방법 비교

	시계열 RNA-seq 차등 발현	시간 계열 eQTL 분석
분야	생물정보학	생물정보학
계열	Process / pipeline	Process / pipeline
기원 연도	2006–2018 (principal methods established)	2010s–2019 (concept established earlier; dynamic framework formalized ~2019)
창시자	Conesa et al. (maSigPro, 2006); extended by Fischer et al. (ImpulseDE2, 2018) and others	Multiple groups; formalized by Strober et al. and others in the context of cellular differentiation (2019)
유형	Computational genomics pipeline	Genetic mapping method
원전	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.	Fair, B. J., et al. (2020). Gene expression variability in human and chimpanzee populations share common determinants. eLife, 9, e59929.
별칭	longitudinal RNA-seq DE analysis, temporal transcriptomics, time-course RNA-seq, dynamic DE analysis	dynamic eQTL analysis, longitudinal eQTL mapping, ts-eQTL, temporal eQTL
관련	6	2
요약	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.	Time-series eQTL analysis identifies genetic variants (eQTLs) whose effect on gene expression changes over time or across developmental stages. By combining longitudinal RNA-seq data with individual genotypes, the method captures how the same SNP can activate, silence, or reshape gene regulation at different time points — revealing the temporal architecture of the genome's regulatory program in processes such as differentiation, disease progression, and environmental response.