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

Genetic Lingo01:11

Genetic Lingo

113.6K
Overview
113.6K
Genomics02:02

Genomics

39.6K
Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
39.6K
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

20.5K
The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
20.5K
Improving Translational Accuracy02:07

Improving Translational Accuracy

3.5K
3.5K
Improving Translational Accuracy02:07

Improving Translational Accuracy

14.0K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
14.0K
Gene Families01:57

Gene Families

3.5K
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

DNA hypomethylation enables the transcriptional repressor SlSPL-CNR to control fruit flavor ester biosynthesis.

Nature communications·2026
Same author

Towards the construction of a virtual yeast.

Nature·2026
Same author

Stachydrine alleviates thoracic aortic aneurysm via regulating lactate dehydrogenase A in Marfan syndrome mice.

Phytomedicine : international journal of phytotherapy and phytopharmacology·2026
Same author

Long-term metabolically unhealthy aging, its underlying molecular underpinnings, and association with cognitive impairment: a 12.4-year longitudinal cohort study.

BMC medicine·2026
Same author

Perivascular adipokine signaling in abdominal aortic aneurysm: cardiometabolic drivers of vascular remodeling and translational opportunities.

Cardiovascular diabetology·2026
Same author

Trans-ethnic estimation and implications of genetic impact on continuous glycemic profiles.

Cell discovery·2026
Same journal

Literature-informed gene extraction and ranking for multimodal data fusion.

Briefings in bioinformatics·2026
Same journal

SA-MTP: a structure-aware framework for multifunctional therapeutic peptide annotation.

Briefings in bioinformatics·2026
Same journal

Genome assemblies and annotations are not static and need support for tracking their evolution.

Briefings in bioinformatics·2026
Same journal

A historical journey of metabolite-protein interaction discovery: from data harmonization to AI-driven prediction.

Briefings in bioinformatics·2026
Same journal

Bridging local-global transmembrane protein contexts with contrastive pretraining for alignment-free pathogenicity prediction.

Briefings in bioinformatics·2026
Same journal

Prediction of drug hypersensitivity by comprehensive modeling of HLA-peptidomes.

Briefings in bioinformatics·2026
查看所有相关文章

相关实验视频

Updated: Jan 11, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.4K

FGeneBERT:功能驱动的预训练基因语言模型用于元基因组学.

Chenrui Duan1,2, Zelin Zang3, Yongjie Xu1,2

  • 1College of Computer Science and Technology, Zhejiang University, No. 866, Yuhangtang Road, 310058 Zhejiang, P. R. China.

Briefings in bioinformatics
|November 10, 2025
PubMed
概括
此摘要是机器生成的。

FGeneBERT是一种新的元基因组模型,使用基于蛋白质的基因上下文来改善对基因关系和功能的理解. 这种方法在复杂的生物数据中增强了基因,功能,细菌和环境层面的分析.

关键词:
它们是DNA DNA DNA DNA.转基因组学是指转基因组学.预先训练的语言模型语言模型.变压器的变压器是一个变压器.

更多相关视频

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.2K
A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

18.3K

相关实验视频

Last Updated: Jan 11, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.4K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.2K
A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

18.3K

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:

  • 超基因组数据对于了解多样化的环境和人类健康至关重要,但由于混合基因组,这也带来了挑战.
  • 目前基于K-mer的方法限制了捕捉结构和功能基因背景,并与复杂的基因关系作斗争.
  • 现有的方法无法有效地编码具有生物意义的基因,并解决元基因组数据中固有的一对多/多对一关系.

研究的目的:

  • 介绍FGeneBERT,这是一种新的预训练模型,旨在克服当前元基因组数据分析的局限性.
  • 增强对基因间上下文关系和基因序列功能关联的理解.
  • 改进复杂的元基因组数据集中的生物意义基因的表示和分析.

主要方法:

  • 开发了FGeneBERT,这是一种利用基于蛋白质的基因表征作为标记器的元基因学预训练模型.
  • 实施了掩盖基因建模,以提高对基因间情境关系的理解.
  • 采用三重增强的元基因学对比学习来阐明基因序列功能关系.

主要成果:

  • 在基因,功能,细菌和环境层面上,FGeneBERT在超基因组数据集中表现出卓越的性能.
  • 该模型有效地处理不同的输入序列大小,从1k到213k.
  • 关于ATP合成酶和基因操作子的案例研究展示了FGeneBERT在功能识别和生物相关性方面的准确性.

结论:

  • FGeneBERT通过提供上下文感知和结构相关的基因表示,在元基因组数据分析方面取得了重大进展.
  • 基于蛋白质的方法和高级学习策略增强了元基因组发现的生物解释性.
  • FGeneBERT的能力对于未来的环境和人类健康相关的元基因组学研究至关重要.