This volume looks at the usage of bioinformatics in processing and interpreting mass spectrometry (MS) data in various -omics. The chapters in the book are organized into four parts that cover the purposes of bioinformatics usage in MS-based studies, as well as emerging trends requiring further customization or innovation. Part One focuses on MS raw data management, especially related to handling data from diverse instrument sources. Part Two explores a range of software tools for the analysis and interpretation of MS data, reflecting ongoing technological advances (e.g., improved mass accuracy), as well as the evolution of experimental strategies. Part Three covers a collection of topics that are not yet mainstream, but likely to gain importance soon such as natural products or glycoconjugates. Part Four discusses the use of Artificial Intelligence (AI) methods in support of MS data analysis in proteomics and metabolomics. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials, step-by-step, readily reproducible protocols, and tips on troubleshooting and avoiding known pitfalls.Cutting-edge and comprehensive, Bioinformatics for Mass Spectrometry is a valuable reference for researchers wish to understand the bioinformatics challenges of processing MS data in a broad range of application fields. Graduate students, researchers tackling molecule identification in complex samples, and bioinformaticians motivated to integrate new or established tools in pipelines will also find this book helpful. The diverse themes covered in this book also reach an extended readership in disciplines like biotechnological, pharmaceutical, biological and medical sciences, as well as computational and engineering sciences.Chapter 14 is available open access under a Creative Commons Attribution-NonCommercial-NoDerivates 4.0. International License via link.springer.com.

This volume looks at the usage of bioinformatics in processing and interpreting mass spectrometry (MS) data in various -omics. The chapters in the book are organized into four parts that cover the purposes of bioinformatics usage in MS-based studies, as well as emerging trends requiring further customization or innovation. Part One focuses on MS raw data management, especially related to handling data from diverse instrument sources. Part Two explores a range of software tools for the analysis and interpretation of MS data, reflecting ongoing technological advances (e.g., improved mass accuracy), as well as the evolution of experimental strategies. Part Three covers a collection of topics that are not yet mainstream, but likely to gain importance soon such as natural products or glycoconjugates. Part Four discusses the use of Artificial Intelligence (AI) methods in support of MS data analysis in proteomics and metabolomics. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials, step-by-step, readily reproducible protocols, and tips on troubleshooting and avoiding known pitfalls.Cutting-edge and comprehensive, Bioinformatics for Mass Spectrometry is a valuable reference for researchers wish to understand the bioinformatics challenges of processing MS data in a broad range of application fields. Graduate students, researchers tackling molecule identification in complex samples, and bioinformaticians motivated to integrate new or established tools in pipelines will also find this book helpful. The diverse themes covered in this book also reach an extended readership in disciplines like biotechnological, pharmaceutical, biological and medical sciences, as well as computational and engineering sciences.Chapter 14 is available open access under a Creative Commons Attribution-NonCommercial-NoDerivates 4.0. International License via link.springer.com.