Executive Summary: The traditional biological paradigm teaches that much of the human genome is

made up of Junk DNA, as it does not code directly for proteins. However, in the last decade, a

torrent of new research has emerged stressing the importance of non-coding regions of the genome as

important culprits of disease progression. In the light of these discoveries, clinicians are searching for

new tools to adequately grasp the growing complexity of the regulatory network involved with major

disease states, most notably cancer. The most famous, and potentially useful of these tools comes in

the form of genome profiling, that is, recording which regions of the genome are expressed in certain

biological maladies. This project took a new stance on profiling, by instead trying to profile the

transcriptome, the entirety of the transcribed, though not necessarily protein coding regions of the

genome. Status quo profiling technologies cannot perform this profiling function in a cost-effective

manner, nor can they record the expression of thousands of transcripts at the same time. In this

experiment, a new model of profiling, termed SNP-Flagged Non-Coding RNA Profiling, was

validated and tested for biological relevance. This model makes use of the biological fact that many of

these important non-coding RNAs lie over regions of the genome called Single Nucleotide

Polymorphisms, single-base pair changes in the genome found in DNA. Methods already exist to

profile SNPs, called microarrays. This project validated the theoretical ability to use SNPs to flag

non-coding RNA by mapping the association between the increase in the SNP copy numbers and the

increase in RNA transcription, which was found to be directly correlative. Afterwards, the SFNR

Profiling method was used to discriminate between different phenotypes of cancer cells, specifically,

those cells, which expressed Estrogen Receptor protein on their cell surfaces and those which did not,

as well as Basal cancer cells as compared to Luminal cells. This method was able to successfully

classify different types of cancer cells based on which SFNR molecules each type of cell expressed,

and therefore, this method can be used to effectively aid in clinical diagnostics and prognostics.