Tools

We created an easy-to-use app for network-based analysis of GWAS called Magnum. Currently, the app allows identifying trait-relevant networks for a given GWAS using the connectivity enrichment analysis described in our paper. In addition to our network compendium, users can also load their own networks and GWAS data.

Magnum can be used as an intuitive desktop app or a command-line tool suitable for running on a computing cluster. It runs locally and can thus be used with sensitive GWAS data.

Download the appropriate package and follow the instructions of the installer or the README file. For further information, see the Documentation. Magnum is distributed as free open-source software under the MIT License. Note that:

Desktop app

Command-line tool

Networks

Release 1.0 of the network compendium consists of:

Refer to the included README files and our paper for information on each type of network.

This is the largest collection of tissue-specific transcriptional regulatory networks for human to date. We plan to further expand coverage using data from the Roadmap Epigenomics project and future releases of FANTOM. Subscribe to our mailing list to receive updates.

Due to the large file size for download, the 394 tissue-specific networks are provided separately from the remaining compendium. We recommend that you first explore the network compendium, which includes the 32 high-level regulatory networks, before downloading the full set of 394 individual networks.

The network compendium is mirrored on our web server and an external repository for open biomedical research (Synapse).

Supplementary data and code

We provide here the data and code used to infer and validate the tissue-specific regulatory networks. Data are provided in two archives containing:

  1. Gene-level summary statistics for 37 GWASs.
  2. Input and validation data including the FANTOM5 promoter and enhancer maps, the TF motifs, genome-wide locations of motif instances, genome annotation used to build networks, ENCODE ChIP-seq data, GTEx eQTLs, and Roadmap epigenomics RNA-seq data.

Public data are included only to ensure reproducibility of our results. Each directory contains a README file with links to the original data sources. Please credit and download the latest versions of public data from the original sources for your work.