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

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...
10.0K
Leaky Scanning02:28

Leaky Scanning

5.1K
During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
5.1K
Next-generation Sequencing03:00

Next-generation Sequencing

89.0K
The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
89.0K

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

Challenges and Advances in Bioinformatics and Computational Biology.

Current issues in molecular biology·2026
Same author

tBN-CSDI: a time-varying blue noise-based diffusion model for time-series imputation.

Bioinformatics advances·2025
Same author

Reconstructing Dynamic Gene Regulatory Networks Using f-Divergence from Time-Series scRNA-Seq Data.

Current issues in molecular biology·2025
Same author

Bidirectional f-Divergence-Based Deep Generative Method for Imputing Missing Values in Time-Series Data.

Stats·2025
Same author

Anomaly Detection in High-Dimensional Time Series Data with Scaled Bregman Divergence.

Algorithms·2025
Same author

Multivariate Time Series Change-Point Detection with a Novel Pearson-like Scaled Bregman Divergence.

Stats·2024
Same journal

Invaders taking over-Mollusc faunal change in volcanic barrier lakes of the Albertine Rift biodiversity hotspot.

PloS one·2026
Same journal

AI-driven molecular diversification and ligand-based optimization of macitentan derivatives targeting VEGFR1 and endothelin signaling pathways.

PloS one·2026
Same journal

Performance patterns and records in the world aquatics masters championships: Where do the most frequently represented nations among the top-ten masters swimmers come from?

PloS one·2026
Same journal

Modeling diurnal Temperature-Rainfall relationships under multicollinearity using PLS-SEM: A case study of Ghana.

PloS one·2026
Same journal

Organizational culture, social capital, and emergency capacity in primary healthcare institutions: A cross-sectional structural equation modeling study comparing ordinary and older communities.

PloS one·2026
Same journal

Impact of kidney function on the metabolome in the general population.

PloS one·2026
查看所有相关文章

相关实验视频

Updated: Jul 11, 2025

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models
09:58

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Published on: December 9, 2016

13.8K

一种基于f-分歧的新型生成对抗归算方法,用于scRNA-seq数据分析.

Tong Si1, Zackary Hopkins2, John Yanev2

  • 1Department of Mathematics and Statistics, Saint Louis University, St. Louis, MO, United States of America.

PloS one
|November 10, 2023
PubMed
概括
此摘要是机器生成的。

我们介绍了sc-fGAIN,这是一个用于在单细胞RNA测序 (scRNA-seq) 数据中赋值缺失值的新方法. 这种方法克服了传统方法的局限性,为增强的细胞多样性分析和个性化疗法提供了强大而准确的归算.

更多相关视频

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

775
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

706

相关实验视频

Last Updated: Jul 11, 2025

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models
09:58

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Published on: December 9, 2016

13.8K
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

775
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

706

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:

  • 单细胞RNA测序 (scRNA-seq) 对于理解细胞多样性和开发个性化疗法至关重要.
  • 在scRNA-seq数据中缺失的值或掉落是一个重要的分析挑战.
  • 传统的归算方法通常依赖于限制性的分布假设,在高缺失率下表现不佳.

研究的目的:

  • 为scRNA-seq数据开发一种新的归算方法,解决现有方法的局限性.
  • 引入sc-fGAIN,一个基于f-分歧的生成对抗归算网络,用于处理缺失值.
  • 为了验证sc-fGAIN在scRNA-seq数据集中准确归因缺失数据方面的有效性.

主要方法:

  • 提出了sc-fGAIN,一个包含f-分歧函数 (交叉,KL,反向KL,Jensen-Shannon) 的生成对抗归算网络.
  • 数学证明 sc-fGAIN 在归算后保留了原始数据分布.
  • 使用真实scRNA-seq数据对传统方法进行sc-fGAIN性能评估.

主要成果:

  • 与传统的归算方法相比,sc-fGAIN显示了较小的平方根平均误差.
  • 该方法在不同的缺失数据速率中表现出稳定性.
  • sc-fGAIN有效地降低了归算变化,从而导致更可靠的下游分析.

结论:

  • sc-fGAIN提供了一种强大而灵活的解决方案,用于在scRNA-seq数据中赋值缺失的值.
  • f-divergence框架允许sc-fGAIN容纳各种数据类型,提高其通用性.
  • 这种方法提高了scRNA-seq数据分析的准确性和可靠性,用于生物发现和治疗开发.