Navigating common pitfalls in metabolite identification and metabolomics bioinformatics

Area of Science:

• Metabolomics and Bioinformatics
• Analytical Chemistry
• Computational Biology

Background:

• Metabolomics, the study of small molecules, is a rapidly advancing field with increasing data complexity.
• Growing demand for bioinformaticians skilled in metabolomics data analysis and metabolite identification.
• Current bioinformatics curricula and analytical chemistry training often lack comprehensive metabolomics components.

Purpose of the Study:

• To provide an educational review summarizing key concepts and common pitfalls in metabolomics bioinformatics.
• To address misunderstandings between bioinformaticians and analytical chemists regarding metabolite annotation and identification.
• To facilitate learning for bioinformaticians entering metabolomics, particularly in metabolite identification, for effective collaboration.

Main Methods:

• Summarizing core concepts in LC-MS/MS based non-targeted metabolomics.
• Comparing metabolomics data types with those familiar to bioinformaticians.
• Highlighting practical challenges and solutions encountered in interdisciplinary collaborations.

Main Results:

• Identified key knowledge gaps and misunderstandings in metabolite annotation and identification processes.
• Demonstrated parallels between metabolomics data and other data types to aid bioinformatician understanding.
• Provided practical insights for successful integration of bioinformatics tools in metabolomics research.

Conclusions:

• Effective collaboration between bioinformaticians and analytical chemists is crucial for advancing metabolomics.
• Targeted educational efforts are needed to equip bioinformaticians with essential metabolomics bioinformatics skills.
• This review serves as a guide to improve understanding and application of in-silico metabolite identification tools.