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An Integrative Approach for the Functional Analysis of Metagenomic Studies

Wassan, Jyotsna Talreja, Wang, Haiying / HY, Fiona, Browne, Wash, Paul, Kelly, Brain, Palu, Cintia, Konstantinidou, Nina, Roehe, Rainer, Dewhurst, Richard and Zheng, Huiru (2017) An Integrative Approach for the Functional Analysis of Metagenomic Studies. In: Intelligent Computing Theories and Application. Springer, pp. 421-427. ISBN 978-3-319-63311-4 [Book section]

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URL: http://dx.doi.org/10.1007/978-3-319-63312-1_37

DOI: 10.1007/978-3-319-63312-1_37

Abstract

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 datasets. In this research, we propose a framework which integrates omics-knowledge to identify suitable-reduced set of microbiome features for gaining insights into functional classification of metagenomic sequences. The proposed approach has been applied to two Use Case studies on: - (1) cattle rumen microbiota samples, differentiating nitrate and vegetable oil treated feed for improving cattle performance 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 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 as feature selector, proved to be most robust in our analysis.

Item Type:Book section
Keywords:Metagenomics, OTUs (Operational Taxonomic Units), Phylogeny, Machine Learning Classification
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute > Smart Environments
Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
ID Code:38405
Deposited By: Dr Huiru Zheng
Deposited On:04 Aug 2017 09:29
Last Modified:04 Aug 2017 09:29

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