4.7 Article

MSTracer: A Machine Learning Software Tool for Peptide Feature Detection from Liquid Chromatography-Mass Spectrometry Data

期刊

JOURNAL OF PROTEOME RESEARCH
卷 20, 期 7, 页码 3455-3462

出版社

AMER CHEMICAL SOC
DOI: 10.1021/acs.jproteome.0c01029

关键词

LC-MS; machine learning; peptide feature detection

资金

  1. NSERC
  2. Genome Canada
  3. Ontario Genomics through a Bioinformatics and Computational Biology program [OGI166]

向作者/读者索取更多资源

MSTracer is a new software tool for detecting peptide features from MS data, which incorporates two scoring functions based on machine learning and has demonstrated significantly better performance compared to existing tools.
Liquid chromatography with tandem mass spectrometry (MS/MS) has been widely used in proteomics. Although a typical experiment includes both MS and MS/MS scans, existing bioinformatics research has focused far more on MS/MS data than on MS data. In MS data, each peptide produces a few trails of signal peaks, which are collectively called a peptide feature. Here, we introduce MSTracer, a new software tool for detecting peptide features from MS data. The software incorporates two scoring functions based on machine learning: one for detecting the peptide features and the other for assigning a quality score to each detected feature. The software was compared with several existing tools and demonstrated significantly better performance.

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