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相关概念视频

Reporter Genes02:11

Reporter Genes

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Reporter genes are a type of protein-coding gene that are often tagged to a gene of interest. Once inside a target cell, reporter genes usually produce visually identifiable characteristics like fluorescence and luminescence when expressed along with the gene of interest. Thus, reporter genes “report” the presence or absence of genes of interest in an organism, determine the gene expression pattern, or track the physical location of a DNA segment or protein in the cell.
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Ribosome Profiling02:24

Ribosome Profiling

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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
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通用报告员基于分数的丰富分析用于omics数据.

Chen Peng1,2, Qiong Chen1,2, Shangjin Tan3,4

  • 1MOE Key Laboratory of Biosystems Homeostasis & Protection, and Zhejiang Provincial Key Laboratory of Cancer Molecular Cell Biology, Life Sciences Institute, Zhejiang University, Hangzhou, Zhejiang 310030, China.

Briefings in bioinformatics
|March 28, 2024
PubMed
概括

通用报告员基于分数的分析 (GRSA) 为多组和纵向omics数据提供了增强的灵敏度. 这种在ReporterScore R包中实施的新方法改进了现有的丰富分析技术.

关键词:
定制的数据库定制的数据库丰富分析是一种丰富分析.多集团的多个集团.奥米克斯数据数据的数据.记者得分 记者得分 记者得分

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科学领域:

  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学
  • 基因组学就是基因组学.

背景情况:

  • 丰富分析对于理解生物医学研究中的高维奥米克数据至关重要.
  • 基于Reporter分数的分析 (RSA) 通过使用P值,提供了比传统方法更好的灵敏度.
  • 现有的RSA方法仅适用于复杂的实验设计,如多组和纵向研究.

研究的目的:

  • 开发一种新的方法,即通用报告员基于分数的分析 (GRSA),用于分析多组和纵向omics数据.
  • 为复杂的生物研究增强基于报告员分数的丰富分析的敏感性和适用性.
  • 提供一个用户友好的R包,ReporterScore,用于实施GRSA并促进omics数据的解释.

主要方法:

  • 开发了通用报告员基于分数的分析 (GRSA) 算法.
  • 使用基准数据集将GRSA与现有丰富分析方法的应用和比较.
  • 在R包"ReporterScore"中实现GRSA,包括可视化工具和路径数据库.

主要成果:

  • 与其他流行的丰富分析方法相比,GRSA在多个数据集中表现出更高的灵敏度.
  • 将GRSA成功应用于微生物组,转录组和代谢组数据,揭示了新的生物学见解.
  • 证明了GRSA在功能丰富分析之外的实用性,包括其与分类学数据库的应用.

结论:

  • GRSA是一种灵敏且多用途的方法,用于对多组和纵向omics数据进行丰富分析.
  • 报告员Score R包为生物医学研究社区提供了一种有价值,易于使用的工具.
  • 通过改进的奥米克数据分析,GRSA促进了对复杂生物系统的系统理解.