Abstract
Genetically engineered mouse models are used in high-throughput phenotyping screens to understand genotype-phenotype associations and their relevance to human diseases. However, not all mutant mouse lines with detectable phenotypes are associated with human diseases. Here, we propose the “Target gene selection system for Genetically engineered mouse models” (TarGo). Using a combination of human disease descriptions, network topology, and genotype-phenotype correlations, novel genes that are potentially related to human diseases are suggested. We constructed a gene interaction network using protein-protein interactions, molecular pathways, and co-expression data. Several repositories for human disease signatures were used to obtain information on human disease-related genes. We calculated disease- or phenotype-specific gene ranks using network topology and disease signatures. In conclusion, TarGo provides many novel features for gene function prediction.
Highlights
In the post-genome era, the functional analysis of protein-coding genes remains an important goal and a major challenge for the field of biology
We evaluated the resulting prioritized genes according to the known genotype-phenotype associations contained in Mouse Genome Informatics (MGI)
Construction of mouse network The gene interaction network was composed of three molecular interaction databases; 377,473 protein-protein interactions (PPIs) from NCBI GeneRIF, 88,279 pathways from Pathway Commons, and 882,705 Co-expression data from HumanNet [19,20,21]
Summary
In the post-genome era, the functional analysis of protein-coding genes remains an important goal and a major challenge for the field of biology. The International Mouse Phenotyping Consortium (IMPC) started generating knockout mice for every mouse gene and collecting phenotyping data for each null mutation [1] To accelerate this goal, a targeted gene selection system for GEM models was deemed necessary. Hyung et al Laboratory Animal Research (2019) 35:23 integration method are the dependence on pre-constructed datasets and non-optimal filtering strategies for false positives. The goal of those previous approaches is gene function estimation, but limitations are remained. We predicted mouse gene function with molecular interactions and sorting relationship for phenotype (or disease) signature genes. The Web server is available at http://combio.snu.ac.kr/targo
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