点积
熵(时间箭头)
模式识别(心理学)
相似性(几何)
计算机科学
人工智能
数据挖掘
数学
物理
几何学
量子力学
图像(数学)
作者
Yuanyue Li,Tobias Kind,Jacob Folz,Arpana Vaniya,Sajjan S. Mehta,Oliver Fiehn
出处
期刊:Nature Methods
[Springer Nature]
日期:2021-12-01
卷期号:18 (12): 1524-1531
被引量:77
标识
DOI:10.1038/s41592-021-01331-z
摘要
Compound identification in small-molecule research, such as untargeted metabolomics or exposome research, relies on matching tandem mass spectrometry (MS/MS) spectra against experimental or in silico mass spectral libraries. Most software programs use dot product similarity scores. Here we introduce the concept of MS/MS spectral entropy to improve scoring results in MS/MS similarity searches via library matching. Entropy similarity outperformed 42 alternative similarity algorithms, including dot product similarity, when searching 434,287 spectra against the high-quality NIST20 library. Entropy similarity scores proved to be highly robust even when we added different levels of noise ions. When we applied entropy levels to 37,299 experimental spectra of natural products, false discovery rates of less than 10% were observed at entropy similarity score 0.75. Experimental human gut metabolome data were used to confirm that entropy similarity largely improved the accuracy of MS-based annotations in small-molecule research to false discovery rates below 10%, annotated new compounds and provided the basis to automatically flag poor-quality, noisy spectra.
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