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相关概念视频

Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

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Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
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Methods to Assess Microbial Populations01:30

Methods to Assess Microbial Populations

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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...
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Methods to Assess Microbial Communities01:19

Methods to Assess Microbial Communities

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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...
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Microorganisms colonize various regions of the human body, including the mouth, nasal passages, throat, stomach, intestines, urogenital tract, and skin. The total number of microbial cells is estimated to range from 10¹³ to 10¹⁴—comparable to, or exceeding, the number of human somatic cells. This host–microbiome relationship has led to the conceptualization of humans as supraorganisms, wherein microbial communities perform vital roles in development, immunity,...
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The Oral Microbiota01:27

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The oral microbiome includes a complex ecosystem comprising over 700 microbial species, identified through genomic sequencing and culture-based analyses to date. This community includes a core microbiome, found universally among individuals, and a variable component influenced by environmental factors such as diet, lifestyle, and host genetics. Site-specific conditions, including oxygen gradients, pH levels, and nutrient availability, determine the spatial distribution of these microorganisms...
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Automated diagnostic analyzers have transformed clinical microbiology by providing rapid and reliable methods for pathogen identification and antibiotic susceptibility testing. Among these systems, the Vitek 2 is widely used because it automates the traditionally labor-intensive processes of microbial identification (ID) and antibiotic susceptibility testing (AST), delivering standardized and timely results that are essential for effective patient care.Microbial Identification with ID CardsThe...
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相关实验视频

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CAIM:用于识别微生物群的基于覆盖范围的分析.

Daniel A Acheampong1,2, Piroon Jenjaroenpun1,3, Thidathip Wongsurawat1,3

  • 1Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.

bioRxiv : the preprint server for biology
|May 15, 2024
PubMed
概括

我们开发了CAIM,这是一个新的生物信息学工具,用于在元基因组样本中准确的微生物分类学概况. 通过基因组覆盖,CAIM改善了物种识别和丰度估计,在各种数据集中表现优于现有的方法.

关键词:
生物信息学是一种生物信息学.我们的肠道微生物组.甲基基因组是指甲基因组中的一部分.甲基基因组覆盖范围分类学识别分类学识别.

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科学领域:

  • 微生物生态学 微生物生态学
  • 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:

  • 准确的分类学概况对于理解微生物生态学至关重要.
  • 整个猎枪元基因组 (WMS) 测序推进了物种级微生物分析.
  • 现有的工具在精确的分类和量化方面面临着挑战.

研究的目的:

  • 开发一种新的生物信息学工具,CAIM,用于准确的分类学分类和量化元基因组样本.
  • 通过基因组覆盖信息和基于核酸计数的丰度估计来增强物种识别.
  • 将CAIM的性能与其他领先的工具进行比较,并验证其在现实世界数据集中的实用性.

主要方法:

  • 开发了基于对齐的生物信息学工具CAIM,利用制技术和基因组覆盖面用于物种识别.
  • 实施了基于核酸计数的丰度估计方法.
  • 在模拟和合成数据集上评估CAIM,将性能与其他工具进行比较.
  • 将CAIM应用于纳米孔,Illumina和便枪甲基因组数据集.

主要成果:

  • 在识别微生物种群和估计各种数据集中的相对丰度方面,CAIM表现一致.
  • 在纳米孔和Illumina测序平台之间观察到分类学概况的高度相似性.
  • 使用CAIM的基因组覆盖切断值的模型在与相对丰度切断值相比,在将癌症患者与对照组区分开来方面表现出优异的预测性能.

结论:

  • 在长度和短度读取的元基因组数据中,CAIM提供了一种强大而准确的分类学分析方法.
  • 该工具有效过错误的阳性结果,并提供可靠的丰度估计.
  • 在临床元基因组学中,CAIM基于基因组覆盖的方法对识别疾病特异性微生物标记物具有前景.