Dataset Information

'NetShift': a methodology for understanding 'driver microbes' from healthy and disease microbiome datasets.

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 https://web.rniapps.net/netshift.

SUBMITTER: Kuntal BK

PROVIDER: S-EPMC6331612 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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'NetShift': a methodology for understanding 'driver microbes' from healthy and disease microbiome datasets.

Kuntal Bhusan K BK Chandrakar Pranjal P Sadhu Sudipta S Mande Sharmila S SS

The ISME journal 20181004 2

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, ...[more]

PMID: 30287886

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Dataset Information

'NetShift': a methodology for understanding 'driver microbes' from healthy and disease microbiome datasets.

Publications

'NetShift': a methodology for understanding 'driver microbes' from healthy and disease microbiome datasets.

Similar Datasets

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