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相关概念视频

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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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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RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Improving Translational Accuracy02:07

Improving Translational Accuracy

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相关实验视频

Updated: Jun 7, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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空间转录组学为轨迹推理带来了新的挑战和机会.

Matthieu Heitz1, Yujia Ma1, Sharvaj Kubal1

  • 1Department of Mathematics, University of British Columbia, Vancouver, British Columbia, Canada;

Annual review of biomedical data science
|November 14, 2024
PubMed
概括

空间转录学 (ST) 分析在轨迹推断 (TI) 中面临挑战,原因是空间批量效应和测量限制. 本综述考察了ST轨迹推断的当前方法和未来方向.

科学领域:

  • 计算生物学 计算生物学
  • 基因组学就是基因组学.
  • 生物信息学是一种生物信息学.

背景情况:

  • 空间转录学 (ST) 将空间信息与单细胞基因表达数据集成.
  • 传统的单细胞分析方法通常需要对ST数据进行调整.
  • 轨迹推断 (TI) 方法特别容易受到ST数据分析方面的挑战.

研究的目的:

  • 审查在将轨迹推理应用于空间转录学数据时遇到的挑战.
  • 检查空间转录学轨迹推断 (STTI) 的当前最先进的方法.
  • 确定未来在STTI中开发方法的机会.

主要方法:

  • 对现有关于空间转录学和轨迹推断的文献进行审查.
  • 分析挑战,包括空间批量效应,组织变形,测量颗粒度和切片偏差.
  • 探索当前的STTI方法来应对这些挑战.

主要成果:

  • 空间批量效应为TI的ST数据带来了显著的噪音和复杂性.
  • 现有的STTI方法主要集中在物理安排批量效应上.
  • 测量颗粒度和切片偏差带来了额外的,往往没有解决的挑战.

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结论:

  • 解决各种噪音和偏差来源对于准确的STTI至关重要.
  • 需要进一步的方法开发,以克服目前在STTI的局限性.
  • 未来的研究应该专注于空间转录学轨迹推断的强有力的方法.