Higher Order Molecular Organisation as a Source of Biological Function

Thomas Gaudelet, Noël Malod-Dognin and Nataša Pržulj

Corresponding author: Prof. N. Pržulj, natasa.przulj@mbzuai.ac.ae

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• Dataset: biological hypernetworks
• Result: functional predictions
• Hypergraphlets counter

Hypergraphlets counter

To compile, set the path to your local Boost installation in the Makefile, then run:

make

To run:

./run_hypercounter -g <hyperedge list> -o <output name> -t <threads> -b <blocks>

• -t: number of threads (OpenMP), default 1
• -b: number of vertex blocks distributed across the threads, default 50. Vertices are assigned to blocks at random, so densely connected vertices don’t end up in the same block.

Input: tab-separated hyperedge list. Each row starts with the hyperedge index (0 to m-1), followed by the vertices it contains (indexed 0 to n-1).

Output: .svml file with one row per vertex. The first and last numbers are the vertex index; in between are the orbit counts as orbit:count.

Citation

T. Gaudelet, N. Malod-Dognin and N. Pržulj. Higher-order molecular organization as a source of biological function. Bioinformatics 34(17), i944–i953 (2018). https://doi.org/10.1093/bioinformatics/bty570