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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.
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A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is comprised  of nucleotides and proteins are comprised of amino acids, a mediator is required to convert the information encoded in DNA into proteins. This mediator is the messenger RNA (mRNA). mRNA copies the blueprint from DNA by a process called transcription. In eukaryotes, transcription occurs in the nucleus by complementary base-pairing with the DNA template. The mRNA is then...
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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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走向对转录异形表达水平的普遍建模.

Savio Ho-Chit Chow1, Christina Huan Shi1, Aniruddha Deshpande1,2

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此摘要是机器生成的。

计算生物学家现在可以使用表观遗传数据来模型转录异形表达. 一个通用模型,在许多人体组织中训练,准确地预测基因表达水平,推进基因调节的研究.

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科学领域:

  • 计算生物学 计算生物学
  • 基因组学就是基因组学.
  • 表观遗传学 在表观遗传学中,表观遗传学是指表观遗传学.

背景情况:

  • 使用表观遗传特征准确建模转录表达对于理解基因调节至关重要.
  • 之前的研究受限于细胞系模型和单基因表达水平,未能捕捉同型多样性.
  • 国际人类表观基因组联盟 (IHEC) 提供大规模的配对转录基因组和表观基因组数据.

研究的目的:

  • 在人类样本中以计算方式建模个人转录异形表达水平.
  • 开发一种"通用"模型,用于预测跨多种组织类型的转录异形表达.
  • 利用基于图形的方法,整合表观遗传特征和基因-基因关系.

主要方法:

  • 利用来自IHEC的29种组织类型的324个人体样本的大规模数据集.
  • 采用基于图形的计算模型,整合特定位置的表观基因组数据.
  • 将多种类型的基因-基因关系纳入建模过程.

主要成果:

  • 在各种人体组织中成功模拟了个体转录异型的表达水平.
  • 证明了在多种组织类型的数据上训练的模型优于特定组织的模型.
  • 发现了强有力的证据,支持"通用"模型的可行性,用于转录异形表达式推断.

结论:

  • 一个通用计算模型可以准确地推断跨人体组织的转录异形表达水平.
  • 整合多样化的表观基因和转录基因数据可以提高基因表达模型的预测能力.
  • 这种方法促进了基因调节在正常状态和疾病状态的定量研究.