KombOver:基于k-core和K-truss的高效表征人类肠道微生物群中的干扰
Nicolae Sapoval1, Marko Tanevski, Todd J Treangen
1Department of Computer Science, Rice University, Houston, TX 77005, USA, nsapoval@rice.edu.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|December 31, 2023
概括
KombOver (KO) 是一种新的计算工具,使用基于图表的方法分析人类肠道微生物群. 它有效地跟踪大队伍中的微生物社区动态,帮助疾病研究.
科学领域:
- 微生物学 微生物学
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 人的肠道微生物群越来越多地与健康和疾病有关.
- 在元基因组测序和计算分析方面的进步改善了对肠道微生物群的理解.
- 现有的计算工具需要加强用于跟踪微生物组动态,特别是短读测序数据.
研究的目的:
- 推出KombOver (KO),这是一个改进的新计算工具,用于分析元基因组数据.
- 加强微生物基因组动态和社区结构的大规模微生物组研究的跟踪.
- 为基于图形的元基因组表示提供理论框架,并改善用户体验.
主要方法:
- 开发KombOver (KO),一个可扩展的计算工具,包含基于图形的k-core和K-truss分析.
- 应用KO分析近1000个人类微生物样本.
- 基于图形的元基因组表示的理论分析.
主要成果:
- KO证明了可扩展性,高效地处理大型微生物组数据集 (每样本<10分钟和10GB RAM).
- 该工具成功地使用基于图表的方法在肌痛性脑脊髓炎/慢性疲劳综合征 (ME/CFS) 队列中确定了微生物社区动态.
- KO提供了更好的用户体验,更容易执行代码和更具描述性的输出.
结论:
- KombOver (KO) 是一种高效且可扩展的工具,用于使用基于图表的方法分析与宿主相关的微生物组动态.
- 基于图形的分析,特别是k-core和K-truss,对于精确确定疾病状态中的微生物社区转移非常有价值.
- KO比以前的元基因组分析方法提供了显著的改进,并且可以作为开源软件使用.
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