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dc.contributor.authorPromponas, Vasilis J.en
dc.contributor.authorOuzounis, Christos A.en
dc.contributor.authorIliopoulos, Ioannis Creteen
dc.creatorPromponas, Vasilis J.en
dc.creatorOuzounis, Christos A.en
dc.creatorIliopoulos, Ioannis Creteen
dc.subjectMolecular biologyen
dc.subjectData processingen
dc.titleExperimental evidence validating the computational inference of functional associations from gene fusion events: a critical surveyen
dc.typeinfo:eu-repo/semantics/articleΣχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied SciencesΤμήμα Βιολογικών Επιστημών / Department of Biological Sciences
dc.description.notes<p>ID: 745en
dc.description.notesIn: Briefings in bioinformatics volume 15 issue 3 page 443.en
dc.description.notesSummary: More than a decade ago, a number of methods were proposed for the inference of protein interactions, using whole-genome information from gene clusters, gene fusions and phylogenetic profiles. This structural and evolutionary view of entire genomes has provided a valuable approach for the functional characterization of proteins, especially those without sequence similarity to proteins of known function. Furthermore, this view has raised the real possibility to detect functional associations of genes and their corresponding proteins for any entire genome sequence. Yet, despite these exciting developments, there have been relatively few cases of real use of these methods outside the computational biology field, as reflected from citation analysis. These methods have the potential to be used in high-throughput experimental settings in functional genomics and proteomics to validate results with very high accuracy and good coverage. In this critical survey, we provide a comprehensive overview of 30 most prominent examples of single pairwise protein interaction cases in small-scale studies, where protein interactions have either been detected by gene fusion or yielded additional, corroborating evidence from biochemical observations. Our conclusion is that with the derivation of a validated gold-standard corpus and better data integration with big experiments, gene fusion detection can truly become a valuable tool for large-scale experimental biology.</p>en
dc.contributor.orcidPromponas, Vasilis J. [0000-0003-3352-4831]

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