This folder contains all the scripts used to build Tissue-spEcific mrNa iSoform functIOnal Networks (TENSION). The README file describes the purpose of every script. The scripts are documented in detail within the actual 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.
This material is based upon work supported by the National Science Foundation under Grant IOS-1062546. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1548562. This work used the XSEDE Comet cluster at San Diego Supercomputer Center (SDSC) through allocation TG-BIO170049.
ABI Innovation: Model-based Alternative Splicing Analysis Across Expression Platforms