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相关概念视频

RNA-seq03:21

RNA-seq

10.0K
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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lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

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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: Jul 5, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

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无监督的空间嵌入空间转录学的深度表示.

Hang Xu1, Huazhu Fu2, Yahui Long1

  • 1Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), Singapore, 138648, Singapore.

Genome medicine
|January 12, 2024
PubMed
概括
此摘要是机器生成的。

SEDR集成了基因表达和空间数据,以更好地进行组织分析. 这种方法改善了细胞通信映射和空间转录学研究中的数据解读.

关键词:
批量集成集成批量集成.基因归因是基因的归因.空间聚类 空间聚类空间转录组学 空间转录组学轨迹推断的推理方法变量图的自动编码器.

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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:

  • 空间转录学使得在组织背景下进行基因表达分析.
  • 了解组织异质性和细胞通信需要将基因表达与空间数据整合起来.
  • 现有的方法面临着高分辨率空间转录学在可扩展性和性能方面的挑战.

研究的目的:

  • 开发一个新的计算框架,SEDR,以实现转录学和空间数据的最佳集成.
  • 增强组织异质性的剖析和细胞间通信的映射.
  • 为了提高空间转录学数据分析的可扩展性和性能.

主要方法:

  • 对于基因表达表现,SEDR使用了带有掩盖自我监督学习的深度自编码器.
  • 一个变量图形自编码器嵌入了基因表达潜伏空间与空间信息.
  • 该方法在Visium数据集和高分辨率空间转录组学数据上进行了评估.

主要成果:

  • 与现有方法相比,SEDR在手动注释的数据集上展示了优越的集群性能.
  • 该框架展示了对高分辨率空间转录组数据集的增强可扩展性.
  • SEDR有效地归因和否定基因表达数据.

结论:

  • SEDR提供了一种有效的方法来整合空间和转录数据.
  • 该方法促进了组织异质性和细胞通信的分析.
  • 对于空间转录学应用,SEDR提供了更好的性能和可扩展性.