Tissue-spEcific mrNa iSoform functIOnal Networks (TENSION) Predictions

This folder contains the input and predictions of the random forest model used to develop the Tissue-spEcific mrNa iSoform functIOnal Networks (TENSION). The README file describes the contents of the files.
Alternative Splicing produces multiple
mRNA isoforms of a gene which have important diverse roles such as regulation
of gene expression, human heritable diseases, and response to environmental
stresses. However, very little has been done to assign functions at the mRNA
isoform level. Functional networks, where the interactions are quantified by
their probability of being involved in the same biological process are
typically generated at the gene level. We use a diverse array of tissue-specific
RNA-seq datasets and sequence information to train random forest models for
predicting the functional networks following a leave-one-tissue-out strategy.
Since there is no mRNA isoform-level gold standard, we use single isoform genes
co-annotated to Gene Ontology biological process annotations, Kyoto
Encyclopedia of Genes and Genomes pathways, BioCyc pathways and protein-protein
interactions as functionally related (positive pair). To generate the
non-functional pairs (negative pair), we use the Gene Ontology annotations
tagged with “NOT” qualifier. We describe 17 Tissue-spEcific mrNa iSoform
functIOnal Networks (TENSION) in addition to an organism level reference
functional network for mouse. We validate our predictions by comparing its
performance with previous methods, randomized positive and negative class
labels, updated Gene Ontology annotations, and by literature evidence.

Version 2: improvements were made to the framework resulting in better performance and new datasets.