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相关概念视频

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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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
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Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
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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 genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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基因组语言模型预测了蛋白质共同调节和功能.

Yunha Hwang1, Andre L Cornman2, Elizabeth H Kellogg3,4

  • 1Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA. yhwang@g.harvard.edu.

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一个新的基因组语言模型 (gLM) 使用对基因组数据的深度学习来理解基因功能和调节关系. 这种方法有效地解读了复杂的基因相互作用在其基因组上下文.

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科学领域:

  • 基因组学就是基因组学.
  • 机器学习 机器学习
  • 生物信息学是一种生物信息学.

背景情况:

  • 了解基因-基因组上下文关系对于生物系统工程至关重要.
  • 机器学习已经进行了先进的序列结构功能分析,但往往忽视了基因组背景.
  • 在基因组环境中的进化模式可以揭示功能性基因产物关系.

研究的目的:

  • 扩展机器学习的能力,以纳入更高阶的基因组上下文信息.
  • 开发一种基因组语言模型 (gLM),用于学习潜在的功能性和调节性基因关系.
  • 从蛋白质序列及其基因组环境中编码具有生物意义的信息.

主要方法:

  • 在数以百万计的元基因组支架上训练了一个基因组语言模型 (gLM).
  • gLM学习了上下文化的蛋白质嵌入,整合了序列和基因组上下文.
  • 分析了注意力模式,以确定学习的共同调节的功能模块 (操作子).

主要成果:

  • gLM成功地编码了生物相关信息,包括酶功能和分类学.
  • 注意力分析证实了gLM能够学习像操作子这样的共同调节的基因模块.
  • 在超基因组数据上展示的无监督深度学习有效地捕捉了基因语义和调控语法.

结论:

  • 在基因组背景下,gLM是编码功能性和调节性基因信息的有希望的方法.
  • 该模型有效地揭示了基因组区域中基因之间的复杂关系.
  • 这种深度学习策略促进了对基因相互作用和生物系统工程的理解.