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時系列メタボロミクス解析×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–2010s2006–2018 (principal methods established)
提唱者Developed from general metabolomics workflows; longitudinal extensions pioneered by A. K. Smilde, R. Bino, and colleaguesConesa et al. (maSigPro, 2006); extended by Fischer et al. (ImpulseDE2, 2018) and others
種類Quantitative longitudinal omics pipelineComputational genomics pipeline
原典Smilde, A. K., van der Werf, M. J., Bijlsma, S., van der Werff-van der Vat, B. J. C., & Jellema, R. H. (2005). Fusion of mass spectrometry-based metabolomics data. Analytical Chemistry, 77(20), 6729–6736. 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 metabolomics, dynamic metabolomics, temporal metabolome profiling, kinetic metabolomicslongitudinal RNA-seq DE analysis, temporal transcriptomics, time-course RNA-seq, dynamic DE analysis
関連66
概要Time-series metabolomics analysis profiles small-molecule metabolites from biological samples collected at multiple, ordered time points, enabling researchers to capture the dynamic flux of metabolic pathways in response to stimuli, disease progression, drug treatment, or developmental change. By integrating longitudinal statistical models with standard metabolomics preprocessing, the approach goes beyond a static metabolic snapshot to reveal how, when, and in what sequence metabolic responses unfold.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 metabolomics analysis · Time-series RNA-seq differential expression. 2026-06-19に以下より取得 https://scholargate.app/ja/compare