GeneSurrounder: network-based identification of disease genes in expression data

Abstract

Background A key challenge of identifying disease–associated genes is analyzing transcriptomic data in the context of regulatory networks that control cellular processes in order to capture multi-gene interactions and yield mechanistically interpretable results. One existing category of analysis techniques identifies groups of related genes using interaction networks, but these gene sets often comprise tens or hundreds of genes, making experimental follow-up challenging. A more recent category of methods identifies precise gene targets while incorporating systems-level information, but these techniques do not determine whether a gene is a driving source of changes in its network, an important characteristic when looking for potential drug targets. Results We introduce GeneSurrounder, an analysis method that integrates expression data and network information in a novel procedure to detect genes that are sources of dysregulation on the network. The key idea of our method is to score genes based on the evidence that they influence the dysregulation of their neighbors on the network in a manner that impacts cell function. Applying GeneSurrounder to real expression data, we show that our method is able to identify biologically relevant genes, integrate pathway and expression data, and yield more reproducible results across multiple studies of the same phenotype than competing methods. Conclusions Together these findings suggest that GeneSurrounder provides a new avenue for identifying individual genes that can be targeted therapeutically. The key innovation of GeneSurrounder is the combination of pathway network information with gene expression data to determine the degree to which a gene is a source of dysregulation on the network. By prioritizing genes in this way, our method provides insights into disease mechanisms and suggests diagnostic and therapeutic targets. Our method can be used to help biologists select among tens or hundreds of genes for further validation. The implementation in R is available at github.com/sahildshah1/gene-surrounder. Electronic supplementary material The online version of this article (10.1186/s12859-019-2829-y) contains supplementary material, which is available to authorized users.

Transcriptional Signatures and Network-Based Approaches Identified Master Regulators Transcription Factors Involved in Experimental Periodontitis Pathogenesis

“…Nonetheless, there are no experimental data for most contexts of interest, i.e., diseases and regulatory interactions are often inferred from expression data solely [31,32]. Therefore, one of the currently employed approaches to identify specific regulatory interactions that will allow a better mechanistic understanding of complex human diseases to find disease-related genes relies on examining transcriptomic data [33][34][35]. Using network-based approaches to determine disease-associated biological interactions has helped simplify the complexity and heterogeneity of biological systems by using specific computational methods [33,36,37].…”

“…Therefore, one of the currently employed approaches to identify specific regulatory interactions that will allow a better mechanistic understanding of complex human diseases to find disease-related genes relies on examining transcriptomic data [33][34][35]. Using network-based approaches to determine disease-associated biological interactions has helped simplify the complexity and heterogeneity of biological systems by using specific computational methods [33,36,37]. The networks representing transcriptional regulation in whole biological systems are gene regulatory networks (GRNs).…”

AutoCore: A network-based definition of the core module of human autoimmunity and autoinflammation

“…Our framework complements recent advancements in developing standardized vocabularies and international harmonization and exchange of patient data ( 40 , 76 , 77 ). More specific phenotypes will allow for the identification of more fine grained disease endotypes, which, in turn, will expedite genetic diagnosis by improving the accuracy of candidate gene prioritization ( 78 – 80 ) and drug repurposing methods ( 81 , 82 ). To facilitate the development of a data-driven, unified view of autoimmune/autoinflammatory diseases, the AutoCore and all data curated for this study are publicly available and can be interactively explored in a dedicated web app under https://menchelab.com/autocoreapp/ .…”

Explainable models using transcription factor binding and epigenome patterns at promoters reveal disease-associated genes and their regulators in the context of cell-types

“…Different computational methods use various strategies to associate genes with diseases. Some studies utilize the expression of the genes to link them to diseases [7][8][9]. However, most often, changes in the expression of genes due to diseases could be the consequence of the disease condition [10].…”