Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Protein Networks02:26

Protein Networks

4.0K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.0K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Living single-cell metabolomics <i>via</i> mass spectrometry: state of the art and perspective.

Chemical science·2026
Same author

Magnetic Metal-Organic Framework (MOF)-Mediated Precision Capture of Mitochondria Reveals Subcellular Metabolic and Lipid Remodeling of Crabtree Effect in Yeast.

Analytical chemistry·2026
Same author

SMODA: Interpretable Multimodal Omics Integration for Disease Classification and Subtype Discovery via Heterogeneous Transfer Learning.

Analytical chemistry·2026
Same author

Distinct metabolomic and proteomic signatures in Parkinson's disease patients with REM sleep behavior disorder.

Signal transduction and targeted therapy·2026
Same author

Maternal metabolic signatures at early gestation associated with birth weight and neurodevelopment in early childhood.

Communications medicine·2026
Same author

Exploratory Analysis of Gut Microbiome and Metabolic Profile Changes Following Lenvatinib and Anti-PD-1 Combination Therapy in Liver Cancer.

Metabolites·2026

相关实验视频

Updated: Jul 21, 2025

Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products
11:13

Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products

Published on: March 12, 2020

11.0K

一个结构导向的分子网络战略全球非定向的代谢学数据注释.

Xinxin Wang1,2,3, Chao Li1,4,2,3, Zaifang Li1,2,3

  • 1CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, P.R. China.

Analytical chemistry
|July 26, 2023
PubMed
概括
此摘要是机器生成的。

一个新的结构导向分子网络策略 (SGMNS) 增强了非目标代谢学中的代谢物注释. 这种方法通过利用化学结构相似性进行光谱预测来提高深度注释的准确性.

更多相关视频

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

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

Published on: November 10, 2023

2.4K
Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures
09:38

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

Published on: January 7, 2019

8.7K

相关实验视频

Last Updated: Jul 21, 2025

Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products
11:13

Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products

Published on: March 12, 2020

11.0K
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

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

Published on: November 10, 2023

2.4K
Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures
09:38

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

Published on: January 7, 2019

8.7K

科学领域:

  • 代谢学 代谢学 代谢学
  • 生物信息学是一种生物信息学.
  • 分析化学 分析化学

背景情况:

  • 大规模的代谢物注释在非目标代谢学中是一个重大挑战.
  • 目前基于网络的方法面临的局限性是由于MS/MS参考光谱不足.

研究的目的:

  • 开发一种结构引导分子网络策略 (SGMNS),用于深度注释非目标超高性能液体染色学高分辨率质谱 (MS) 代谢学数据.
  • 通过利用化学结构信息来克服现有的注释方法的局限性.

主要方法:

  • 基于化学结构的分子指纹相似性构建了一个全球连接分子网络 (GCMN).
  • 采用网络注释传播,使用已知的代谢物作为种子,将"伪"光谱分配给邻居.
  • 通过对实验数据进行预测的保留时间,MS1和"伪"光谱的搜索来代地传播注释.

主要成果:

  • SGMNS在代谢组注释方面表现出独特的优势.
  • 在多种生物样本 (细胞,便,血,组织,尿液) 中成功注释了大量代谢物,精度高 (>83%) 和低可变性 (RSD <2%).
  • 标注 701 (细胞),1557 (便),1147 (血),1095 (组织),1237 (尿液) 和2041 (汇总) 的代谢物.

结论:

  • SGMNS有效地利用现有代谢组的化学空间进行深度代谢物注释.
  • 该策略克服了在非目标代谢学中缺乏MS/MS参考光谱的瓶.
  • 这种方法显著提高了全面的代谢物识别能力.