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相关概念视频

Regulated mRNA Transport02:22

Regulated mRNA Transport

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In eukaryotes, transcription and translation are compartmentalized; an mRNA is first synthesized in the nucleus and then selectively transported to the cytoplasm for protein synthesis. Before transport, a pre-mRNA undergoes several steps of post-transcriptional modifications including splicing, 5' capping, and the addition of a poly-adenine tail. Various proteins bind to the pre-mRNA during these modifications. The mRNA transport takes place with the help of multiple proteins playing...
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Proteins targeted to the nucleus carry short stretches of amino acid sequences called the nuclear localization signal or NLS. Classical nuclear localization signals are of two types: monopartite and bipartite NLS. Monopartite classical NLS (cNLS) consists of a single cluster of 4-8 amino acids. Bipartite cNLS consists of two clusters of  2-3 amino acids and a 9-12 residue long proline-rich linker bridging the two clusters. Signal clusters are rich in positively charged amino acids such as...
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相关实验视频

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Visualization of Endoplasmic Reticulum Localized mRNAs in Mammalian Cells
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MulStack:一个集体学习预测模型的多标签mRNA亚细胞局部化.

Ziqi Liu1, Tao Bai2, Bin Liu3

  • 1School of Computer Science and Technology, Xidian University, Xian, 710075, China.

Computers in biology and medicine
|April 30, 2024
PubMed
概括

这项研究介绍了MulStack,这是一种用于预测信使RNA (mRNA) 亚细胞定位的新型计算模型. 通过整合序列和残留级别特征与新型位置编码来增强生物洞察力,MulStack提高了准确性.

关键词:
深度学习是一种深度学习.集体学习预测器多标签mRNA细胞下局部化位置编码 位置编码在两个层次的mRNA特征.

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Method for Labeling Transcripts in Individual Escherichia coli Cells for Single-molecule Fluorescence In Situ Hybridization Experiments
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相关实验视频

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科学领域:

  • 分子生物学分子生物学
  • 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:

  • 传递 RNA (mRNA) 的亚细胞局部化对于调节蛋白质合成,细胞极性和细胞运动至关重要.
  • 对于mRNA本地化预测的现有计算方法通常使用有限的特征集,并且不包含核酸位置信息.
  • 大多数mRNA分布在多个亚细胞位置,需要多标签预测方法.

研究的目的:

  • 开发一种先进的计算模型,用于预测多标签mRNA亚细胞定位.
  • 将包括核酸的位置编码在内的新功能纳入mRNA本地化预测中.
  • 提高mRNA亚细胞局部化预测的准确性和生物相关性.

主要方法:

  • 提出了MulStack,一个整体学习模型,将随机森林和深度学习结合起来,用于多标签mRNA亚细胞定位.
  • 利用了两个级别的mRNA特征:序列级和残留级.
  • 在该领域首次引入了mRNA序列内核酸位置的位置编码.
  • 使用卷积神经网络 (CNN) 来提取可能与RNA结合蛋白质基因相关的位置重量矩阵 (PWM).

主要成果:

  • 与现有方法相比,MulStack表现出优越的性能,特别是用于预测核,细胞质和外体的局部化.
  • 该模型成功地集成了序列级和残留级特征与位置编码.
  • 使用CNN提取的PWM显示出识别RNA结合蛋白相互作用的潜力.
  • 基因本体学 (GO) 丰富分析提供了对局部mRNAs生物作用的见解.

结论:

  • 穆尔斯塔克在预测多标签mRNA亚细胞局部化方面取得了重大进展.
  • 整合位置编码代表了这一预测任务的新有效策略.
  • 该模型能够识别潜在的RNA结合蛋白质基因,并提供GO丰富分析,从而提高其生物解释性和实用性.