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相关概念视频

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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Cell Specific Gene Expression01:58

Cell Specific Gene Expression

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RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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DNA Microarrays02:34

DNA Microarrays

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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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相关实验视频

Updated: Jun 28, 2025

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards
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Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

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在单细胞RNA-seq数据中识别基因表达程序,使用线性相关性解释解释.

Yulia I Nussbaum1, K S M Tozammel Hossain2, Jussuf Kaifi3

  • 1Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65201, USA.

Journal of biomedical informatics
|April 17, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了线性CoreEx,这是一种机器学习方法,可以从单细胞RNA测序数据中识别与细胞类型和生物活动相关的基因表达程序 (GEP),从而改善基因调节的洞察力.

关键词:
发育生物学是发展生物学.机器学习 机器学习一个单细胞的单细胞.转移学习转移学习这就是 scRNA-seqq.

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相关实验视频

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Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards
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Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

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科学领域:

  • 基因组学和生物信息学
  • 计算生物学是一种计算生物学.
  • 分子生物学分子生物学

背景情况:

  • 单细胞RNA测序 (scRNA-seq) 推动了基因调控研究.
  • 目前的方法主要是识别细胞类型特定的基因表达程序 (GEP).
  • 对生物过程和刺激反应的GEP的特征是有限的.

研究的目的:

  • 从scRNA-seq数据中推断出具有生物意义的GEP.
  • 将GEP与细胞表型和活动计划联系起来.
  • 开发一种强大的方法来分析复杂的scRNA-seq信号.

主要方法:

  • 应用线性CoreEx,一种机器学习方法,根据总相关性优化对基因进行分组.
  • 利用模拟和现实世界scRNA-seq数据集进行GEP推断.
  • 员工将学习转移到跨数据集的项目推断的GEP.

主要成果:

  • 线性CorEx在模拟数据上的细胞类型和活动程序识别方面表现优于类似的方法.
  • 在小鼠牙状和胚胎结肠发育数据中确定了生物相关的GEP.
  • 证明了线性CorEx的跨物种敏感性和转移学习潜力.

结论:

  • 线性CoreEx是全面scRNA-seq数据分析的一个有价值的工具.
  • 提供了对基因表达动态和细胞异质性的更深入的见解.
  • 增强对复杂生物系统中的调节机制的理解.