The article "Untargeted Metabolomics Strategies – Challenges and Emerging Directions" by Alexandra C. Schrimpe-Rutledge, Simona G. Codreanu, Stacy D. Sherrod, and John A. McLean discusses the challenges and advancements in untargeted metabolomics, particularly in mammalian systems. Metabolites, which are essential for cellular function, can provide valuable insights into phenotype prediction. The field of metabolomics has evolved from proteomics, leveraging similar methodologies but facing unique challenges, such as the identification process. Untargeted metabolomics workflows are valuable for generating hypotheses, but they also present challenges due to the complexity and diversity of metabolites. The article highlights the importance of accurate metabolite identification, which is crucial for biological interpretation. It reviews the strengths and weaknesses of different metabolomics platforms, including LC-MS, and discusses various data acquisition methods, such as DDA and DIA. The authors propose a confidence level system for metabolite annotations, ranging from validated identifications to preliminary and molecular formula candidates. They also address the issue of false positives and the need for orthogonal data to improve identification accuracy. The biological analysis of metabolites is emphasized, with tools and methods for integrating metabolomic data with biological networks and pathways. The article concludes by highlighting the exciting future of metabolomics research, emphasizing the need to address challenges in data interpretation, database content, isomer resolution, identification confidence, and False Discovery Rate (FDR).The article "Untargeted Metabolomics Strategies – Challenges and Emerging Directions" by Alexandra C. Schrimpe-Rutledge, Simona G. Codreanu, Stacy D. Sherrod, and John A. McLean discusses the challenges and advancements in untargeted metabolomics, particularly in mammalian systems. Metabolites, which are essential for cellular function, can provide valuable insights into phenotype prediction. The field of metabolomics has evolved from proteomics, leveraging similar methodologies but facing unique challenges, such as the identification process. Untargeted metabolomics workflows are valuable for generating hypotheses, but they also present challenges due to the complexity and diversity of metabolites. The article highlights the importance of accurate metabolite identification, which is crucial for biological interpretation. It reviews the strengths and weaknesses of different metabolomics platforms, including LC-MS, and discusses various data acquisition methods, such as DDA and DIA. The authors propose a confidence level system for metabolite annotations, ranging from validated identifications to preliminary and molecular formula candidates. They also address the issue of false positives and the need for orthogonal data to improve identification accuracy. The biological analysis of metabolites is emphasized, with tools and methods for integrating metabolomic data with biological networks and pathways. The article concludes by highlighting the exciting future of metabolomics research, emphasizing the need to address challenges in data interpretation, database content, isomer resolution, identification confidence, and False Discovery Rate (FDR).