Computing The Role Of Alternative Splicing In Cancer
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Accumulating evidence indicates that recurrent spliceosomal mutations contribute to the initiation and progression of several cancers through diverse fundamental cellular processes.A number of computational tools are used to characterize splicing effects in cancer. These tools present limitations that can be overcome by running alternative splicing analysis with multiple tools and integrating the results.Extracting splicing events functionally relevant to cancer requires rigorous quality control to filter technical artifacts, crossvalidate the events using independent datasets, and integrate alternative approaches including regulatory network characterization and cancer signaling pathway analyses. By taking advantage of the increasing amount of genomic data, deep learning-based methods have dramatically improved the state-of-the-art performance of alternative splicing analysis.
“Computing The Role Of Alternative Splicing In Cancer”
AUTHORS: Zhaoqi Liu, Raul Rabadan.
LINK TO PUBLICATION:
Trends In Cancer. 2021 Jan 23. doi: 10.1016/j.trecan.2020.12.015