Ontological discovery environment: A system for integrating gene–phenotype associations

Abstract

The wealth of genomic technologies has enabled biologists to rapidly ascribe phenotypic characters to biological substrates. Central to effective biological investigation is the operational definition of the process under investigation. We propose an elucidation of categories of biological characters, including disease relevant traits, based on natural endogenous processes and experimentally observed biological networks, pathways and systems rather than on externally manifested constructs and current semantics such as disease names and processes. The Ontological Discovery Environment (ODE) is an Internet accessible resource for the storage, sharing, retrieval and analysis of phenotype-centered genomic data sets across species and experimental model systems. Any type of data set representing gene-phenotype relationships, such quantitative trait loci (QTL) positional candidates, literature reviews, microarray experiments, ontological or even meta-data, may serve as inputs. To demonstrate a use case leveraging the homology capabilities of ODE and its ability to synthesize diverse data sets, we conducted an analysis of genomic studies related to alcoholism. The core of ODE’s gene-set similarity, distance and hierarchical analysis is the creation of a bipartite network of gene-phenotype relations, a unique discrete graph approach to analysis that enables set-set matching of non-referential data. Gene sets are annotated with several levels of metadata, including community ontologies, while gene set translations compare models across species. Computationally derived gene sets are integrated into hierarchical trees based on gene-derived phenotype interdependencies. Automated set identifications are augmented by statistical tools which enable users to interpret the confidence of modeled results. This approach allows data integration and hypothesis discovery across multiple experimental contexts, regardless of the face similarity and semantic annotation of the experimental systems or species domain.

Potential translational targets revealed by linking mouse grooming behavioral phenotypes to gene expression using public databases

“…To complement these analyses, the Mouse Genome Informatics (MGI) database (Shaw, 2009) provided aberrant phenotypes of genetically modified mice (relevant for each of the candidate genes), allowing additional insight into gene-behavior interactions for the group of candidate genes identified in our study (Table 3). Protein-specific BLAST searches enabled further comparison of the homology between candidate mouse gene products and their human orthologs (Table 3), and the Ontological Discovery Environment (ODE) (Baker et al, 2009) and Drug Related Gene Database (DRG) (Gardner et al, 2008) were used to further search published gene expression studies linking candidate genes to mouse neural phenotypes. As shown in Table 3, bioinformatics-based analysis of the 31 genes present in both mice and humans revealed several interesting patterns, including 4 genes that encode tubulin-associated proteins ( Tubgcp4, Ttl, Racgap and Mapre1 ), and 5 genes related to either actin or myosin ( Pdgfb, Myo1b, Cdh1, Myo7a and Parvg ).…”

Identification of a QTL in Mus musculus for Alcohol Preference, Withdrawal, and Ap3m2 Expression Using Integrative Functional Genomics and Precision Genetics

“…This system has been previously described in detail (Baker et al 2009(Baker et al , 2012. GeneWeaver tools each operate on bipartite graphs with two sets of vertices, or nodes, representing genes and the gene sets to which they belong (Baker et al 2009).…”

“…This system has been previously described in detail (Baker et al 2009(Baker et al , 2012. GeneWeaver tools each operate on bipartite graphs with two sets of vertices, or nodes, representing genes and the gene sets to which they belong (Baker et al 2009). This data-intensive approach allows for the matching of many genes to many phenotypes through a scalable maximal biclique enumeration algorithm (Zhang et al 2014).…”

“…The HiSim tool constructs the hierarchical network of all set-set intersections, and the HiSim Graph tool organizes gene sets based on the genes that they contain, presenting a network graph of hierarchical intersections among a collection of input gene sets and enabling discovery of the relationships among these sets. The network was produced from the overlap of maximal bicliques in discrete bipartite structures, and a directed acyclic graph of intersecting gene sets was produced (Baker et al 2009(Baker et al , 2012Zhang et al 2014). Default options were used, such that bootstrapping of branching was performed, homology was used to integrate gene overlap across species, and the minimum number of intersecting genes required to produce a node was set at 1.…”

“…The diversity and intensiveness of statistical procedures required to re-analyze primary data and the cross-platform and cross-species data integration required for matching identifiers across various experimental platforms place the process out of reach for widespread application. We designed the GeneWeaver software system (Baker et al 2009(Baker et al , 2012 to address these problems by enabling users to integrate their own data with large numbers of experimental results across multiple species using combinatorial algorithms in a real-time, web-based environment. GeneWeaver contains multiple streams of genomic data, including ontological annotations from Generic Model Organism Databases, QTL positional candidates, transcriptomic results from published experimental studies, user-submitted functional genomics data sets, GWAS candidates, literature annotation, and other strategies.…”