Network reconstruction based on proteomic data and prior knowledge of protein connectivity using graph theory

A strategy to incorporate prior knowledge into correlation network cutoff selection

Correlation networks are frequently used to statistically extract biological interactions between omics markers. Network edge selection is typically based on the statistical significance of the correlation coefficients. This procedure, however, is not guaranteed to capture biological mechanisms. We here propose an alternative approach for network reconstruction: a cutoff selection algorithm that maximizes the overlap of the inferred network with available prior knowledge. We first evaluate the approach on IgG glycomics data, for which the biochemical pathway is known and well-characterized. Importantly, even in the case of incomplete or incorrect prior knowledge, the optimal network is close to the true optimum. We then demonstrate the generalizability of the approach with applications to untargeted metabolomics and transcriptomics data. For the transcriptomics case, we demonstrate that the optimized network is superior to statistical networks in systematically retrieving interactions that were not included in the biological reference used for optimization.

HRGRN: A Graph Search-Empowered Integrative Database of Arabidopsis Signaling Transduction, Metabolism and Gene Regulation Networks

The biological networks controlling plant signal transduction, metabolism and gene regulation are composed of not only tens of thousands of genes, compounds, proteins and RNAs but also the complicated interactions and co-ordination among them. These networks play critical roles in many fundamental mechanisms, such as plant growth, development and environmental response. Although much is known about these complex interactions, the knowledge and data are currently scattered throughout the published literature, publicly available high-throughput data sets and third-party databases. Many 'unknown' yet important interactions among genes need to be mined and established through extensive computational analysis. However, exploring these complex biological interactions at the network level from existing heterogeneous resources remains challenging and time-consuming for biologists. Here, we introduce HRGRN, a graph search-empowered integrative database of Arabidopsis signal transduction, metabolism and gene regulatory networks. HRGRN utilizes Neo4j, which is a highly scalable graph database management system, to host large-scale biological interactions among genes, proteins, compounds and small RNAs that were either validated experimentally or predicted computationally. The associated biological pathway information was also specially marked for the interactions that are involved in the pathway to facilitate the investigation of cross-talk between pathways. Furthermore, HRGRN integrates a series of graph path search algorithms to discover novel relationships among genes, compounds, RNAs and even pathways from heterogeneous biological interaction data that could be missed by traditional SQL database search methods. Users can also build subnetworks based on known interactions. The outcomes are visualized with rich text, figures and interactive network graphs on web pages. The HRGRN database is freely available at http://plantgrn.noble.org/hrgrn/.