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時系列メタゲノム多様性解析×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2010s (formalized with 16S amplicon sequencing era; expanded ~2012–2020)2006–2018 (principal methods established)
提唱者Developed iteratively through the microbiome research community; key contributions from Susan Holmes, Rob Knight, and colleaguesConesa et al. (maSigPro, 2006); extended by Fischer et al. (ImpulseDE2, 2018) and others
種類Longitudinal observational / bioinformatics pipelineComputational genomics pipeline
原典Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A., & Holmes, S. P. (2016). DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods, 13(7), 581–583. DOI ↗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 microbiome diversity analysis, temporal microbiome analysis, repeated-measures microbiome diversity, time-course microbiome analysislongitudinal RNA-seq DE analysis, temporal transcriptomics, time-course RNA-seq, dynamic DE analysis
関連56
概要Time-series microbiome diversity analysis tracks how the richness, evenness, and community composition of microbial communities change across multiple time points within the same subjects. By combining standard diversity metrics with longitudinal statistical models, it separates true temporal dynamics from inter-individual variation, identifying when and how perturbations such as diet changes, antibiotic treatment, or disease onset reshape the microbiome.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.
ScholarGateデータセット
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ScholarGate手法を比較: Time-series microbiome diversity analysis · Time-series RNA-seq differential expression. 2026-06-19に以下より取得 https://scholargate.app/ja/compare