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相关概念视频

RNA-seq03:21

RNA-seq

9.9K
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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Real Time RT-PCR02:57

Real Time RT-PCR

57.1K
Real-time reverse transcription-polymerase chain reaction, or Real-time RT-PCR, is an analytical tool used to determine the expression level of target genes. The method involves converting mRNA to complementary DNA with the help of an enzyme known as reverse transcriptase, followed by the PCR amplification of the cDNA. These two processes can be performed simultaneously in a single tube or separately as a two-step reaction.
The real-time quantification of the number of amplified products is...
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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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相关实验视频

Updated: Jun 17, 2025

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

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RankCompV3:基于相对表达顺序和应用在单细胞RNA转录学中的差异表达分析算法.

Jing Yan1, Qiuhong Zeng1, Xianlong Wang2,3

  • 1Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, School of Medical Technology and Engineering, Fujian Medical University, Fuzhou, 350122, China.

BMC bioinformatics
|August 7, 2024
PubMed
概括

RankCompV3通过比较基因表达顺序,准确地识别单细胞RNA测序 (scRNA-seq) 数据中的差异表达基因 (DEG). 这种新的方法可以提高对微弱生物信号的灵敏度,并有效控制假阳性率.

关键词:
微分表达式分析 微分表达式分析不同表达的基因.相对表达式的排序相对表达式的排序单细胞RNA测序的一个细胞.

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Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
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科学领域:

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

背景情况:

  • 对差异表达基因 (DEGs) 的准确识别对于单细胞RNA测序 (scRNA-seq) 数据至关重要,但具有挑战性.
  • 现有的算法往往遭受高假阳性率 (FPRs) 和错过微妙的生物信号.

研究的目的:

  • 引入RankCompV3,一种用于识别scRNA-seq配置文件中的DEGs的新方法.
  • 解决现有的DEG检测算法在准确性和灵敏性方面的局限性.

主要方法:

  • RankCompV3使用基因对的相对表达顺序 (REO).
  • 基因表达水平在单细胞资料中进行比较,以确定对对排名.
  • 使用3x3应急表和麦卡拉的方法来评估基因失调.

主要成果:

  • RankCompV3在模拟和真实scRNA-seq数据上展示了FPR的强有力的控制和高精度.
  • 该方法的性能优于其他11种常见的单细胞DEG检测算法.
  • 与现有方法相比,RankCompV3对弱生物信号的敏感性更高.

结论:

  • 基于REO的RankCompV3算法是scRNA-seq数据分析的宝贵工具.
  • 它可以准确和敏感地识别DEGs.
  • 该算法是在 Julia 中实现的,并且可以在 R 中调用,并且有源代码可用.