Gene fusion analysis in the battle against the African endemic sleeping sickness

Molecular fusion events in carcinogenic organisms: a bioinformatics study for the detection of fused proteins between viruses, bacteria and eukaryotes

Molecular fusion events have a prominent role in the initial steps of carcinogenesis. In this study, a bioinformatics analysis was performed between four organisms that are known to induce cancer development in humans: two viruses, Human Herpesvirus 4, and Human T-cell leukaemia virus, one bacterium, Helicobacter Pylori, and one trematode, Schistosoma mansoni. The annotated proteomes from these organisms were analysed using the SAFE software to identify protein fusion events, which may provide insight into protein function similarities and possible merging events during the course of evolution. Based on the results, five fused proteins with very similar functions were detected, whereas proteins with different functions that might act in the same molecular complex or biochemical pathway were not found. Thus, this study analysed the above four well-known cancer-related organisms with de novo bioinformatics programs and provided useful information on protein fusion events, hopefully leading to deeper understanding of carcinogenenesis.

Systematic identification and analysis of frequent gene fusion events in metabolic pathways

Background

Gene fusions are the most powerful type of in silico-derived functional associations. However, many fusion compilations were made when <100 genomes were available, and algorithms for identifying fusions need updating to handle the current avalanche of sequenced genomes. The availability of a large fusion dataset would help probe functional associations and enable systematic analysis of where and why fusion events occur.

Results

Here we present a systematic analysis of fusions in prokaryotes. We manually generated two training sets: (i) 121 fusions in the model organism Escherichia coli; (ii) 131 fusions found in B vitamin metabolism. These sets were used to develop a fusion prediction algorithm that captured the training set fusions with only 7 % false negatives and 50 % false positives, a substantial improvement over existing approaches. This algorithm was then applied to identify 3.8 million potential fusions across 11,473 genomes. The results of the analysis are available in a searchable database at http://modelseed.org/projects/fusions/. A functional analysis identified 3,000 reactions associated with frequent fusion events and revealed areas of metabolism where fusions are particularly prevalent.

Conclusions

Customary definitions of fusions were shown to be ambiguous, and a stricter one was proposed. Exploring the genes participating in fusion events showed that they most commonly encode transporters, regulators, and metabolic enzymes. The major rationales for fusions between metabolic genes appear to be overcoming pathway bottlenecks, avoiding toxicity, controlling competing pathways, and facilitating expression and assembly of protein complexes. Finally, our fusion dataset provides powerful clues to decipher the biological activities of domains of unknown function.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2782-3) contains supplementary material, which is available to authorized users.