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BLOBFISH: Bipartite Limited Subnetworks from Multiple Observations using Breadth-First Search with Constrained Hops
Tara Eicher1, Marouen Ben Guebila1, John Quackenbush1,2
1Department of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, MA, 02115, USA.
Motivation:
In analyzing biological network models, such as gene regulatory networks, a common question is how members of a particular set of genes are connected. For example, one might want to explore network relationships between a set of differentially expressed genes, a gene set previously reported in the literature, or elements of one or more pathways. BLOBFISH uses a breadth-first search algorithm adapted to bipartite graphs to identify a compact subnetwork connecting the members of a pre-specified set of genes, providing a regulatory context that can shed light on specific mechanisms involved in a phenotype and its development.
Results:
We demonstrate the use of BLOBFISH to extract connected subnetworks between candidate nodes in and gene regulatory and eQTL networks reflecting tissue specificity using publicly available data from the Genotype Tissue Expression (GTEx) project.
Availability:
Source code is available from GR as part of the netZooR R package (v1.6) (https://github.com/netZoo/netZooR). Replication scripts are available from https://github.com/QuackenbushLab/BLOBFISH_paper_scripts. eQTL networks are available from Zenodo (doi: 10.5281/zenodo.20820178). LIONESS networks are available from GRAND (https://grand.networkmedicine.org/tissues/).
Supplementary Information:
Supplementary data are available at Bioinformatics online.
