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

Non-LTR Retrotransposons03:18

Non-LTR Retrotransposons

11.4K
As the name suggests, non-LTR retrotransposons lack the long terminal repeats characteristic of the LTR retrotransposons. Additionally, both LTR and non-LTR retrotransposons use distinct mechanisms of mobilization. Non-LTR retrotransposons are further divided into two classes - Long interspersed nuclear elements (LINEs) and short interspersed nuclear elements (SINEs), both of which occur abundantly in most mammals, including humans. Some of the active non-LTR retrotransposons in humans are L1...
11.4K

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

ProLM: a plasma proteomics pretrained model for the general population.

Nature communications·2026
Same author

Phagocytic aberrations in macrophages in asthma: a mechanistic systematic review integrating <i>in vitro</i>, animal, and human evidence.

Frontiers in immunology·2026
Same author

TaR3H interacts with Pm21 and confers powdery mildew resistance.

Journal of experimental botany·2026
Same author

Epigenetics and macrophage polarization in asthmatic airway inflammation.

Translational research : the journal of laboratory and clinical medicine·2026
Same author

Effects of aromatic salts on the phase behavior and viscoelastic properties of a cationic gemini surfactant in aqueous solutions.

RSC advances·2026
Same author

MSTO1 modulates RAD51 activity to safeguard mitochondrial DNA integrity and control immune responses.

Cancer gene therapy·2026

相关实验视频

Updated: Jun 13, 2025

Detection of Retrotransposition Activity of Hot LINE-1s by Long-Distance Inverse PCR
10:54

Detection of Retrotransposition Activity of Hot LINE-1s by Long-Distance Inverse PCR

Published on: July 27, 2019

8.6K

MEHunter:基于变压器的移动元件变体检测从长时间读取.

Tao Jiang1,2, Zuji Zhou1, Zhendong Zhang1

  • 1Center for Bioinformatics, Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.

Bioinformatics (Oxford, England)
|September 17, 2024
PubMed
概括

MEHunter使用变压器模型准确检测移动遗传元素变体 (MEV),改善遗传疾病研究. 该工具识别了现有方法遗漏的新型MEV,增强了基因组分析.

更多相关视频

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

15.2K
Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Published on: August 21, 2016

12.9K

相关实验视频

Last Updated: Jun 13, 2025

Detection of Retrotransposition Activity of Hot LINE-1s by Long-Distance Inverse PCR
10:54

Detection of Retrotransposition Activity of Hot LINE-1s by Long-Distance Inverse PCR

Published on: July 27, 2019

8.6K
Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

15.2K
Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Published on: August 21, 2016

12.9K

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:

  • 移动遗传元件 (MEs) 是导致遗传疾病的重要变异原体.
  • 长读测序技术为全面检测ME变种 (MEV) 提供了潜在的潜力.
  • 精确的MEV检测是具有挑战性的,因为长度可变和杂的长读数据.

研究的目的:

  • 开发一种高性能方法来检测移动遗传元素变体 (MEV).
  • 使用先进的计算方法提高MEV检测的准确性和灵敏度.

主要方法:

  • 提出了MEHunter,一种使用微调变压器模型的新方法.
  • 该模型旨在通过在长时间阅读中识别碎片化的特征来识别MEV.
  • 在模拟和现实世界数据集上评估MEHunter.

主要成果:

  • 与最先进的工具相比,MEHunter表现出卓越的精度和灵敏度.
  • 这种方法有效地应对了杂的长时间读取序列数据所带来的挑战.
  • 确定了以前在人口研究中被忽视的新型,潜在的个体特异性MEV.

结论:

  • MEHunter提供了一个强大的解决方案,用于精确而敏感的MEV检测.
  • 该工具推进了基因组学中移动遗传元素的全面分析.
  • 有助于发现与遗传疾病和个体变异相关的新MEV.