GeNets: a unified web platform for network-based genomic analyses

Abstract

Functional genomics networks are widely used to identify unexpected pathway relationships in large genomic datasets. However, it is challenging to compare the signal-to-noise ratios of different networks and to identify the optimal network with which to interpret a particular genetic dataset. We present GeNets, a platform in which users can train a machine-learning model (Quack) to carry out these comparisons and execute, store, and share analyses of genetic and RNA-sequencing datasets.

Figure 1 |. Features of the GeNets web platform.

a) GeNets overview. b) AUCs of five heterogeneous networks as determined by the Quack machine-learning algorithm. Each model was trained on N = 597 pathways (70% of the 853 curated MSigDB pathways). c) Local biological signal of five networks (rows) across 730 pathways (columns). Colors as indicated in the color key and cells are blank if genes in a pathway were not covered by enough connections in the network in question for Quack to determine an AUC. Interactive view with all pathway names and more details is available from the GeNets Dashboard.

Figure 2 |. Using GeNets to explore pathways implicated in autism spectrum disorders (ASD).

a) Heat map of the local biological signal of the five networks across neurological and neurodevelopmental pathways [determined by training network-specific Quack models]. b) AUC distributions of neurological pathways represented in the five networks. Only pathways with enough connections for Quack to determine an AUC are included, and their numbers are indicated in each network. Center line, median; box limits, upper and lower quartiles; whiskers, 1.5× interquartile range; points, outliers. c) Direct protein-protein interactions (from InWeb) between 65 genes implicated in ASD (where only genes with interactions are shown). Upon clicking the edges in GeNets, there is a direct link to the publication supporting the relevant data exemplified here with the SYNGAP1-GRIN2B protein interaction. The bottom box illustrates information available upon mouse over of genes in the network exemplified with CHD8. d) Thirty-one potential autism candidate proteins (green) based on protein-protein interactions to 65 ASD input genes (light blue) after training of a neurodevelopmental-specific Quack Model. Darker green means higher confidence candidate as indicated.