非表示:
キーワード:
DATA-INDEPENDENT ACQUISITION; CHEMICAL CROSS-LINKING; PHOSPHORYLATION
SITE LOCALIZATION; FALSE DISCOVERY RATE; PROTEIN IDENTIFICATION; PEPTIDE
IDENTIFICATION; TARGETED PROTEOMICS; STATISTICAL-MODEL; ENABLES
ACCURATE; SOFTWAREMathematical & Computational Biology; computational proteomics; mass spectrometry; posttranslational
modifications; multiomics data analysis; multivariate analysis; network
analysis;
要旨:
Computational proteomics is the data science concerned with the identification and quantification of proteins from high-throughput data and the biological interpretation of their concentration changes, posttranslational modifications, interactions, and subcellular localizations. Today, these data most often originate from mass spectrometry-based shotgun proteomics experiments. In this review, we survey computational methods for the analysis of such proteomics data, focusing on the explanation of the key concepts. Starting with mass spectrometric feature detection, we then cover methods for the identification of peptides. Subsequently, protein inference and the control of false discovery rates are highly important topics covered. We then discuss methods for the quantification of peptides and proteins. A section on downstream data analysis covers exploratory statistics, network analysis, machine learning, and multiomics data integration. Finally, we discuss current developments and provide an outlook on what the near future of computational proteomics might bear.
