Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/97572
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Type: Journal article
Title: A reproducible approach to high-throughput biological data acquisition and integration
Author: Börnigen, D.
Moon, Y.
Rahnavard, G.
Waldron, L.
McIver, L.
Shafquat, A.
Franzosa, E.
Miropolsky, L.
Sweeney, C.
Morgan, X.
Garrett, W.
Huttenhower, C.
Citation: PeerJ, 2015; 3(3):e791-1-e791-35
Publisher: PeerJ
Issue Date: 2015
ISSN: 2167-8359
2167-8359
Statement of
Responsibility: 
Daniela Börnigen, Yo Sup Moon, Gholamali Rahnavard, Levi Waldron, Lauren McIver, Afrah Shafquat, Eric A. Franzosa, Larissa Miropolsky, Christopher Sweeney, Xochitl C. Morgan, Wendy S. Garrett, and Curtis Huttenhower
Abstract: Modern biological research requires rapid, complex, and reproducible integration of multiple experimental results generated both internally and externally (e.g., from public repositories). Although large systematic meta-analyses are among the most effective approaches both for clinical biomarker discovery and for computational inference of biomolecular mechanisms, identifying, acquiring, and integrating relevant experimental results from multiple sources for a given study can be time-consuming and error-prone. To enable efficient and reproducible integration of diverse experimental results, we developed a novel approach for standardized acquisition and analysis of high-throughput and heterogeneous biological data. This allowed, first, novel biomolecular network reconstruction in human prostate cancer, which correctly recovered and extended the NFκB signaling pathway. Next, we investigated host-microbiome interactions. In less than an hour of analysis time, the system retrieved data and integrated six germ-free murine intestinal gene expression datasets to identify the genes most influenced by the gut microbiota, which comprised a set of immune-response and carbohydrate metabolism processes. Finally, we constructed integrated functional interaction networks to compare connectivity of peptide secretion pathways in the model organisms Escherichia coli, Bacillus subtilis, and Pseudomonas aeruginosa.
Keywords: Data acquisition; Data integration; Heterogeneous data; High-throughput data; Meta-analysis; Reproducibility
Rights: © 2015 Boernigen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
RMID: 0030040724
DOI: 10.7717/peerj.791
Appears in Collections:Medicine publications

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