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相关概念视频

Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

6.8K
Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...
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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...
7.9K
MALDI-TOF Mass Spectrometry01:19

MALDI-TOF Mass Spectrometry

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Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.
Matrix-assisted laser desorption ionization (MALDI) is a commonly...
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通过修改的预期最大化算法,在基于质谱的元蛋白学中跨分类学水平的生物功能分配.

Gelio Alves, Aleksey Y Ogurtsov, Yi-Kuo Yu

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    此摘要是机器生成的。

    使用预期最大化 (EM) 算法的新MiCId工作流改善了微生物识别和生物功能赋值在metaproteomics. 与现有方法相比,这种增强的工具提供了更高的准确性和对错误发现的控制.

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    科学领域:

    • 微生物学 微生物学
    • 生物信息学是一种生物信息学.
    • 甲型蛋白质组学是什么意思?

    背景情况:

    • 在元蛋白质组学中,微生物功能的准确识别和量化受到"共享可靠识别的问题"的阻碍.
    • 当前的工具经常使用最小共同祖先 (LCA) 算法,导致不完整的分类学和功能赋值.
    • 现有的方法在精确的生物质估计和控制整个微生物谱系的错误发现方面扎.

    研究的目的:

    • 开发一个增强的MiCId工作流程,解决元蛋白质组数据分析的局限性.
    • 提高微生物识别,生物质估计和生物功能分配的准确性.
    • 为了更好地控制在metaproteomic分析中的错误发现.

    主要方法:

    • 在MiCId工作流中实现预期最大化 (EM) 算法.
    • 整合一个全面的生物功能数据库.
    • 使用合成数据集进行验证,并对人类口腔和肠道微生物群数据进行重新分析.

    主要成果:

    • 与使用合成数据的Unipept和MetaGOmics相比,增强的MiCId工作流在微生物识别和生物量估计方面表现出更高的准确性.
    • 在生物功能识别方面,MiCId显示了更好的准确性和更好的错误发现控制,而不是Unipept.
    • 在整个分类谱系中实现了函数丰度的可靠计算.
    • 对微生物组数据集的重新分析产生了与原始出版物一致的结果.

    结论:

    • 增强的MiCId工作流为基于质谱的元蛋白质组学提供了重大进步.
    • 它提供了更准确和可靠的识别微生物及其功能,改进了错误发现控制.
    • 该工具增强了对微生物群落及其生物学作用的全面理解.