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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...
9.9K
Ribosome Profiling02:24

Ribosome Profiling

3.5K
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...
3.5K

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相关实验视频

Updated: Jun 25, 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

Published on: September 18, 2021

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RNA-Seq数据分析:模型和非模型生物的实用指南.

Enrique Pola-Sánchez1, Karen Magdalena Hernández-Martínez2, Rafael Pérez-Estrada3

  • 1Laboratorio Nacional de Genómica para la Biodiversidad-Unidad de Genómica Avanzada, Centro de Investigación y de Estudios Avanzados del IPN (CINVESTAV), Unidad Irapuato, Irapuato, México.

Current protocols
|May 29, 2024
PubMed
概括
此摘要是机器生成的。

本指南为研究人员和学生提供一步一步的RNA测序 (RNA-seq) 数据分析工作流. 它涵盖从原始读取到功能洞察的基因表达分析,适用于任何生物体.

关键词:
在RNA-seqq.组装的组装组装的组装.不同的表达方式,不同的表达方式.非模范植物的非模范植物策划 策划 策划 策划可复制性的可复制性

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Last Updated: Jun 25, 2025

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

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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.

背景情况:

  • RNA测序 (RNA-seq) 是全基因组基因表达分析的重要工具.
  • 对那些没有生物信息学专业知识的人来说,分析大型RNA-seq数据集带来了重大挑战.

研究的目的:

  • 为RNA-seq数据分析提供一个全面的,逐步指南.
  • 帮助学生和研究人员分析来自任何有机体的基因表达数据,有或没有参考基因组.

主要方法:

  • 该指南详细介绍了从原始读取处理到功能丰富分析的工作流程.
  • 它使用了公认的生物信息学工具,讨论了它们的利弊.
  • 包括对具有参考基因组的模型生物的协议和对非模型生物的 de novo 组装.

主要成果:

  • 介绍了一种用于RNA-seq数据分析的实用和可重复的协议.
  • 该指南有助于从复杂的数据集中提取有价值的生物见解.
  • 所有必要的脚本和样本数据集都通过GitHub提供.

结论:

  • 本指南使用户能够自信地进行RNA-seq数据分析.
  • 它民主化了对先进的基因表达分析技术的访问.
  • 通过可访问的生物信息工作流来增强生物发现.