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相关概念视频

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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Ribosome Profiling02:24

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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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Synthetic Biology02:55

Synthetic Biology

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Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
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RNA interference (RNAi) is a cellular mechanism that inhibits gene expression by suppressing its transcription or activating the RNA degradation process. The mechanism was discovered by Andrew Fire and Craig Mello in 1998 in plants. Today, it is observed in almost all eukaryotes, including protozoa, flies, nematodes, insects, parasites, and mammals. This precise cellular mechanism of gene silencing has been developed into a technique that provides an efficient way to identify and determine the...
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相关实验视频

Updated: Jul 23, 2025

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms
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变形:用于模块化单细胞RNA-seq数据集成的统一计算框架.

Aziz Fouché1,2,3,4, Loïc Chadoutaud1,2,3, Olivier Delattre5

  • 1Institut Curie, PSL Research University, 75005 Paris, France.

NAR genomics and bioinformatics
|July 14, 2023
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概括
此摘要是机器生成的。

变形框架集成了来自不同来源的单细胞RNA测序 (scRNA-seq) 数据集. 这种灵活的工具简化了scRNA-seq数据分析,并使强大的细胞类型识别成为可能.

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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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相关实验视频

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Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project
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科学领域:

  • 计算生物学 计算生物学
  • 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:

  • 单细胞RNA测序 (scRNA-seq) 数据集成对于组合来自多个实验的数据集至关重要.
  • 有效的整合解决了批量效应,并改善了下游分析,如聚类和细胞类型推断.
  • 现有的scRNA-seq集成工具往往缺乏灵活性,阻碍了对特定研究需求的适应.

研究的目的:

  • 为灵活而强大的scRNA-seq数据集成引入变形框架.
  • 提供一个软件生态系统,允许用户设计定制的数据集成管道.
  • 为了证明Transmorphic在解决实际scRNA-seq数据挑战中的实用性.

主要方法:

  • 开发了Transmorphic框架,这是一个开源的Python包.
  • 使用Transmorphic框架的数据集成管道的工程.
  • 应用Transmorphic来解决联合数据集嵌入,基因空间集成和注释转移方面的挑战.

主要成果:

  • 变形允许创建可适应的scRNA-seq数据集成管道.
  • 该框架成功地将来自不同来源的数据集集集成到一个共同的表示中.
  • 在关节嵌入,基因空间集成和细胞周期阶段注释转移方面表现出有效性.

结论:

  • 变形为scRNA-seq数据集成挑战提供灵活而强大的解决方案.
  • 该框架的软件生态系统支持定制集成策略的开发.
  • 变形增强scRNA-seq数据分析,通过促进跨多种数据集的强有力的集成.