Title | Transkingdom Networks: A Systems Biology Approach to Identify Causal Members of Host-Microbiota Interactions. |
Publication Type | Journal Article |
Year of Publication | 2018 |
Authors | Rodrigues, RR, Shulzhenko, N, Morgun, A |
Journal | Methods Mol Biol |
Volume | 1849 |
Pagination | 227-242 |
Date Published | 2018 |
ISSN | 1940-6029 |
Keywords | Animals, Computational Biology, Gene Regulatory Networks, Host Microbial Interactions, Humans, Microbiota, Systems Biology |
Abstract |
Improvements in sequencing technologies and reduced experimental costs have resulted in a vast number of studies generating high-throughput data. Although the number of methods to analyze these "omics" data has also increased, computational complexity and lack of documentation hinder researchers from analyzing their high-throughput data to its true potential. In this chapter we detail our data-driven, transkingdom network (TransNet) analysis protocol to integrate and interrogate multi-omics data. This systems biology approach has allowed us to successfully identify important causal relationships between different taxonomic kingdoms (e.g., mammals and microbes) using diverse types of data.
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DOI | 10.1007/978-1-4939-8728-3_15 |
Alternate Journal | Methods Mol Biol |
PubMed ID | 30298258 |
PubMed Central ID | PMC6557635 |
Grant List | R01 DK103761 / DK / NIDDK NIH HHS / United States U01 AI109695 / AI / NIAID NIH HHS / United States |