Background: gene fusions derive from chromosomal rearrangements. The resulting chimeric transcripts are often endowed
with oncogenic potential. Furthermore, they serve as diagnostic tools for the clinical classification of cancer subgroups with
different prognosis and, in some cases, they can provide specific drug targets. To date, many efforts have been carried out to
study gene fusion events occurring in tumor samples. In recent years, the availability of a comprehensive next-generation
sequencing dataset for all existing human tumor cell lines has provided the opportunity to further investigate these data in
order to identify novel and still uncharacterized gene fusion events. Results: In our work, we have extensively reanalyzed
935 paired-end RNA-sequencing experiments downloaded from the Cancer Cell Line Encyclopedia repository, aiming at
addressing novel putative cell-line specific gene fusion events in human malignancies. The bioinformatics analysis has
been performed by the execution of four gene fusion detection algorithms. The results have been further prioritized by
running a Bayesian classifier that makes an in silico validation. The collection of fusion events supported by all of the
predictive software results in a robust set of ∼1,700 in silico predicted novel candidates suitable for downstream analyses.
Given the huge amount of data and information produced, computational results have been systematized in a database
named LiGeA. The database can be browsed through a dynamic and interactive web portal, further integrated with
validated data from other well-known repositories. Taking advantage of the intuitive query forms, the users can easily
access, navigate, filter, and select the putative gene fusions for further validations and studies. They can also find suitable
experimental models for a given fusion of interest. Conclusions: We believe that the LiGeA resource can represent not only
the first compendium of both known and putative novel gene fusion events in the catalog of all of the human malignant
cell lines but it can also become a handy starting point for wet-lab biologists who wish to investigate novel cancer
biomarkers and specific drug targets
