質量分析法のためのバイオインフォマティクス Bioinformatics for Mass Spectrometry

著者名：Lisacek, Frédérique (EDT)

Humana（2026/10/15発売）

• 言語：ENG
• ISBN：9781071655184
• eISBN：9781071655191

Description

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.

Table of Contents

MSDMS: An Open-Source Web Application to Design and Manage Custom Tandem Mass Spectrometry Libraries.- Standardized Annotation of Quantitative Proteomics Datasets Using SDRF-Proteomics.- An Open Infrastructure for Mass Spectrometry Data in R.- Preprocessing Large-Scale Proteomics Data.- PSMatch: An R/Bioconductor Package to Explore Proteomics Identification Data.- A Uniquome-Based Method for Protein Identification by Mass Spectrometry.- ProtyQuant: A Step-By-Step Protocol for Label-Free Protein Quantification in Data-Dependent Acquisition (DDA) Shotgun Proteomics – From Raw Spectra to Comparative Analysis.- Integrated Analysis of Post-Translational Modifications and Total Proteome: Methods for Distinguishing Abundance from Usage Changes.- Pathway and Kinase Analysis in Proteomics and Phosphoproteomics with rROMA.- Software Pipelines for Targeted and Untargeted Proteomics in Bioengineering Applications.- Annotation and dereplication of peptidic natural products (PNPs).- Glycopeptide Mass Spectrometry Data Analysis using Protein Prospector.- Artificial Intelligence and Machine Learning in Mass Spectrometry-Based Proteomics and Metabolomics.- Annotating Tandem Mass Spectra in Metabolomics with DreaMS and MassSpecGym.- ProteoRift Protocols: A Deep Learning Filtering Workflow for Peptide Identification in Mass Spectrometry Data.- pXg: Comprehensive and Reliable Identification of MHC-I Antigens Using De Novo Peptide Sequencing and Immunotranscriptomic Datasets.