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Analysis of RNA-seq time-series for prostate cancer progression

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  • Thu, 07/27/2017 - 2:00pm - 4:00pm




Analysis of RNA-seq time-series for prostate cancer progression

PhD. Thesis Proposal by:

Abedalrhman Alkhateeb

Date: Thursday, July 27, 2017
Time: 2:00 pm - 4:00 pm
Location:122, Essex Hall

Abstract:

Studying the abundance of select mRNA species throughout cancer progression can provide additional insight into the molecular mechanisms of progression of the disease. In this thesis, I propose a pipe-line that consists of preprocessing RNA-Seq data, hierarchical clustering of universally aligned transcripts profiles, distance function and Index validation for clusters are suitable in identifying outlier transcripts, which have different trending than the majority of the transcripts. The trending of a transcript is the abundance throughout different stages of prostate cancer. For modeling of cancer progression, the stages are represented as time points, and the increase in transcript abundance throughout those time points are cubic spline interpolated.

The identified stage-specific mRNA species termed outlier transcripts that exhibit unique trending patterns are compared to other transcripts during disease progression. This method can identify those outliers rather than finding patterns among the trending transcripts compared to traditional clustering methods.

Thesis Committee:     
Internal Reader: Dr. Jianguo Lu
Internal Reader: Dr. Alioune Ngom  
External Reader: Dr. Lisa Porter     
Advisor: Dr. Luis Rueda       



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