NetShift: a methodology for understanding ‘driver microbes’ from healthy and disease microbiome datasets
| Title | NetShift: a methodology for understanding ‘driver microbes’ from healthy and disease microbiome datasets |
| Publication Type | Journal Article |
| Year of Publication | 2019 |
| Authors | Kuntal, BK, Chandrakar, P, Sadhu, S, Mande, SS |
| Journal | ISME Journal |
| Volume | 13 |
| Issue | 2 |
| Pagination | 442-454 |
| Date Published | JAN |
| Type of Article | Article |
| Abstract | The 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 |
| DOI | 10.1038/s41396-018-0291-x |
| Type of Journal (Indian or Foreign) | Foreign |
| Impact Factor (IF) | 9.520 |
Divison category:
Physical and Materials Chemistry