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Ribosome Profiling

Ribosome Profiling

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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Updated: May 28, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

STMiner:以基因为中心的空间转录学,用于破译瘤组织.

Peisen Sun¹, Stephen J Bush¹, Songbo Wang¹

¹School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China; MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China.

|February 13, 2025

概括

此摘要是机器生成的。

STMiner通过专注于基因表达模式而不是细胞位置来分析空间转录组学数据. 这种方法克服了瘤样本中的偏见,揭示了隐藏的生物学见解和空间结构.

关键词:

高斯混合物模型模型的高斯混合物模型.生物信息学是一种生物信息学.基因中心的基因中心.机器学习是机器学习.最优运输理论最优运输理论这就是 scRNA-seqq.空间转录学空间转录学空间变量的基因瘤是一个瘤.

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Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

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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.
生物信息学是一种生物信息学.

背景情况:

对瘤的空间转录学分析面临着诸如边界不清晰,细胞密度不均和高异质性等挑战.
这些因素导致背景识别偏差,导致错误识别空间结构并阻碍病理洞察力.

研究的目的:

开发一种新的计算方法,STMiner,用于精确分析复杂组织中的空间转录学数据.
克服现有的基于现场的方法在识别空间变量的基因和结构的局限性.

主要方法:

STMiner利用二维高斯混合模型和最佳运输理论直接描述空间基因分布.
该方法分析了整体的基因表达模式,减轻了背景偏差和数据稀疏性.

主要成果:

STMiner有效地识别了关键的基因组和空间结构,而传统的基于点的工具错过了它们.
该方法增强了从空间转录组学数据中发现新的生物学见解的发现.

结论:

STMiner为分析空间转录学提供了一个强大的框架,改善了在具有挑战性的瘤微环境中识别生物模式.
它的核心方法提供了在不断发展的空间奥米克技术中更广泛应用的潜力.