{"refrec":{"BRefID":295819,"RR":"<b>Faust, K.; Lahti, L.; Gonze, D.; de Vos, W.M.; Raes, J.</b> (2015). Metagenomics meets time series analysis: unraveling microbial community dynamics. <i>Curr. Opin. Microbiol. 25</i>: 56-66. <a href=\"https://dx.doi.org/10.1016/j.mib.2015.04.004\" target=\"_blank\">https://dx.doi.org/10.1016/j.mib.2015.04.004</a>","BEntID":287907,"PublicFlag":1,"CheckedFlag":1,"wosflag":1,"vabbflag":0,"RefStringPartII":". <i>Curr. Opin. Microbiol. 25</i>: 56-66. <a href=\"https://dx.doi.org/10.1016/j.mib.2015.04.004\" target=\"_blank\">https://dx.doi.org/10.1016/j.mib.2015.04.004</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":0,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Faust, K.; Lahti, L.; Gonze, D.; de Vos, W.M.; Raes, J.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Faust, K. <i>et al.</i>","Englishabstract":"The recent increase in the number of microbial time series studies offers new insights into the stability and dynamics of microbial communities, from the world's oceans to human microbiota. Dedicated time series analysis tools allow taking full advantage of these data. Such tools can reveal periodic patterns, help to build predictive models or, on the contrary, quantify irregularities that make community behavior unpredictable. Microbial communities can change abruptly in response to small perturbations, linked to changing conditions or the presence of multiple stable states. With sufficient samples or time points, such alternative states can be detected. In addition, temporal variation of microbial interactions can be captured with time-varying networks. 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