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Electronic supplementary material The online version of this article doi Keywords: Systems biology graphical notation, Neo4j, Graph database, Systems biology, Systems medicine Background When modeling in Systems Biology and in Systems Medicine, the resulting data is often extensive, complex and heterogeneous. A visual representation can support users in data analysis and interpretation [ 1 ]. However, the manual construction of such representations is a time-consuming task.
In addition, the manual exploration of the visualized networks may not even be feasible due to the size. Recently, standards emerged for the representation of biological models in a consistent and reusable manner.
The network for modeling in Computational Biology COMBINE [ 2 ] coordinates the development of such machine-readable standards and implements reliable and efficient model reuse. It is widely used in Systems Biology and fills the previous gap of standardized visual representations for biological networks. It creates detailed SBGN maps by representing hierarchical structures and biological complexes. It is the most elementary of the SBGN languages.
In this work, we consider only the PD and AF languages and propose to store biological networks as graph-oriented models in a graph database.
We take existing biological models represented in SBGN-ML files as input and convert them into a graph representation. The novelty of our work lies in the fact that, for the first time, SBGN maps are stored in a structured way and thus can be queried and compared to each other. Several studies show that graphs are realistic and well-suited for the representation of biological networks [ 9 , 10 ].
Employing graph databases to store and explore biological models requires less effort and offers new insights into analyses [ 11 ]. Our recent paper [ 12 ] describes the efficient storage of computational models in a graph database and informs on the improved interrogation of data relationships when represented as a graph.
One of the many interesting possibilities of applying the queries [ 13 ] is to highlight the important nodes in a network [ 14 ]. First, whilst a minority of the causal variants underlying these association signals are coding and therefore provide direct inference regarding the genes and proteins through which they act , most lie in regulatory sequence.
This makes assignment of their effector transcripts a non-trivial exercise and obscures the downstream mechanisms through which these variants impact T2D risk [ 8 — 10 ]. This challenge can increasingly be addressed through the integration of diverse sources of relevant data including a experimental data e.
The second challenge lies in the requirement to define functional relationships between sets of candidate effector transcripts in ways that are robust and, in particular, orthogonal to the data used to assign candidacy in the first place [ 13 , 14 ]. Solutions for the second challenge are less well-developed but generally involve some type of network analysis e.
However, recourse to co-expression information or functional pathway enrichment methods to generate and evaluate such networks runs the risk of introducing circularity, given that information on expression and function typically contributes whether explicitly or not to assignments of effector transcript candidacy. The use of protein-protein interaction data provides one possible solution to this conundrum [ 18 ].
In the present study, we make use of external protein-protein interaction data from the InWeb3 dataset [ 19 , 20 ] to evaluate and characterise the connectivity of the T2D candidate effector transcripts in terms of their ability to nucleate empirically confirmed interactions between their encoded proteins. Methods Positional candidacy score derivation We developed a framework to score the candidacy of genes mapping to GWAS association signals which aggregated data from multiple sources.
The information collected fell into two categories. First, we used regression-based approaches to link disease-associated variants most of which map into non-coding sequence and are therefore presumed to act through transcriptional regulation of nearby genes to their likely effector transcripts, using a combination of variant-based annotations and expression QTL data [ 21 ].
We then annotated the most associated variants in each interval using gene-based annotations for all genes in the interval from several sources. We assigned each variant a binary value based on whether it overlapped one of the discrete annotations for a gene in the interval exon, promoter, distal element. Second, we collected summary statistic expression QTL eQTL data from liver, skeletal muscle, whole blood, subcutaneous adipose and visceral adipose GTEx version 6 [ 21 ] and pancreatic islets [ 11 ].
Third, we calculated the distance of each variant to the TSS of each gene in the interval and assigned each variant the inverse TSS distance for each gene i.
Variants without values in the eQTL datasets were removed from the analysis. We then performed feature selection for each T2D locus separately using elastic net regression R package glmnet with the T2D p values as the outcome variable and binary genomic annotations exon, promoter, distal element , distance to TSS and cell type cis-eQTL p values for each gene in the interval as the predictor variables.
We also included minor allele frequency and imputation quality of each variant at the locus as predictor variables. We obtained the effects of features selected from the resulting model.
We applied a fold scaling factor to coding exon features, based on known enrichment of T2D variants in coding exons [ 25 , 26 ]. Where multiple features were selected for the same gene e. Both gene documents were converted into a word matrix. We used the resulting matrices to identify genes with functional attributes that indicated relevance to the T2D pathogenesis. Combining gene scores For each of the genes, we scaled scores from these two analyses to the sum of scores for each of the x genes at each locus resulting in a semantic score sg and variant link score vg.
To calculate a positional candidacy score PCS , we averaged the two scores and rescaled across all x genes at each locus. We performed network analyses using an updated version of InWeb3, a previously described comprehensive map of protein-protein interactions, containing , high-confidence interactions between 12, gene products compiled from a variety of sources [ 19 , 20 ].
APCST-like approaches have been widely used to solve network-design problems [ 28 — 30 ]. Network expansion is controlled by the balance between the benefits of adding a particular node increased connectivity between seed genes, driven by the collection of prizes vs. This allowed us to force each seed node in turn to be included in the network, in contrast to the default APCST method which initialises network construction from the nodes with higher weights.
This was reprojected onto the InWeb3 interactome to recover missing connections across nodes. As this final network represents a superposition of many different networks, linking nodes may sometimes appear at the periphery.
We assessed the specificity of each node in the final network by running the algorithm times with the same parameter settings, but with random input data. We define specificity in this context as the complement of the percentage with which a given seed or linking node from the final network appears in runs generated from random input data. For each random run, we selected, from the InWeb3 interactome, random seed nodes matching the binding degree distribution of the observed set of seeds, and assigned them the same prize value as the original.