Indiana University-Purdue University Indianapolis (IUPUI) / The availability of highly-distributed computing compliments the proliferation of
next generation sequencing (NGS) and genome-wide association studies (GWAS)
datasets. These data sets are often complex, poorly annotated or require complex domain
knowledge to sensibly manage. These novel datasets provide a rare, multi-dimensional
omics (proteomics, transcriptomics, and genomics) view of a single sample or patient.
Previously, biologists assumed a strict adherence to the central dogma:
replication, transcription and translation. Recent studies in genomics and proteomics
emphasize that this is not the case. We must employ big-data methodologies to not only
understand the biogenesis of these molecules, but also their disruption in disease states.
The Cancer Genome Atlas (TCGA) provides high-dimensional patient data and illustrates
the trends that occur in expression profiles and their alteration in many complex disease
states.
I will ultimately create a bottom-up multi-omics approach to observe biological
systems using big data techniques. I hypothesize that big data and systems biology
approaches can be applied to public datasets to identify important subsets of genes in
cancer phenotypes. By exploring these signatures, we can better understand the role of
amplification and transcript alterations in cancer.
| Identifer | oai:union.ndltd.org:IUPUI/oai:scholarworks.iupui.edu:1805/24621 |
| Date | 11 1900 |
| Creators | Kechavarzi, Bobak David |
| Contributors | Wu, Huanmei, Doman, Thompson, Dow, Ernst, Liu, Yunlong, Liu, Xiaowen, Yan, Jingwen |
| Source Sets | Indiana University-Purdue University Indianapolis |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
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