Predictive and comparative network analysis of the gut microbiota in type 2 diabetes

Mostafa M. Abbas, Yasser El-Manzalawy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Metagenome-wide analysis studies provide a unique set of microbial features for biomarker discovery of associated disease as well as for studying diversity and dynamics of microbial communities under different conditions. Taxonomic classification of microbes in metagenomic samples quantifies what microbes are present and in what proportion. Despite the availably of several computational taxonomy profiling methods, this crucial step in metagenome-wide analysis remains very challenging and how using different taxonomy profiling methods might influence the outcome of the analysis is not wellstudied. In this work, we consider three taxonomy profiling methods (MetaPhlAn2, Kraken, and EBI metagenomics pipeline) and examine their effect on the outcome of metagenome-wide analysis based on machine learning and comparative network approaches. Our results suggest that Kraken OTU-based data representation yields the best performing classifiers even using less number of features (e.g., OTUs). In addition, our preliminary results underscore the viability of leveraging multiple taxonomic classification methods in microbial network analysis. Finally, our analysis results are consistent with the current knowledge and reveal novel insights into interaction relationships between potential biomarkers in the gut microbiome associated with T2D.

Original languageEnglish (US)
Title of host publicationACM-BCB 2017 - Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics
PublisherAssociation for Computing Machinery, Inc
Pages313-320
Number of pages8
ISBN (Electronic)9781450347228
DOIs
StatePublished - Aug 20 2017
Event8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2017 - Boston, United States
Duration: Aug 20 2017Aug 23 2017

Publication series

NameACM-BCB 2017 - Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

Conference

Conference8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2017
CountryUnited States
CityBoston
Period8/20/178/23/17

All Science Journal Classification (ASJC) codes

  • Software
  • Biomedical Engineering
  • Health Informatics
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Predictive and comparative network analysis of the gut microbiota in type 2 diabetes'. Together they form a unique fingerprint.

  • Cite this

    Abbas, M. M., & El-Manzalawy, Y. (2017). Predictive and comparative network analysis of the gut microbiota in type 2 diabetes. In ACM-BCB 2017 - Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (pp. 313-320). (ACM-BCB 2017 - Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics). Association for Computing Machinery, Inc. https://doi.org/10.1145/3107411.3107472