Denoising inferred functional association networks obtained by gene fusion analysis

Abstract

BackgroundGene fusion detection – also known as the 'Rosetta Stone' method – involves the identification of fused composite genes in a set of reference genomes, which indicates potential interactions between its un-fused counterpart genes in query genomes. The precision of this method typically improves with an ever-increasing number of reference genomes.ResultsIn order to explore the usefulness and scope of this approach for protein interaction prediction and generate a high-quality, non-redundant set of interacting pairs of proteins across a wide taxonomic range, we have exhaustively performed gene fusion analysis for 184 genomes using an efficient variant of a previously developed protocol. By analyzing interaction graphs and applying a threshold that limits the maximum number of possible interactions within the largest graph components, we show that we can reduce the number of implausible interactions due to the detection of promiscuous domains. With this generally applicable approach, we generate a robust set of over 2 million distinct and testable interactions encompassing 696,894 proteins in 184 species or strains, most of which have never been the subject of high-throughput experimental proteomics. We investigate the cumulative effect of increasing numbers of genomes on the fidelity and quantity of predictions, and show that, for large numbers of genomes, predictions do not become saturated but continue to grow linearly, for the majority of the species. We also examine the percentage of component (and composite) proteins with relation to the number of genes and further validate the functional categories that are highly represented in this robust set of detected genome-wide interactions.ConclusionWe illustrate the phylogenetic and functional diversity of gene fusion events across genomes, and their usefulness for accurate prediction of protein interaction and function.

Gene Fusion Analysis in the Battle against the African Endemic Sleeping Sickness

“…Overall, we observed that the more proteomes we used for the comparison with T. brucei , the more fusion events were identified (Figure 2A). Additionally, the number of events found in each organism shows a positive correlation, in general, with the size of the proteome examined (Figure 2B), as has been described previously [9], [11], [12].…”

“…Several studies using this technique have been published, but most have focused on bacteria and fungi [9], [12], [26], [27], [28]. A preliminary analysis for T. brucei included only a small number of organisms [5], whereas in this study, we chose organisms so that they would represent every major lineage of the tree of life.…”

“…The backward BLAST process, used as a confirmation step for the fusion events [9]. According to this process, the two separate proteins found to participate in a fusion event, must correspond to the fused protein as the best reciprocal BLAST hit.…”

Protein Complexes Are under Evolutionary Selection to Assemble via Ordered Pathways

“…Essentially, the idea is that proteins that are encoded by different genes in one organism but fused together in another are very likely to physically interact, or at least be functionally related, when expressed as separate gene products. This has been supported by comprehensive analyses (Enright and Ouzounis, 2001; Yanai et al., 2001; Marcotte and Marcotte, 2002; Kamburov et al., 2007; Reid et al., 2010). …”

Knowledge-guided inference of domain–domain interactions from incomplete protein–protein interaction networks

“…In some cases, these co-occurring domains are fused together to form a single domain/protein in some reference organisms (Kamburov et al , 2007). In addition to deducing one-to-one DDI patterns, K-GIDDI was also designed to detect these one-to-many or many-to-many DDI patterns, which may be biologically meaningful interaction templates.…”