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相关概念视频

Ribosome Profiling02:24

Ribosome Profiling

3.5K
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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Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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DNA Microarrays02:34

DNA Microarrays

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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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Reporter Genes02:11

Reporter Genes

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Reporter genes are a type of protein-coding gene that are often tagged to a gene of interest. Once inside a target cell, reporter genes usually produce visually identifiable characteristics like fluorescence and luminescence when expressed along with the gene of interest. Thus, reporter genes “report” the presence or absence of genes of interest in an organism, determine the gene expression pattern, or track the physical location of a DNA segment or protein in the cell.
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相关实验视频

Updated: Jun 21, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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HEARTSVG:一种快速而准确的方法,用于在大型空间转录学中识别空间变量基因.

Xin Yuan1,2, Yanran Ma1, Ruitian Gao1

  • 1Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.

Nature communications
|July 7, 2024
PubMed
概括

HEARTSVG是一种在空间转录学中寻找空间变量基因 (SVGs) 的新方法. 它快速,准确,并且比现有方法识别出更多生物学意义上的SVGs,揭示了瘤的复杂性.

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科学领域:

  • 空间转录组学 空间转录组学
  • 基因组学就是基因组学.
  • 计算生物学是一种计算生物学.

背景情况:

  • 识别空间变量基因 (SVGs) 对于理解组织结构和疾病进展至关重要.
  • 当前的方法面临的挑战是准确性,速度和可扩展性,大规模的空间转录数据.

研究的目的:

  • 引入HEARTSVG,一种新的,无分发的,基于测试的方法,用于有效和准确地识别SVG.
  • 通过模拟和真实世界的数据集来评估HEARTSVG的性能与最先进的方法对比.

主要方法:

  • HEARTSVG采用一种无分发的,基于测试的方法来识别SVG.
  • 通过对12个不同的空间转录组数据集进行广泛的模拟和分析来评估性能.
  • 已识别的SVG的聚类被用于探索瘤数据中的空间域.

主要成果:

  • HEARTSVG在模拟中表现出卓越的性能,获得高平均"分数" (0.948) 并减少错误阳性.
  • 对真实数据集的分析表明,HEARTSVG与现有方法相比,发现了更多具有生物学意义的SVG (平均AUC=0.792).
  • 该方法在结直肠癌数据中成功发现了具有独特表达模式和功能的独特瘤空间域.

结论:

  • HEARTSVG提供了一种计算效率高且可扩展的解决方案,用于在大型空间转录组数据集中识别SVG.
  • 该方法增强了生物学相关的空间基因表达模式的发现,有助于理解像瘤这样的复杂生物系统.