A unified web platform for network-based analyses of genomic data

A major bottleneck in network-based analyses of genomic data is quantitatively comparing biological signal in different networks and to identifying the optimal network dataset to answer a particular biological question. Towards these aims, we developed a unified web platform 9Broad Institute Web Platform for Genome Networks (GeNets)9, where users can compare biological signal of networks, and execute, store, and share network analyses. We designed a machine learningmachine-learning algorithm (Quack) which), which uses topological features to can quantify the overall and pathway-specific biological signals in networks, thus enabling users to choose the optimal network dataset for their analyses. We illustrated a typical workflow using GeNets to identify interesting autism candidate genes in the network that, when compared to four other networks, best recapitulates established neurodevelopmental pathway information. GeNets is a scalable, general and uniquely enabling computational framework for analyzing, managing and sharing analyses of genetic datasets using heterogeneous functional genomics networks, for example, from single-cell transcriptional analyses.