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時間経過プロテオミクス解析×時系列RNAシーケンス差次的発現×
分野バイオインフォマティクスバイオインフォマティクス
系統Process / pipelineProcess / pipeline
提唱年2000s (quantitative framework: Gygi et al. 1999; time-series designs: 2004–2010)2006–2018 (principal methods established)
提唱者Multiple groups; Gygi et al. (1999) established quantitative proteomics; time-series designs emerged in the 2000s with LC-MS/MS workflowsConesa et al. (maSigPro, 2006); extended by Fischer et al. (ImpulseDE2, 2018) and others
種類Quantitative longitudinal omics pipelineComputational genomics pipeline
原典Lemeer, S., & Heck, A. J. R. (2012). The phosphoproteomics data explosion. Current Opinion in Chemical Biology, 16(1–2), 1–8. link ↗Conesa, A., Nueda, M. J., Ferrer, A., & Talon, M. (2006). maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments. Bioinformatics, 22(9), 1096–1102. link ↗
別名longitudinal proteomics, temporal proteomics, dynamic proteomics, time-course proteomicslongitudinal RNA-seq DE analysis, temporal transcriptomics, time-course RNA-seq, dynamic DE analysis
関連66
概要Time-series proteomics analysis quantifies protein abundance across two or more ordered time points to reveal how the proteome changes dynamically in response to stimuli, developmental stages, or disease progression. By combining mass spectrometry-based protein quantification with statistical models designed for temporal data, the method identifies proteins with significant expression trends, oscillatory patterns, or delayed responses that cannot be detected in single time-point studies.Time-series RNA-seq differential expression analysis identifies genes whose expression levels change systematically across ordered time points — such as during development, disease progression, or response to a treatment. Unlike two-condition DE analysis, it explicitly models the temporal structure of the data, capturing dynamic gene expression trajectories rather than a single snapshot contrast. Tools such as maSigPro, ImpulseDE2, and splineTimeR have been developed specifically for this design.
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ScholarGate手法を比較: Time-series proteomics analysis · Time-series RNA-seq differential expression. 2026-06-19に以下より取得 https://scholargate.app/ja/compare