A reproducible approach to high-throughput biological data acquisition and integration

作者：Boernigen Daniela; Moon Yo Sup; Rahnavard Gholamali; Waldron Levi; McIver Lauren; Shafquat Afrah; Franzosa Eric A; Miropolsky Larissa; Sweeney Christopher; Morgan Xochitl C; Garrett Wendy S; Huttenhower Curtis^*

来源：PeerJ, 2015, 3: e791.

DOI：10.7717/peerj.791

• High-throughput data
• Data integration
• Data acquisition
• Meta-analysis
• Heterogeneous data
• Reproducibility

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 NF kappa B 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.

• 出版日期2015-3-31
• 单位MIT