As the cost of sequencing decreases, the complexity and size of RNA sequencing (RNA-Seq) experiments are rapidly growing. In particular, paired, longitudinal and other correlated study designs are becoming commonplace [1, 2]. Longitudinal studies of how gene expression changes over the disease course and under differing treatment and environmental conditions are critical to understanding how disease evolves in individual patients and for developing accurate biomarkers of disease progression and treatment response. However, the most popular RNA-Seq data analysis tools are not equipped to analyze correlated and longitudinal data [3].
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