Forster, D. T. | bioRxiv Science

bioRxiv2025

A global genetic interaction map of a human cell reveals conserved principles of genetic networks

We generated a genome-scale, genetic interaction network from the analysis of more than 4 million double mutants in the haploid human cell line, HAP1. The network maps [~]90,000 genetic interactions, including thousands of extreme synthetic lethal and genetic suppression interactions. Genetic interaction profiles enabled assembly of a hierarchical model of cell function, including modules corresponding to protein complexes, pathways, biological processes, and cellular compartments. Comparative analyses showed that general principles of genetic networks are conserved from yeast to human cells. A genetic interaction network mapped in a single genetic background complements the DepMap gene co-essentiality network, recapitulating many of the same biological connections and also capturing unique functional information to reveal roles of uncharacterized genes and molecular determinants of specific cancer cell line genetic dependencies.

systems biology↗

bioRxiv2021

BIONIC: Biological Network Integration using Convolutions

Biological networks constructed from varied data, including protein-protein interactions, gene expression data, and genetic interactions can be used to map cellular function, but each data type has individual limitations such as bias and incompleteness. Network integration promises to address these limitations by combining and automatically weighting input information to obtain a more accurate and comprehensive representation of the underlying biology. However, existing network integration methods may fail to adequately scale to the number of nodes and networks present in genome-scale data, may perform poorly, and may not handle partial network overlap. To address these issues, we developed a deep learning-based network integration algorithm that incorporates a graph convolutional network (GCN) framework to effectively learn dependencies between any input network. Our method, BIONIC (Biological Network Integration using Convolutions), learns features which contain substantially more functional information compared to existing approaches, linking genes that share diverse functional relationships, including co-complex and shared bioprocess annotation. BIONIC can integrate networks in a fully unsupervised manner if functional gene annotations are not available, and it can also leverage available annotations in a semi-supervised manner. BIONIC is scalable in both size and quantity of the input networks, making it feasible to integrate numerous networks on the scale of the human genome. To demonstrate the utility of BIONIC in identifying novel biology, we predicted essential gene chemical-genetic interactions from a small set of diagnostic non-essential gene profiles in yeast, and experimentally validated these predictions. BIONIC correctly predicted many chemical-genetic interactions, and it correctly predicted genes that are required for proper {beta}-1,6-glucan synthesis as significant interactions with the bioactive compound pseudojervine.

bioinformatics↗