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相关概念视频

Proteomics01:33

Proteomics

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
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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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相关实验视频

Updated: Jul 16, 2025

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.
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Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

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MoleculeExperiment能够为生物导体中的分子分辨率空间奥米克数据提供一致的基础设施.

Bárbara Zita Peters Couto1,2,3, Nicholas Robertson1,2,3,4, Ellis Patrick1,2,3,4,5

  • 1School of Mathematics and Statistics, The University of Sydney, Camperdown, NSW 2006, Australia.

Bioinformatics (Oxford, England)
|September 12, 2023
PubMed
概括
此摘要是机器生成的。

一个新的R/生物导体包,MoleculeExperiment,标准化了分子级空间转录组学 (ST) 数据. 这使得基于成像的ST技术能够对亚细胞生物学进行一致的分析.

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Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
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Using the Open-Source MALDI TOF-MS IDBac Pipeline for Analysis of Microbial Protein and Specialized Metabolite Data
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相关实验视频

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Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
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科学领域:

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

背景情况:

  • 基于成像的空间转录学 (ST) 为组织内的分子检测提供了亚细胞分辨率.
  • 现有的R/生物导体计算工具缺乏用于分子级ST数据的基础设施,阻碍了分析.
  • 商业ST平台的兴起需要为单个分子数据提供标准化的数据结构.

研究的目的:

  • 在R/生物导体中开发分子级ST数据的计算框架.
  • 标准化和整合来自各种基于成像的ST技术的分子级数据.
  • 为了促进分子级ST数据的过渡到已建立的分析对象,如SpatialExperiment.

主要方法:

  • 分子实验R/生物导体包的开发.
  • 实现用于存储分子和细胞细分信息的数据结构.
  • 在ST平台上为分子级数据创建标准化协议.

主要成果:

  • MoleculeExperiment包成功地存储和标准化了分子级的ST数据.
  • 它可以将分子级数据无转换为空间实验对象.
  • 该包支持来自各种基于成像的ST技术的数据,包括10× Genomics Xenium.

结论:

  • MoleculeExperiment提供了基本的数据基础设施,用于分子解析的空间奥米克.
  • 它可以对高分辨率的ST数据进行一致和精简的分析.
  • 该方案促进通过ST更广泛地采用和更深入地了解亚细胞生物过程.