Network-Based Interpretation of Diverse High-Throughput Datasets through the Omics Integrator Software Package

Abstract

High-throughput, ‘omic’ methods provide sensitive measures of biological responses to perturbations. However, inherent biases in high-throughput assays make it difficult to interpret experiments in which more than one type of data is collected. In this work, we introduce Omics Integrator, a software package that takes a variety of ‘omic’ data as input and identifies putative underlying molecular pathways. The approach applies advanced network optimization algorithms to a network of thousands of molecular interactions to find high-confidence, interpretable subnetworks that best explain the data. These subnetworks connect changes observed in gene expression, protein abundance or other global assays to proteins that may not have been measured in the screens due to inherent bias or noise in measurement. This approach reveals unannotated molecular pathways that would not be detectable by searching pathway databases. Omics Integrator also provides an elegant framework to incorporate not only positive data, but also negative evidence. Incorporating negative evidence allows Omics Integrator to avoid unexpressed genes and avoid being biased toward highly-studied hub proteins, except when they are strongly implicated by the data. The software is comprised of two individual tools, Garnet and Forest, that can be run together or independently to allow a user to perform advanced integration of multiple types of high-throughput data as well as create condition-specific subnetworks of protein interactions that best connect the observed changes in various datasets. It is available at http://fraenkel.mit.edu/omicsintegrator and on GitHub at https://github.com/fraenkel-lab/OmicsIntegrator.

Hepatic Dysfunction Caused by Consumption of a High-Fat Diet

“…To obtain a more comprehensive view of the data, we expanded upon an established network modeling algorithm called the prize-collecting Steiner forest (PCSF) (Tuncbag et al, 2013, 2016). We built a combined protein-protein and protein-metabolite interactome from the iRefIndex (v.13) database (Razick et al, 2008) for protein-protein interactions and obtained protein-metabolite interactions from the Human Metabolome Database (HMDB; v.3.6) (Wishart et al, 2013) and the human metabolic reconstruction Recon 2 (v.3) (Thiele et al, 2013).…”

“…To integrate all the omic datasets we collected, we built on the established PCSF network modeling approach (Tuncbag et al, 2013, 2016). The PCSF method is not required to include all omic data yet is capable of introducing predicted nodes that are critical for establishing connections between the detected molecules.…”

“…Briefly, the prize-collecting Steiner forest (Tuncbag et al, 2013, 2016) aims to find a forest F ( V F , E F ) from the graph G(V, E, c(e), p(v)) , with nodes V , edges E , edge costs c(e) ≥ 0, and node prizes p(v) for v ∈ V , that minimizes the objective function: $$\mathrm{italicPCSF}false(Ffalse)=true\sum _{v\notin V_F}pfalse(vfalse)+true\sum _{e\in E_F}cfalse(efalse)+\omega \cdot \kappa ,$$ where κ represents the number of trees in the forest, ω represents a tuning parameter that influences the number of trees included in the final forest, and: $$pfalse(vfalse)=\beta \cdot p_{o}false(vfalse)-\mu \cdot \text{degree}{false(vfalse)}^n$$…”

“…We identified genes, proteins, and metabolites altered between CD and HFD and jointly analyzed our epigenomic and transcriptomic data to predict transcriptional regulators that likely influence gene expression changes between the diets. We then developed a network modeling approach based on the prize-collecting Steiner forest (PCSF) algorithm (Tuncbag et al, 2013, 2016) to analyze all the omic data in the context of known protein-protein and protein-metabolite interactions. For this purpose, we constructed a vast interactome of such associations and developed computational methods to avoid biases from well-studied, highly connected proteins and metabolites.…”

Synthesizing Signaling Pathways from Temporal Phosphoproteomic Data

“…We use the prize-collecting Steiner forest implementation from Omics Integrator (Tuncbag et al, 2016) nominates pathway members that are not detected by the mass spectrometry but form critical pathway connections to phosphorylated proteins, like ABL2 and AKT1 in our EGF response study ( Figure 5). The Supplemental Experimental Procedures describe how we set these parameters, ran PCSF multiple times to identify parallel connections between proteins, generated prizes from the phosphoproteomic data, and created a weighted interaction network .…”