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시계열 단백질체학 분석×시계열 RNA-seq 차등 발현×
분야생물정보학생물정보학
계열Process / pipelineProcess / pipeline
기원 연도2000s (quantitative framework: Gygi et al. 1999; time-series designs: 2004–2010)2006–2018 (principal methods established)
창시자Multiple 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
유형Quantitative longitudinal omics pipelineComputational genomics pipeline
원전Lemeer, 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 ↗
별칭longitudinal proteomics, temporal proteomics, dynamic proteomics, time-course proteomicslongitudinal RNA-seq DE analysis, temporal transcriptomics, time-course RNA-seq, dynamic DE analysis
관련66
요약Time-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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ScholarGate방법 비교: Time-series proteomics analysis · Time-series RNA-seq differential expression. 2026-06-19에 다음에서 검색함: https://scholargate.app/ko/compare