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相关概念视频

Ribosome Profiling02:24

Ribosome Profiling

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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
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Proteomics01:33

Proteomics

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
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Protein Networks02:26

Protein Networks

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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,...
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Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

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The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
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Protein-protein Interfaces02:04

Protein-protein Interfaces

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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...
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General Transcription Factors01:30

General Transcription Factors

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Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
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相关实验视频

Updated: Jun 5, 2025

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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CrossIsoFun:通过整合多omics数据来预测异形函数.

Yiwei Liu1,2, Hong-Dong Li1,2, Jianxin Wang1,2

  • 1School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P.R. China.

Bioinformatics (Oxford, England)
|December 16, 2024
PubMed
概括
此摘要是机器生成的。

由于数据集成的局限性,在计算上对基因异形函数进行注释是具有挑战性的. CrossIsoFun利用多omics数据和异形-异形相互作用来改进异形函数预测,优于现有方法.

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Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
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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:

  • 由替代拼接产生的基因异型可以具有不同的生物功能.
  • 了解异形水平的功能对于破译基因组功能多样性至关重要.
  • 当前的计算方法在整合异构的多omics数据以进行异形函数预测方面存在困难,特别是低利用异形-异形相互作用 (IIIs).

研究的目的:

  • 开发一个先进的计算框架,用于准确的异形函数预测.
  • 增强多omics数据集成,以改善异型级别的功能注释.
  • 在预测模型中有效地纳入异形-异形相互作用 (IIIs).

主要方法:

  • 介绍CrossIsoFun,一个多omics数据分析框架.
  • 使用图形卷积网络 (GCNs) 进行欧米特异性分类.
  • 采用视图相关性发现网络 (VCDN) 进行跨omics集成.
  • 通过使用循环一致性生成对抗网络 (cycleGAN) 来从蛋白质-蛋白质相互作用 (PPI) 中生成合成III,以丰富相互作用学数据.

主要成果:

  • 与最先进的方法相比,CrossIsoFun在多个数据集上表现出卓越的性能.
  • 该框架有效地整合了mRNA表达,序列和PPI数据.
  • 由CrossIsoFun生成的预测显示与亚细胞局部化和文献支持的异形注释一致.

结论:

  • CrossIsoFun提供了一种强大而有效的方法,通过整合各种omics数据和IIIs来进行异形函数预测.
  • 该框架解决了当前多学科整合方法的局限性.
  • 这项工作推进了异形函数的计算预测,为基因组功能多样性提供了宝贵的见解.