T. Nishioka, T. Kasama, T. Kinumi, H. Makabe, Fumio Matsuda, Daisuke Miura, M. Miyashita, Takemichi Nakamura, Kenichi Tanaka, Atsushi Yamamoto
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引用次数: 22
摘要
CASMI (Critical Assessment of Small Molecule Identification)是一项竞赛,参赛者使用盲质谱作为挑战数据来识别挑战分子的分子式和化学结构。7个研究团队参与了CASMI2013。CASMI2013的冠军是美国伊利诺斯州芝加哥大学的Andrew Newsome和Dejan Nikolic团队。该团队通过人工解释挑战数据、搜索内部和公共质谱数据库、化学物质和文献数据库,确定了16个挑战分子中的15个。MAGMa被选为CASMI2013的最佳自动化工具。在一些挑战中,大多数自动化工具成功地识别了挑战分子,而不依赖于化合物类别和分子质量的大小。在这些挑战数据中,所有同位素峰和鉴定所需的产物离子都在预期的质量精度范围内观察到。在其他挑战中,大多数自动化工具都失败了,或者识别了解决方案候选以及许多假阳性候选。然后,我们根据质谱的质量、解离机制、挑战分子的化合物类别和元素组成来分析这些挑战数据。
Winners of CASMI2013: Automated Tools and Challenge Data.
CASMI (Critical Assessment of Small Molecule Identification) is a contest in which participants identify the molecular formula and chemical structure of challenging molecules using blind mass spectra as the challenge data. Seven research teams participated in CASMI2013. The winner of CASMI2013 was the team of Andrew Newsome and Dejan Nikolic, the University of Illinois at Chicago, IL, USA. The team identified 15 among 16 challenge molecules by manually interpreting the challenge data and by searching in-house and public mass spectral databases, and chemical substance and literature databases. MAGMa was selected as the best automated tool of CASMI2013. In some challenges, most of the automated tools successfully identified the challenge molecules, independent of the compound class and magnitude of the molecular mass. In these challenge data, all of the isotope peaks and the product ions essential for the identification were observed within the expected mass accuracy. In the other challenges, most of the automated tools failed, or identified solution candidates together with many false-positive candidates. We then analyzed these challenge data based on the quality of the mass spectra, the dissociation mechanisms, and the compound class and elemental composition of the challenge molecules.