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相关概念视频

Genome Annotation and Assembly03:36

Genome Annotation and Assembly

19.3K
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.
19.3K
Tagging and Fusion Proteins01:24

Tagging and Fusion Proteins

6.9K
Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
6.9K
Protein Networks02:26

Protein Networks

4.1K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.1K
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

11.4K
Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
11.4K
Protein-protein Interfaces02:04

Protein-protein Interfaces

13.3K
Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
13.3K
Protein Families02:47

Protein Families

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相关实验视频

Updated: Sep 13, 2025

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes
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A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

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使用DeepGO蛋白质功能预测工具对基因组进行注释.

Rund Tawfiq1,2,3, Kexin Niu1,2,3, Maxat Kulmanov4,2,3

  • 1Biological and Environmental Sciences & Engineering (BESE) Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.

Methods in molecular biology (Clifton, N.J.)
|July 29, 2025
PubMed
概括
此摘要是机器生成的。

深度学习工具DeepGO增强了基因组注释的蛋白质功能预测. 它的最新版本DeepGO-SE在细菌基因组案例研究中表现出高精度,有助于基因组分析.

关键词:
深度学习是一种深度学习.在深度深度的深度.基因本体学 基因本体学基因组是基因组的组成部分.蛋白质的功能 蛋白质的功能

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A Protocol for Computer-Based Protein Structure and Function Prediction

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相关实验视频

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科学领域:

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

背景情况:

  • 蛋白质功能预测对于理解生物系统和基因组注释至关重要.
  • 传统方法在准确性和可扩展性方面面临挑战.
  • 深度学习提供了一种有希望的方法来解决这些局限性.

研究的目的:

  • 探索DeepGO的演变和应用,这是一个用于蛋白质功能预测的深度学习套件.
  • 提供DeepGO在基因组注释方面的进展和实际实用性的概述.
  • 引导研究人员利用深度学习进行增强的基因组分析.

主要方法:

  • 审查DeepGO版本及其关键的进步.
  • 应用最新的DeepGO模型,DeepGO-SE,用于蛋白质功能预测.
  • 使用DeepGO-SE.进行细菌基因组注释的案例研究.

主要成果:

  • DeepGO已经发生了显著的进化,每个版本都引入了关键的改进.
  • 在预测蛋白质功能方面,DeepGO-SE表现出高效率和准确性.
  • 该案例研究证实了DeepGO-SE在基因组注释中的实际实用性.

结论:

  • 像DeepGO这样的深度学习工具对于蛋白质功能预测非常强大.
  • DeepGO-SE代表了该领域的重大进步,改善了基因组注释的准确性.
  • 这项工作为利用基因组学深度学习的研究人员提供了宝贵的资源.