单同位素质量
洗脱
化学
质谱
色谱法
相似性(几何)
分析化学(期刊)
分辨率(逻辑)
质谱法
人工智能
计算机科学
图像(数学)
作者
Zuo‐Fei Yuan,Chao Liu,Hai‐Peng Wang,Rui‐Xiang Sun,Yan Fu,Jingfen Zhang,Leheng Wang,Hao Chi,You Li,Li‐Yun Xiu,Wenping Wang,Si‐Min He
出处
期刊:Proteomics
[Wiley]
日期:2011-11-22
卷期号:12 (2): 226-235
被引量:72
标识
DOI:10.1002/pmic.201100081
摘要
Abstract Determining the monoisotopic peak of a precursor is a first step in interpreting mass spectra, which is basic but non‐trivial. The reason is that in the isolation window of a precursor, other peaks interfere with the determination of the monoisotopic peak, leading to wrong mass‐to‐charge ratio or charge state. Here we propose a method, named pParse, to export the most probable monoisotopic peaks for precursors, including co‐eluted precursors. We use the relationship between the position of the highest peak and the mass of the first peak to detect candidate clusters. Then, we extract three features to sort the candidate clusters: (i) the sum of the intensity, (ii) the similarity of the experimental and the theoretical isotopic distribution, and (iii) the similarity of elution profiles. We showed that the recall of pParse, MaxQuant, and BioWorks was 98–98.8%, 0.5–17%, and 1.8–36.5% at the same precision, respectively. About 50% of tandem mass spectra are triggered by multiple precursors which are difficult to identify. Then we design a new scoring function to identify the co‐eluted precursors. About 26% of all identified peptides were exclusively from co‐eluted peptides. Therefore, accurately determining monoisotopic peaks, including co‐eluted precursors, can greatly increase peptide identification rate.
科研通智能强力驱动
Strongly Powered by AbleSci AI