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相关概念视频

RNA-seq03:21

RNA-seq

9.8K
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...
9.8K
Protein Networks02:26

Protein Networks

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2.2K
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...
17.1K
Regulated mRNA Transport02:22

Regulated mRNA Transport

6.2K
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...
6.2K
Ribosome Profiling02:24

Ribosome Profiling

3.4K
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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相关实验视频

Updated: May 23, 2025

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

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顺:从被拒绝的空间转录学数据中有效推断空间协同表达网络.

Chase Holdener1,2, Iwijn De Vlaminck1

  • 1Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA.

bioRxiv : the preprint server for biology
|March 10, 2025
PubMed
概括

顺滑是一种新的方法,通过消除它并建立共同表达网络来分析空间基因表达数据. 这种方法有助于从复杂的空间转录组学数据集中发现基因关系和生物学见解.

科学领域:

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

背景情况:

  • 空间转录学使得在组织背景下进行基因表达分析.
  • 数据稀疏性和噪声在空间转录组学分析中存在重大挑战.
  • 了解共同表达模式对于将基因与生物功能联系起来至关重要.

研究的目的:

  • 介绍Smoothie,一种用于分析空间转录组学数据的新计算方法.
  • 为了应对空间转录学中的稀疏性和噪声的挑战.
  • 为了使全基因组共同表达网络的构建和集成.

主要方法:

  • 用于否定空间转录组学数据的高斯平滑.
  • 构建和整合全基因组共同表达网络.
  • 利用隐式和显式并行实现可扩展性.

主要成果:

  • 顺滑有效地否定了空间转录组学数据.
  • 该方法可实现精确的基因模块检测和功能注释.
  • 可扩展到大型数据集 (>1亿个点) 具有快速运行时间和低内存使用.
  • 便于将基因表达与基因组架构和多样本比较联系起来.

更多相关视频

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

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Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
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Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

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相关实验视频

Last Updated: May 23, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

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

Published on: July 6, 2022

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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

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Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

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结论:

  • Smoothie为空间转录组学分析提供了一个可扩展和多功能框架.
  • 该方法可以从高分辨率数据中提取更深层次的生物学见解.
  • 能够在各种生物环境中对基因表达模式进行可靠的评估.