Expanding interactome analyses beyond model eukaryotes

Abstract

Interactome analyses have traditionally been applied to yeast, human and other model organisms due to the availability of protein–protein interaction data for these species. Recently, these techniques have been applied to more diverse species using computational interaction prediction from genome sequence and other data types. This review describes the various types of computational interactome networks that can be created and how they have been used in diverse eukaryotic species, highlighting some of the key interactome studies in non-model organisms.

Data Incompleteness May form a Hard-to-Overcome Barrier to Decoding Life’s Mechanism

“…A variety of biochemical, genetic, and cellular methods have been developed for mapping interactomes [ 10 , 44 , 58 ], which are often represented as networks (graphs), allowing for both visual and computational analyses of their structure and connectivity ([ 61 , 62 , 63 , 64 , 65 , 66 ] with references from [ 67 ]). Figure 2 schematically illustrates the information that emerges from the research on interactomes.…”

OMICS and Other Advanced Technologies in Mycological Applications

“…Among these omics approaches, proteomics [ 15 , 16 ], transcriptomics [ 17 ], metatranscriptomics [ 18 ], and metabolomics [ 19 ] have revolutionized the current understanding of the biological processes of fungi. In addition, more specialized omics methods such as ionomics, glycomics, glycoproteomics, glycogenomics [ 20 ], lipidomics [ 21 , 22 ], and interactomics [ 23 , 24 ] coupled with bioinformatics [ 25 , 26 ] can contribute to a greater understanding of fungal metabolism. The combination of omics approaches (multiomics) can be used to characterize fungal genomes and their metabolites, making multiomics approaches essential for detecting and characterizing novel metabolites with important biological properties, such as anticancer, antimicrobial, and antidiabetic for human health applications [ 27 , 28 , 29 ].…”