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

From DNA to Protein03:06

From DNA to Protein

18.0K
The flow of genetic information in cells from DNA to mRNA to protein is described by the central dogma, which states that genes specify the sequence of mRNAs, which in turn specify the sequence of amino acids making up all proteins. The decoding of one molecule to another is performed by specific proteins and RNAs. Because the information stored in DNA is so central to cellular function, it makes intuitive sense that the cell would make mRNA copies of this information for protein synthesis...
18.0K
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
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

5.7K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
5.7K
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

18.8K
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.
18.8K
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

7.0K
The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
7.0K
The Central Dogma01:25

The Central Dogma

123.4K
Overview
123.4K

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

Design of bacterial DNT sensors based on computational models.

Nucleic acids research·2026
Same author

Designing genetically stable multicopy gene constructs with the ChimeraUGEM web server.

NAR genomics and bioinformatics·2025
Same author

Yeast-derived low-purity FGF2 supports bovine ESC and MSC aggregates in suspension.

Frontiers in nutrition·2025
Same author

Silent mutations in coding regions of Hepatitis C virus affect patterns of HCV RNA structures and attenuate viral replication and pathogenesis.

Genome biology·2025
Same author

AI-directed gene fusing prolongs the evolutionary half-life of synthetic gene circuits.

Science advances·2025
Same author

From Binding to Catalysis: Emergence of a Rudimentary Enzyme Conferring Intrinsic Antibiotic Resistance.

Molecular biology and evolution·2025
Same journal

Chemotactic self-organization captures the dynamics of mammalian hair follicle patterning.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Tomographic imaging of superconducting order using particle-hole interference.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Inhibitory potential of autologous neutralizing antibodies sets quantitative limits on the rebound-competent HIV-1 reservoir.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Inferring epidemiological parameters under an infectious phylogeography model with visitor dynamics.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Analytical modeling for suction cup designs for skin-interfaced wearable devices.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Improving cell-free metabolism through direct integration of artificial respiratory chains.

Proceedings of the National Academy of Sciences of the United States of America·2026
查看所有相关文章

相关实验视频

Updated: Jun 4, 2025

Identifying Amino Acid Overproducers Using Rare-Codon-Rich Markers
10:41

Identifying Amino Acid Overproducers Using Rare-Codon-Rich Markers

Published on: June 24, 2019

8.3K

使用人工智能预测基因序列,以研究代码子使用模式.

Tomer Sidi1, Shir Bahiri-Elitzur2, Tamir Tuller2,3

  • 1Department of Computer Science, University of Haifa, Haifa 3303221, Israel.

Proceedings of the National Academy of Sciences of the United States of America
|December 31, 2024
PubMed
概括
此摘要是机器生成的。

人工智能模型学习了细菌和真核生物中复杂的编码子使用模式,显著超过了基本方法. 这些发现推动了我们对进化选择的理解,并为优化蛋白质表达提供了工具.

关键词:
编码器AI模型编码器的预测模仿编码子的编码子

更多相关视频

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

15.8K
An Integrated Approach for Microprotein Identification and Sequence Analysis
09:37

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

3.3K

相关实验视频

Last Updated: Jun 4, 2025

Identifying Amino Acid Overproducers Using Rare-Codon-Rich Markers
10:41

Identifying Amino Acid Overproducers Using Rare-Codon-Rich Markers

Published on: June 24, 2019

8.3K
Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

15.8K
An Integrated Approach for Microprotein Identification and Sequence Analysis
09:37

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

3.3K

科学领域:

  • 计算生物学 计算生物学
  • 基因组学就是基因组学.
  • 生物信息学是一种生物信息学.

背景情况:

  • 选择性压力塑造了密码子的使用,优化了尚未完全理解的生物信号.
  • 的使用模式受到进化过程和基因表达水平的影响.

研究的目的:

  • 训练人工智能模型,根据各种生物体的氨基酸序列来预测密码子的使用情况.
  • 调查自然发生的编码子模式可以在多大程度上被学习并用于改进预测.
  • 探索预测准确性,基因表达,蛋白质长度和进化因素之间的关系.

主要方法:

  • 训练有素的人工智能 (AI) 模型对*Saccharomyces cerevisiae*,*Schizosaccharomyces pombe*,*Escherichia coli*和*Bacillus subtilis*的蛋白质序列进行了训练.
  • 对不同长度和表达水平的蛋白质单独数据集的评估模型预测.
  • 将人工智能模型的性能与原始频率基预测方法进行比较.

主要成果:

  • 人工智能模型显著优于基于频率的方法,表明进化选择的编码子使用中可以学习的依赖性.
  • 高表达基因的预测准确性更高,在细菌中比真核生物更高,支持选择压力和有效种群规模之间的联系.
  • 模型显示,S. cerevisiae和细菌中较长的蛋白质的准确性有所提高,这表明与共翻译折叠有联系;基因功能和保存也影响了性能.

结论:

  • 当代的人工智能方法可以有效地学习复杂的,进化选择的密码体使用模式.
  • 开发的基于深度学习的预测工具提供了对内源和异源蛋白质表达的编码子优化的洞察.
  • 这些发现支持了将子使用复杂性与选择性压力和有效种群规模联系起来的假设.