@article {Wang116863, author = {Qingqing Wang and Donald C. Rio}, title = {The Junction Usage Model (JUM): A method for comprehensive annotation-free differential analysis of tissue-specific global alternative pre-mRNA splicing patterns}, elocation-id = {116863}, year = {2017}, doi = {10.1101/116863}, publisher = {Cold Spring Harbor Laboratory}, abstract = {Alternative pre-mRNA splicing (AS) generates exceptionally diverse transcriptome and proteome profiles that critically affect eukaryotic gene expression in different tissues, developmental stages and disease. However, current efforts to evaluate tissue-specific AS patterns rely completely or partially on an annotated libraries of known gene transcripts, which hinders the analysis of AS patterns that are novel or specific to the cell/tissue or for non- or poorly annotated genomes. To tackle this problem, we describe a method called the Junction Usage Model (JUM) that offers a de novo approach to analyze tissue-specific AS profiles without any prior knowledge of the transcriptome. JUM exclusively uses RNA-seq reads mapped to splice junctions to construct statistical models and to accurately quantify AS changes, and then faithfully reconstructs the detected splice junctions into AS patterns based on their unique topological features. Compared to other recent methods, we found that JUM consistently identified true novel tissue-specific AS events that could not be identified by other methods, and further rejected false positive and/or misclassified AS events. In summary, JUM provides a new framework and software that enables the thorough investigation of the dynamic and tissue-specific AS regulation in a wide range of cells, tissues and organisms.}, URL = {https://www.biorxiv.org/content/early/2017/03/14/116863}, eprint = {https://www.biorxiv.org/content/early/2017/03/14/116863.full.pdf}, journal = {bioRxiv} }