Tissue-spEcific mrNa iSoform functIOnal Networks (TENSION) Predictions

2019-01-09T15:37:11Z (GMT) by Julie Dickerson Gaurav Kandoi
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.