The advent of RNA-seq technologies has switched the paradigm of genetic analysis from a genome
to a transcriptome-based perspective. Alternative splicing generates functional diversity in genes,
but the precise functions of many individual isoforms are yet to be elucidated. Gene Ontology was
developed to annotate gene products according to their biological processes, molecular functions and
cellular components. Despite a single gene may have several gene products, most annotations are not
isoform-specifc and do not distinguish the functions of the diferent proteins originated from a single
gene. Several approaches have tried to automatically annotate ontologies at the isoform level, but
this has shown to be a daunting task. We have developed ISOGO (ISOform+GO function imputation),
a novel algorithm to predict the function of coding isoforms based on their protein domains and their
correlation of expression along 11,373 cancer patients. Combining these two sources of information
outperforms previous approaches: it provides an area under precision-recall curve (AUPRC) fve times
larger than previous attempts and the median AUROC of assigned functions to genes is 0.82. We tested
ISOGO predictions on some genes with isoform-specifc functions (BRCA1, MADD,VAMP7 and ITSN1)
and they were coherent with the literature. Besides, we examined whether the main isoform of each
gene -as predicted by APPRIS- was the most likely to have the annotated gene functions and it occurs
in 99.4% of the genes. We also evaluated the predictions for isoform-specifc functions provided by
the CAFA3 challenge and results were also convincing. To make these results available to the scientifc
community, we have deployed a web application to consult ISOGO predictions (https://biotecnun.unav.
es/app/isogo). Initial data, website link, isoform-specifc GO function predictions and R code is available
at https://gitlab.com/icassol/isogo
