เปรียบเทียบวิธี

	การวิเคราะห์โปรตีโอมิกส์อนุกรมเวลา	การวิเคราะห์เมแทบอโลมิกส์อนุกรมเวลา
สาขาวิชา	ชีวสารสนเทศศาสตร์	ชีวสารสนเทศศาสตร์
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	2000s (quantitative framework: Gygi et al. 1999; time-series designs: 2004–2010)	2000s–2010s
ผู้ริเริ่ม	Multiple groups; Gygi et al. (1999) established quantitative proteomics; time-series designs emerged in the 2000s with LC-MS/MS workflows	Developed from general metabolomics workflows; longitudinal extensions pioneered by A. K. Smilde, R. Bino, and colleagues
ประเภท	Quantitative longitudinal omics pipeline	Quantitative longitudinal omics pipeline
แหล่งต้นตำรับ	Lemeer, S., & Heck, A. J. R. (2012). The phosphoproteomics data explosion. Current Opinion in Chemical Biology, 16(1–2), 1–8.	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.
ชื่อเรียกอื่น	longitudinal proteomics, temporal proteomics, dynamic proteomics, time-course proteomics	longitudinal metabolomics, dynamic metabolomics, temporal metabolome profiling, kinetic metabolomics
ที่เกี่ยวข้อง	6	6
สรุป	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 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.