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Genome Annotation and Assembly03:36

Genome Annotation and Assembly

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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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Phase II reactions are essential for the detoxification and elimination of drugs from the body. These reactions involve the conjugation of parent drugs or their phase I metabolites with endogenous molecules, resulting in more hydrophilic drug conjugates. The primary conjugation reactions in this phase are sulfation and glucuronidation. Both sulfation and glucuronidation typically produce biologically inactive metabolites. However, in some cases involving prodrugs, active metabolites may be...
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An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
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基于深度学习的代谢物注释

Hoi Yan Katharine Chau, Hongyu Ao, Xinran Zhang

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
    PubMed
    概括
    此摘要是机器生成的。

    机器学习通过预测来自双重质谱 (MS/MS) 数据的分子指纹来增强非目标代谢学中的代谢物注释,在光谱库有限时有助于识别.

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    科学领域:

    • 分析化学 分析化学
    • 计算生物学 计算生物学
    • 生物化学 生物化学

    背景情况:

    • 代谢物注释是使用液态染色体质谱法 (LC-MS) 的非目标代谢学的一个关键挑战.
    • 有限的公共光谱图书馆包含串联质谱 (MS/MS) 数据,阻碍了对未知化合物的准确识别.
    • 机器学习为预测MS/MS光谱的分子性质提供了一个有希望的途径.

    研究的目的:

    • 从MS/MS数据提高分子指纹预测的准确性.
    • 为了增强基于LC-MS的非目标代谢学中的代谢物识别.
    • 调查高维光谱和指纹表示的实用性.

    主要方法:

    • 利用卷积神经网络 (CNN) 来进行分子指纹预测.
    • 雇佣的MS/MS从MoNA存储库和NIST 20中的光谱数据.
    • 探索了光谱数据和分子指纹的高维表示.

    主要成果:

    • 该研究研究了使用MS/MS光谱来提高分子指纹预测准确性的方法.
    • 研究了高维的表示,以提高预测性能.
    • 该方法有助于对现有图书馆中无法找到的光谱的候选代谢物ID进行排名.

    结论:

    • 机器学习,特别是CNN,可以有效地从MS/MS数据中预测分子指纹.
    • 这种预测能力有助于注释缺乏光谱库匹配的代谢物.
    • 研究高维数据表示显示了进一步提高代谢学注释准确性的潜力.