Arete – candidate gene prioritization using biological network topology with additional evidence types

Abstract

BackgroundRefinement of candidate gene lists to select the most promising candidates for further experimental verification remains an essential step between high-throughput exploratory analysis and the discovery of specific causal genes. Given the qualitative and semantic complexity of biological data, successfully addressing this challenge requires development of flexible and interoperable solutions for making the best possible use of the largest possible fraction of all available data.ResultsWe have developed an easily accessible framework that links two established network-based gene prioritization approaches with a supporting isolation forest-based integrative ranking method. The defining feature of the method is that both topological information of the biological networks and additional sources of evidence can be considered at the same time. The implementation was realized as an app extension for the Cytoscape graph analysis suite, and therefore can further benefit from the synergy with other analysis methods available as part of this system.ConclusionsWe provide efficient reference implementations of two popular gene prioritization algorithms – DIAMOnD and random walk with restart for the Cytoscape system. An extension of those methods was also developed that allows outputs of these algorithms to be combined with additional data. To demonstrate the utility of our software, we present two example disease gene prioritization application cases and show how our tool can be used to evaluate these different approaches.Electronic supplementary materialThe online version of this article (doi:10.1186/s13040-017-0141-9) contains supplementary material, which is available to authorized users.

Emerging landscape of molecular interaction networks: Opportunities, challenges and prospects

“…New approaches like Hybrid-Ranker exploit topological properties to prioritize genes based on their proximity to the causal genes of a particular disease of interest and information on its corresponding co-morbid disease (Razaghi-Moghadam and Nikoloski 2020 ). Arete is a similar tool incorporated as an app in the Cytoscape graph analysis suite (Lysenko et al 2017 ). Novel gene prioritization tools like GenePANDA (Yin et al 2017 ) and TopControl (Nazarieh and Helms 2019 ) use additional features like the relative distance of the candidate disease gene to the known disease genes and dominating sets on co-regulatory networks instead of high-degree nodes.…”

“…Dynamic graphical model frameworks are developed for comprehensive analyses of tumour heterogeneity by integrating different genome-level datasets. These frameworks are useful to understand the role of mutations in conferring heterogeneity at different stages of cancer progression and additional complexities in tumour evolution (Lysenko et al 2017 ). Pathway topology: In the current era of precision medicine, a system-wide pathway-level understanding plays a crucial role.…”

Discovery of host-directed modulators of virus infection by probing the SARS-CoV-2-host protein-protein interaction network

“…We used a network-based protein prioritisation approach, the random walk with restart (RWR) (19), to identify host protein targets closest to the VIP protein target set. A random walk is a stochastic process, where the steps on the network take place with a certain probability.…”

A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases

“…In order to address this problem, various network propagation methods, modeling information flow over the network have been developed. ToppNet [54], GeneWanderer [55], PhenoRank [31] and many others [56], [57], [58], [59], [60], [61], [62] apply random walk-based algorithms [63], [64], [65] in order to assess relative importance of a node to a group nodes considering the global network topology. Other methods mathematically related [66] with random walk, modelling diffusion [67], [68], [69] or electric current flow [70] through the network have been used successfully in gene prioritization.…”