rMSIfragment:通过自动化源中fragment注释来改善MALDI-MSI脂管学
Gerard Baquer1, Lluc Sementé2, Pere Ràfols3,4,5
1Department of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain. gerard.baquer@alumni.urv.cat.
Journal of cheminformatics
|September 15, 2023
概括
本研究介绍了rMSIfragment,这是一个R包,用于在矩阵辅助激光消化离子化质谱成像 (MALDI-MSI) 中自信地识别脂质. 它使用源中碎片来提高准确性,并减少脂质注释中的假阳性.
科学领域:
- 生物化学 生物化学
- 分析化学 分析化学
- 计算生物学 计算生物学
背景情况:
- 矩阵辅助激光吸附离子化质谱成像 (MALDI-MSI) 对于组织的空间化学分析至关重要.
- 脂质在生物过程中起着至关重要的作用,因此在MALDI-MSI中对其进行鉴定具有高度意义.
- 目前在MALDI-MSI中对脂质进行鉴定是具有挑战性的,因为分离能力有限,并没有针对性的双重质谱.
研究的目的:
- 开发一个开源的R包,rMSIfragment,在MALDI-MSI中进行可靠的脂质注释.
- 利用源中碎片化数据来改进脂质结构推断和识别.
- 在复杂的生物样本中提高脂质分析的准确性和可靠性.
主要方法:
- 开发了rMSIfragment,一个R包,利用已知的 adducts和碎片化途径进行脂质注释.
- 实施了一种新的评分系统来排名脂质注释,使用HPLC-MS和Target-Decoy方法进行验证.
- 在各种 MALDI-MSI 样品类型和实验设置中应用了该套件.
主要成果:
- rMSIfragment包成功地通过利用源中碎片在MALDI-MSI数据中的脂质进行注释.
- 新的评分系统在ROC分析中实现了0.7的曲线下的面积,表明了高的注释性能.
- 证明,与忽视它们的工作流相比,将源中碎片纳入数据源显著减少了不正确的脂质注释.
结论:
- rMSIfragment提供了一个强大的解决方案,可以在MALDI-MSI研究中增加对脂质识别的信心.
- 考虑源中碎片化数据对于准确的脂质注释和最大限度地减少假阳性是必不可少的.
- 该套件广泛适用于各种MALDI-MSI实验,提高了脂质组分析的整体质量.
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