We plan to map the RNASeq and run quantify expression and mRNA splicing variations across the samples using software we developed (MAJIQ) and other available tools.
The genetic variations derived from the WGS will be evaluated for pathogenicity and their possible effect on splicing using in house algorithms such as our splicing codes, and publicly available ones. Then, we plan to train the next generation of splicing codes using the matched RNASeq (step1) and the genetic variants (step2).
With the above established we plan to apply both the quantifications and splicing prediction algorithms on the data to gain insights both into the disease etiology. In addition, we expec that the models trained using this data will offer higher accuracy and thus help in the study of normal and other disease states.
