The recent development and implementation of second-generation, deep sequencing technologies has
provided an unprecedented opportunity to characterize genomic changes in cancer. However, the enormous
data output from these sequencing platforms also presents a formidable statistical and computational
challenge to separate and validate the minority of cancer-causing driver mutations from the overwhelming
majority of irrelevant bystander passenger mutations. Computational analysis plays a critically important role in
making biological sense out of the mountains of genomic sequencing data. In this proposal, I propose to
continue my research work as an expert in cancer bioinformatics, to develop computational tools to narrow
down the candidate cancer-causing disease mutations involved in the development and progression of cancer.
My bioinformatics work will focus on three research areas: (i) identification of genes causing familial disposition
to cancer susceptibility, (ii) detection and characterization of large structural alterations in cancer, and (iii)
identification of cancer genes using mouse cancer model system. My short term goal is to develop
bioinformatics tools to develop bioinformatics tools to identify cancer-causing disease mutations using genome
sequencing data from human patients and mouse cancer models. My long term goal is to characterize these
driver mutations further, generating molecular targets to improve diagnosis, risk stratification and treatment of
cancer. In my first aim, I propose to develop a bioinformatics pipeline to identify cancer predisposing germline
mutations from patients with strong familial history of cancer using whole-genome or whole-exome sequencing
data. This aim tests the hypothesis that cancer predisposing mutations can be weighted and validated from
enormous sequencing data sets using statistical and bioinformatics methods. In my second specific aim, I will
develop bioinformatics algorithms to detect and characterize large structural variants in human cancer. This
aim tests the hypothesis that integrative analysis of different genome sequencing platforms can be further
refined and validated the full structure of complex genomic alteration. In my third aim, I will develop algorithms
to identify the genes that accelerate the development of cancer in mouse cancer models. This aim tests the
hypothesis that the computational algorithms and statistical approaches can identify genes predisposing
animals to develop cancer and can predict their relevance to human cancer. Successful completion of this
groundbreaking new informatics research as a Research Specialist will shed new light on the molecular basis
of many cancers, will contribute to active cancer research at Ohio State, and will continue significant recent
progress in developing new genomics technologies and analytical methods in studies of human cancers.
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