
  • 生物信息学
  • 植物科学

背景情况:

  • 植物根微生物组是由细菌,宿主植物和环境因素影响的复杂社区.
  • 合成社区 (SynCom) 实验简化了这些相互作用,为微生物组组装和功能提供了洞察力.
  • 增加SynCom复杂性以实现更大的自然表现,这给生物信息学带来了挑战,特别是通过16S rRNA amplicon测序准确识别和量化成员,因为它们具有很高的amplicon相似性.

研究的目的:

  • 推出一个新的生物信息工作流程 - - SynCom Fingerprinting (SyFi).
  • 提高合成微生物群落 (SynComs) 中的识别和定量成员的分辨率和准确性.
  • 为了克服复杂SynComs标准安普利康测序分析的局限性.

主要方法:

  • SyFi使用基因组序列和/或原始读取构建每个SynCom成员的基因组指纹,考虑目标基因拷贝数和序列变异.
  • 通过从基因组指纹中提取目标区域来创建与片序列相关的二次指纹.
  • 基于伪对齐的SynCom成员数量测定使用这些指纹作为对抗amplicon测序读数的参考.

主要成果:

  • 与标准的安普利康分析方法相比,SyFi表现出更高的性能.
  • 工作流程有效地利用自然的基因内变异来精确区分密切相关的SynCom成员.
  • SyFi显著提高了分析复杂SynComs的可靠性,这些SynComs更接近自然根微生物组.

结论:

  • SyFi提高了复杂合成微生物群体中识别和量化成员的准确性.
  • 这种改进的分辨率对于进一步了解根微生物组的动态及其对植物健康的影响至关重要.
  • SyFi的工作流支持在农业和生态环境中更可靠的微生物组研究.