01643nas a2200169   4500008004100000245011300041210006900154260000800223300001200231490000700243520100500250100001901255700001901274700001401293700001801307856014801325       2019                        eng d00aNetShift: a methodology for understanding ‘driver microbes’ from healthy and disease microbiome datasets0 aNetShift a methodology for understanding driver microbes from he  cJAN  a442-4540 v133 aThe combined effect of mutual association within the co-inhabiting microbes in human body is known to play a major role in determining health status of individuals. The differential taxonomic abundance between healthy and disease are often used to identify microbial markers. However, in order to make a microbial community based inference, it is important not only to consider microbial abundances, but also to quantify the changes observed among inter microbial associations. In the present study, we introduce a method called ‘NetShift’ to quantify rewiring and community changes in microbial association networks between healthy and disease. Additionally, we devise a score to identify important microbial taxa which serve as ‘drivers’ from the healthy to disease. We demonstrate the validity of our score on a number of scenarios and apply our methodology on two real world metagenomic datasets. The ‘NetShift’ methodology is also implemented as a web-based application available at 1 aKuntal, B., K.1 aChandrakar, P.1 aSadhu, S.1 aMande, S., S.  uhttps://library.ncl.res.in/content/netshift-methodology-understanding-%E2%80%98driver-microbes%E2%80%99-healthy-and-disease-microbiome-datasets