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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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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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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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对预测 lncRNA 亚细胞局部化的机器学习模型的评估.

Jason R Miller1,2, Weijun Yi2, Donald A Adjeroh2

  • 1Department of Computer Science and Information Technology; Hood College, Frederick, MD 21701, USA.

NAR genomics and bioinformatics
|September 19, 2024
PubMed
概括
此摘要是机器生成的。

以前的研究过度估计了长非编码RNA (lncRNA) 细胞下局部化预测的准确性. 我们的研究结果显示,当前的模型在未过的数据上表现不佳,这表明任务比以前认为的要复杂得多.

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科学领域:

  • 生物信息学是一种生物信息学.
  • 分子生物学分子生物学
  • 计算生物学 计算生物学

背景情况:

  • 长非编码RNAs (lncRNAs) 在细胞过程中起着至关重要的作用.
  • 准确预测 lncRNA 亚细胞定位对于理解它们的功能至关重要.
  • 现有的机器学习模型在lncRNA本地化预测方面表现中等.

研究的目的:

  • 重新评估机器学习模型对于 lncRNA 亚细胞定位的性能.
  • 调查数据过方法对模型性能的影响.
  • 为未来的 lncRNA 局部化预测研究建立一个基准.

主要方法:

  • 利用了IncATLAS数据库,其中包含来自15个人类细胞系的IncRNA丰度数据.
  • 应用各种机器学习模型来预测 lncRNA 亚细胞定位.
  • 在过 ("中间排除") 与未过的数据集上比较模型性能.

主要成果:

  • "中间排除"数据过方法膨胀了性能指标,而没有改善真正的模型性能.
  • 在未过的lncRNA数据上评估的模型只能达到60%左右的准确性.
  • 由于数据过偏差,以前的报告可能高估了模型性能.

结论:

  • 从核酸序列预测lncRNA亚细胞定位比以前认为的更具挑战性.
  • "中间排斥"协议是误导性的,在未来的评估中应该避免使用它.
  • 为了在这个领域取得可靠的进展,需要一个标准化的基准模型和评估程序.