Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Methods to Assess Microbial Populations01:30

Methods to Assess Microbial Populations

Assessing microbial populations is crucial for understanding microbial roles in health, ecology, and industry. Various complementary techniques—both culture-based and molecular—enable detailed analysis of microbial abundance, diversity, and function.Viable Plate CountThe viable plate count is a traditional culture-based method used to estimate the number of living microbes in a sample. After serial dilution, the sample is spread onto nutrient agar plates. Each viable cell forms a visible...
Methods to Assess Microbial Communities01:19

Methods to Assess Microbial Communities

Microbial communities, comprising bacteria, archaea, and eukaryotic microorganisms, inhabit diverse ecosystems and play crucial roles in environmental and biological processes. Their diversity is defined by three main parameters: species richness (the number of distinct species), species abundance (the relative quantity of each species), and species evenness (how uniformly individual species are distributed in various locations). These factors together shape the structure and ecological balance...
Development of Human Microbiota01:30

Development of Human Microbiota

The human microbiota begins developing at birth and undergoes continual change as we age. Infancy marks a critical period of microbial sensitivity, offering a “window of opportunity” during which beneficial microbes help mature the immune system. By age three, children typically develop a more stable and diverse microbial community. Newborns acquire microbes from their immediate environment; vaginal delivery favors maternal vaginal microbes, while cesarean births favor microbes from the skin...

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Φ-Space ST: A platform-agnostic method to identify cell states in spatial transcriptomics studies.

Cell reports methods·2026
Same author

Multi-Omics Reveals Early Pregnancy Placental Dysfunction Associated With Preterm and Term Preeclampsia.

MedComm·2026
Same author

Integrated lipidome and miRNome analyses reveal sex-based differences in circulating extracellular vesicles of alcohol use disorder patients.

Cell biology and toxicology·2026
Same author

Integrated transcriptomic and clinical analysis of autism spectrum disorder reveals structured heterogeneity and links Methyl-CpG Binding Domain Protein 2 expression with symptom severity.

Psychiatry and clinical neurosciences·2026
Same author

phylobar: an R package for multiresolution compositional barplots in omics studies.

Bioinformatics (Oxford, England)·2026
Same author

Scalable cell-specific coexpression networks for granular regulatory pattern discovery with NeighbourNet.

Genome research·2026

相关实验视频

Updated: Jun 17, 2026

A Method for Targeted 16S Sequencing of Human Milk Samples
09:09

A Method for Targeted 16S Sequencing of Human Milk Samples

Published on: March 23, 2018

9.8K

用LUPINE进行纵向微生物组研究的微生物网络推断.

Saritha Kodikara1, Kim-Anh Lê Cao2

  • 1Melbourne Integrative Genomics, School of Mathematics and Statistics, The University of Melbourne, Royal Parade, 3052, Parkville, Victoria, Australia.

Microbiome
|March 3, 2025
PubMed
概括

研究人员开发了LUPINE,这是一种分析纵向微生物群数据的新方法,以推断随时间推移的微生物相互作用. 这种方法有效地捕捉了复杂微生物生态系统中的动态关系,即使数据有限.

关键词:
16S 16S 是一个纵向的 纵向的 纵向的网络 网络 网络 网络 网络 网络部分相关性 部分相关性

更多相关视频

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
09:49

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

4.3K
Investigation of Microbial Cooperation via Imaging Mass Spectrometry Analysis of Bacterial Colonies Grown on Agar and in Tissue During Infection
09:49

Investigation of Microbial Cooperation via Imaging Mass Spectrometry Analysis of Bacterial Colonies Grown on Agar and in Tissue During Infection

Published on: November 18, 2022

2.0K

相关实验视频

Last Updated: Jun 17, 2026

A Method for Targeted 16S Sequencing of Human Milk Samples
09:09

A Method for Targeted 16S Sequencing of Human Milk Samples

Published on: March 23, 2018

9.8K
Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
09:49

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

4.3K
Investigation of Microbial Cooperation via Imaging Mass Spectrometry Analysis of Bacterial Colonies Grown on Agar and in Tissue During Infection
09:49

Investigation of Microbial Cooperation via Imaging Mass Spectrometry Analysis of Bacterial Colonies Grown on Agar and in Tissue During Infection

Published on: November 18, 2022

2.0K

科学领域:

  • 微生物组研究的研究.
  • 网络推断网络推断.
  • 生物信息学是一种生物信息学.

背景情况:

  • 微生物组研究传统上使用横截面数据,限制了对动态微生物相互作用的理解.
  • 纵向微生物组研究正在获得引力,以推断种类之间的时间关联.
  • 对于纵向微生物组数据,现有的网络推断方法尚未得到充分探索.

研究的目的:

  • 引入LUPINE (LongitUdinal建模与部分最小平方回归用于NETwork推断),这是一种用于纵向微生物群网络推断的新方法.
  • 解决传统方法在处理稀疏,组成和多变量微生物组数据方面的局限性.
  • 通过考虑所有过去的时间点来捕捉动态的微生物相互作用.

主要方法:

  • LUPINE利用了条件独立性和低维数据表示.
  • 该方法是为具有小样本大小和少数时间点的场景而设计的.
  • LUPINE结合了过去所有时间点的信息来推断网络.

主要成果:

  • LUPINE成功地推断了微生物网络跨时间,捕捉了动态相互作用.
  • 在模拟数据和四个案例研究中的验证表明,LUPINE能够识别相关的种类.
  • 该方法在各种实验设计中被证明是有效的,包括人类和小鼠研究.
  • 使用指标来比较推断网络,并检测随时间或外部因素的变化.

结论:

  • LUPINE是一种用于纵向微生物组数据分析的创新方法.
  • 这种方法适用于超越微生物组研究的各种生物背景.
  • 公共可用的R代码和数据有助于应用和进一步研究.