Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

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

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Chemical Proteomics Reveals a Novel Long-Acting Maleimide Algaecide Targeting Glyceraldehyde-3-Phosphate Dehydrogenase for Cyanobacterial Bloom Control.

Journal of agricultural and food chemistry·2026
Same author

Yap mediates hippo signaling to balance proliferation and differentiation in the developing glandular stomach epithelium.

Cell reports·2026
Same author

DeepDOX1: A Dual-Drive Framework Integrating Deep Learning and First-Principles Quantum Chemistry for Drug-Protein Affinity Prediction.

JACS Au·2026
Same author

GCAS: An Integrated R Package and Shiny App for Comprehensive Cancer Data Analysis.

Biomolecules·2026
Same author

Stiffness-Activated Stellate Cells Drive Pancreatic Cancer Liver Colonization via GMFG-TNS4 Signaling.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

From traditional medicine to advanced sensing: A natural Fraxin-based supramolecular probe for visual pH detection and in vivo gastrointestinal imaging.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2026

相关实验视频

Updated: Jun 27, 2025

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms
05:12

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Published on: February 2, 2024

731

scCRT:一种基于对比的缩小维度模型,用于scRNA-seq轨迹推断.

Yuchen Shi1, Jian Wan2, Xin Zhang1

  • 1Hangzhou Dianzi University, Hangzhou City, Zhejiang Province, China.

Briefings in bioinformatics
|May 3, 2024
PubMed
概括

scCRT通过整合先前的细胞信息来改善单细胞RNA测序轨迹推断,以更好地减少维度. 这种新的方法提高了细胞谱系推断在发育生物学研究中的准确性.

关键词:
相反的学习学习学习.代表性学习学习学习单细胞RNA的测序.轨迹推断的推断是指轨迹的推断.

更多相关视频

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data
07:35

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Published on: December 1, 2023

651
Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
08:35

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

5.6K

相关实验视频

Last Updated: Jun 27, 2025

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms
05:12

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Published on: February 2, 2024

731
Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data
07:35

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Published on: December 1, 2023

651
Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
08:35

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

5.6K

科学领域:

  • 基因组学就是基因组学.
  • 计算生物学 计算生物学
  • 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) 对于理解细胞分化和发育动态至关重要.
  • 轨迹推断方法对于分析scRNA-seq数据至关重要,但通常受到传统的缩小维度技术的限制.
  • 现有的方法无法充分利用先前的信息,影响细胞谱系重建的准确性.

研究的目的:

  • 引入scCRT,一种新型的维度减小模型,专门用于scRNA-seq数据中的轨迹推理.
  • 利用先前的细胞状态信息来提高缩小维空间中的细胞表示的准确性.
  • 通过整合细胞层面和集群层面的特征学习来提高轨迹推断的性能.

主要方法:

  • scCRT集成了一个细胞层次的配对模块,以保持细胞-细胞关系在一个缩小尺寸空间.
  • 一个集群级对比模块利用先前的细胞状态信息来聚合相似的细胞,防止低维分散.
  • 该模型通过结合这两个特征学习组件来学习精确的细胞表示.

主要成果:

  • 与现有的轨迹推断方法相比,scCRT在54个真实数据集和81个合成数据集中表现出更高的性能.
  • 一项废除研究证实,细胞层面和集群层面的模块都对学习精确的细胞特征作出了重大贡献.
  • 增强的细胞特征促进了更精确的细胞系推断.

结论:

  • 通过有效地整合先前的生物信息,scCRT在scRNA-seq轨迹推断方面取得了重大进展.
  • 该模型能够学习精确的细胞表征,提高了动态生物过程的重建,如细胞分化.
  • scCRT为研究细胞发育和谱系追踪的研究人员提供了一个强大的新工具.