Weighill, D. | bioRxiv Science

bioRxiv2021

GRAND: A database of gene regulatory network models across human conditions

Gene regulation plays a fundamental role in shaping tissue identity, function, and response to perturbation. Regulatory processes are controlled by complex networks of interacting elements, including transcription factors, miRNAs and their target genes. The structure of these networks helps to determine phenotypes and can ultimately influence the development of disease or response to therapy. We developed GRAND (https://grand.networkmedicine.org) as a database for gene regulatory network models that can be compared between biological states, or used to predict which drugs produce changes in regulatory network structure. The database includes 12,468 genome-scale networks covering 36 human tissues, 28 cancers, 1,378 unperturbed cell lines, as well as 173,013 TF and gene targeting scores for 2,858 small molecule-induced cell line perturbation paired with phenotypic information. GRAND allows the networks to be queried using phenotypic information and visualized using a variety of interactive tools. In addition, it includes a web application that matches disease states to potentially therapeutic small molecule drugs using regulatory network properties. Graphical abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY Modeling gene regulation across human conditions integrates cancer tissues and cell lines, small molecules, and normal tissue networks. C_FIG_DISPLAY

genomics↗

bioRxiv2020

Gene Regulatory Network Inference as Relaxed Graph Matching

Gene regulatory network inference is instrumental to the discovery of genetic mechanisms driving diverse diseases, including cancer. Here, we present a theoretical framework for PANDA, an established method for gene regulatory network inference. PANDA is based on iterative message passing updates that resemble the gradient descent of an optimization problem, OTTER, which can be interpreted as relaxed inexact graph matching between a gene-gene co-expression and a protein-protein interaction matrix. The solutions of OTTER can be derived explicitly and inspire an alternative spectral algorithm, for which we can provide network recovery guarantees. We compare different solution approaches of OTTER to other inference methods using three biological data sets, which we make publicly available to offer a new application venue for relaxed graph matching in gene regulatory network inference. We find that using modern gradient descent methods with superior convergence properties solving OTTER outperforms state-of-the-art gene regulatory network inference methods in predicting binding of transcription factors to regulatory regions.

bioinformatics↗