Sherwani, M. K.; Ruuskanen, M.; Feldner-Busztin, D.; Firbas, P. N.; Boza, G.; Moreh, A.; Borman, T.; Erawijantari, P. P.; Scheuring, I.; Gopalakrishnan, S.; Lahti, L.

Recent developments in data generation have opened up unprecedented insights into living systems. It has been recognized that integrating and characterizing temporal variation simultaneously across multiple scales, from specific molecular interactions to entire ecosystems, is crucial for uncovering biological mechanisms and understanding the emergence of complex phenotypes. With the increasing number of studies incorporating multi-omics data sampled over time, it has become clear that integrated approaches are pivotal for these efforts. However, standard data analytical practices in longitudinal multi-omics are still shaping up and many of the available methods have not yet been widely evaluated and adopted. Thus, we performed a systematic literature review on data science methods for longitudinal multi-omics, with a particular focus on microbiome research.