An Integrative Approach for the Functional Analysis of Metagenomic Studies [chapter]

Jyotsna Talreja Wassan, Haiying Wang, Fiona Browne, Paul Wash, Brain Kelly, Cintia Palu, Nina Konstantinidou, Rainer Roehe, Richard Dewhurst, Huiru Zheng
2017 Lecture Notes in Computer Science  
Metagenomics is one of the most prolific "omic" sciences in the context of biological research on environmental microbial communities. The studies related to metagenomics generate high-dimensional, sparse, complex, and biologically rich data-sets. In this research, we propose a framework which integrates omics-knowledge to identify suitable-reduced set of microbiomes features, for gaining insights into functional classification of the metagenomic sequences. The proposed approach has been
more » ... to two Use Case studies, on: 1) cattle rumen microbiota samples, for differentiating nitrate and vegetable oil treated feed, for improving cattle performance, under MetaPlat H-2020 Project 1 , and 2) human gut microbiota and classifying them in functionally annotated categories of leanness, obesity, or overweight. A high Accuracy of 97.5 % and Area Under Curve performance value (AUC) of 0.972 was achieved for classifying Bos taurus, cattle rumen microbiota data samples using Logistic Regression (LR) as classification model as well as feature selector in wrapper based strategy for Use Case 1 and 94.4 % Accuracy with AUC of 1.000, for Use Case 2 on human gut microbiota. In general, LR classifier with Wrapper-LR learner (with ridge estimator) as feature selector, proved to be most robust in analysis.
doi:10.1007/978-3-319-63312-1_37 fatcat:jcgndid52bc5honfhmrvqp7tje