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相关概念视频

Conserved Binding Sites01:49

Conserved Binding Sites

4.2K
Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
4.2K
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

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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...
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Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...
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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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相关实验视频

Updated: Jun 25, 2025

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

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通过整合特定区域的特征来预测功能性UTR变体.

Guangyu Li1, Jiayu Wu1, Xiaoyue Wang1

  • 1State Key Laboratory of Common Mechanism Research for Major Diseases; Center for bioinformatics, National Infrastructures for Translational Medicine, Institute of Clinical Medicine and Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 1 Shuai Fu Yuan, Dongcheng District, Beijing 100005, China.

Briefings in bioinformatics
|May 24, 2024
PubMed
概括

预测信使核糖核酸 (mRNA) 未翻译区域 (UTR) 变体现在更加准确. 新的机器学习模型识别功能性UTR变体,改善疾病风险预测.

关键词:
在 UTR 中使用 UTR.功能变体的功能变体预测模型 预测模型

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科学领域:

  • 基因组学就是基因组学.
  • 分子生物学分子生物学
  • 生物信息学是一种生物信息学.

背景情况:

  • mRNA的未翻译区域 (UTR) 调节基因表达,其中的变异与人类疾病有关.
  • 对UTR变异效应的计算预测具有挑战性,目前的方法往往忽略了UTR特定特征.

研究的目的:

  • 开发准确的计算模型来预测UTR变体的功能影响.
  • 为了确定驱动功能性UTR变体的关键序列决定因素.

主要方法:

  • 使用综合变体数据集对50多个特定区域的UTR特征进行系统分析.
  • 开发机器学习分类模型,利用已识别的UTR特征.

主要成果:

  • 识别了序列组合特征 (例如,5'UTR中的C/G,3'UTR中的A/T),以区分功能和非功能变体.
  • 实现了高预测性能,AUC值为0.94的5'UTR和0.85的3'UTR.
  • 开发出超越现有的UTR变体预测方法的模型.

结论:

  • 开发的机器学习模型显著提高了功能性UTR变体的预测.
  • 这些模型为临床解释遗传变异和疾病风险评估提供了有价值的工具.