Methods of Metabolite Identification Using MS/MS Data. Issue 1 (2nd January 2022)

• Record Type: Journal Article
• Authors: Kwak, Myungjae; Kang, Kyungwoo; Wang, Yingfeng
• Is Part Of: Journal of computer information systems. Volume 62:Issue 1(2022)
• Page Start: 12
• Page End: 18
• Publication Date: 2022-01-02
• Subjects: Bioinformatics -- machine learning -- metabolite identification -- tandem mass spectrometry; Electronic data processing -- Study and teaching -- Periodicals; 658.4038011
• DOI: 10.1080/08874417.2019.1681328
• Languages: English
• ISSNs: 0887-4417

Abstract

ABSTRACT: Researchers in bioinformatics and medical science fields have been developing various innovative information systems and software tools using emerging technologies. Metabolite profiling is one of the fields in which bioinformatics researchers are intensely developing various innovative methods and software tools. Metabolites are the intermediate and end products of metabolism, which is the set of life-sustaining chemical reactions in living organisms. Accurate and complete identification of metabolites can advance many medical science and bioinformatics fields by providing a direct way of observing metabolic activities. The analysis of tandem mass spectrometry (MS/MS) data has been a long-term computational challenge in bioinformatics. This study examines various existing and emerging methods and software tools for analyzing MS/MS data for metabolite identification and discusses challenges and future perspectives in this important bioinformatics research area.