  Motivation: Despite being often perceived as the main contributors to cell
fate and physiology, genes alone cannot predict cellular phenotype. During the
process of gene expression, 95% of human genes can code for multiple proteins
due to alternative splicing. While most splice variants of a gene carry the
same function, variants within some key genes can have remarkably different
roles. To bridge the gap between genotype and phenotype, condition- and
tissue-specific models of metabolism have been constructed. However, current
metabolic models only include information at the gene level. Consequently, as
recently acknowledged by the scientific community, common situations where
changes in splice-isoform expression levels alter the metabolic outcome cannot
be modeled. Results: We here propose GEMsplice, the first method for the
incorporation of splice-isoform expression data into genome-scale metabolic
models. Using GEMsplice, we make full use of RNA-Seq quantitative expression
profiles to predict, for the first time, the effects of splice isoform-level
changes in the metabolism of 1455 patients with 31 different breast cancer
types. We validate GEMsplice by generating cancer-versus-normal predictions on
metabolic pathways, and by comparing with gene-level approaches and available
literature on pathways affected by breast cancer. GEMsplice is freely available
for academic use at https://github.com/GEMsplice/GEMsplice_code. Compared to
state-of-the-art methods, we anticipate that GEMsplice will enable for the
first time computational analyses at transcript level with splice-isoform
resolution.
