Network-Based Gene Function Prediction in Mouse and Other Model Vertebrates Using MouseNet Server

State of the Interactomes: an evaluation of molecular networks for generating biological insights

Advancements in genomic and proteomic technologies have powered the use of gene and protein networks ("interactomes") for understanding genotype-phenotype translation. However, the proliferation of interactomes complicates the selection of networks for specific applications. Here, we present a comprehensive evaluation of 46 current human interactomes, encompassing protein-protein interactions as well as gene regulatory, signaling, colocalization, and genetic interaction networks. Our analysis shows that large composite networks such as HumanNet, STRING, and FunCoup are most effective for identifying disease genes, while smaller networks such as DIP and SIGNOR demonstrate strong interaction prediction performance. These findings provide a benchmark for interactomes across diverse network biology applications and clarify factors that influence network performance. Furthermore, our evaluation pipeline paves the way for continued assessment of emerging and updated interaction networks in the future.

NGSEA: Network-Based Gene Set Enrichment Analysis for Interpreting Gene Expression Phenotypes with Functional Gene Sets

Gene set enrichment analysis (GSEA) is a popular tool to identify underlying biological processes in clinical samples using their gene expression phenotypes. GSEA measures the enrichment of annotated gene sets that represent biological processes for differentially expressed genes (DEGs) in clinical samples. GSEA may be suboptimal for functional gene sets; however, because DEGs from the expression dataset may not be functional genes per se but dysregulated genes perturbed by bona fide functional genes. To overcome this shortcoming, we developed network-based GSEA (NGSEA), which measures the enrichment score of functional gene sets using the expression difference of not only individual genes but also their neighbors in the functional network. We found that NGSEA outperformed GSEA in identifying pathway gene sets for matched gene expression phenotypes. We also observed that NGSEA substantially improved the ability to retrieve known anti-cancer drugs from patient-derived gene expression data using drug-target gene sets compared with another method, Connectivity Map. We also repurposed FDA-approved drugs using NGSEA and experimentally validated budesonide as a chemical with anti-cancer effects for colorectal cancer. We, therefore, expect that NGSEA will facilitate both pathway interpretation of gene expression phenotypes and anti-cancer drug repositioning. NGSEA is freely available at www.inetbio.org/ngsea.

MaizeNet: a co-functional network for network-assisted systems genetics in Zea mays

Maize (Zea mays) has multiple uses in human food, animal fodder, starch and sweetener production and as a biofuel, and is accordingly the most extensively cultivated cereal worldwide. To enhance maize production, genetic factors underlying important agricultural traits, including stress tolerance and flowering, have been explored through forward and reverse genetics approaches. Co-functional gene networks are systems biology resources useful in identifying trait-associated genes in plants by prioritizing candidate genes. Here, we present MaizeNet (http://www.inetbio.org/maizenet/), a genome-scale co-functional network of Z. mays genes, and a companion web server for network-assisted systems genetics. We describe the validation of MaizeNet network quality and its ability to functionally predict molecular pathways and complex traits in maize. Furthermore, we demonstrate that MaizeNet-based prioritization of candidate genes can facilitate the identification of cell wall biosynthesis genes and detect network communities associated with flowering-time candidate genes derived from genome-wide association studies. The demonstrated gene prioritization and subnetwork analysis can be conducted by simply submitting maize gene models based on the commonly used B73 RefGen_v3 and the latest B73 RefGen_v4 reference genomes on the MaizeNet web server. MaizeNet-based network-assisted systems genetics will substantially accelerate the discovery of trait-associated genes for crop improvement.