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时间序列蛋白质组学分析×时间序列 RNA-seq 差异表达×
领域生物信息学生物信息学
方法族Process / pipelineProcess / pipeline
起源年份2000s (quantitative framework: Gygi et al. 1999; time-series designs: 2004–2010)2006–2018 (principal methods established)
提出者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
类型Quantitative longitudinal omics pipelineComputational genomics pipeline
开创性文献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 ↗
别名longitudinal proteomics, temporal proteomics, dynamic proteomics, time-course proteomicslongitudinal RNA-seq DE analysis, temporal transcriptomics, time-course RNA-seq, dynamic DE analysis
相关66
摘要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方法对比: Time-series proteomics analysis · Time-series RNA-seq differential expression. 于 2026-06-19 检索自 https://scholargate.app/zh/compare