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Optimizing Approaches for Sensitive, High Performance Clustering of Gene Expressions
Moler, James C.

2011, Master of Science, Miami University, Computer Science and Systems Analysis.
This thesis presents several new algorithmic approaches to the problem of clustering conventional ESTs and high throughput gene expression data, which are implemented in the software tool PEACE. The d2 algorithm for sequence comparison is improved and enhanced with a novel two-pass extension, and a minimum spanning tree-based algorithm is used to cluster ESTs, providing an efficient and accurate solution. Furthermore, in order to address the unique challenges of high throughput sequencing technologies such as 454, Illumina and SOLiD sequencing, an adaptive d2 algorithm is introduced to handle variations in fragment length. The resulting tool compares favorably with other leading tools in the literature, including WCD, CAP3, and TGICL, on both EST and next-generation sequencing (NGS) data.
John Karro, PhD (Advisor)
Dhananjai Rao, PhD (Committee Member)
Mufit Ozden, PhD (Committee Member)
40 p.

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Moler, J. (2011). Optimizing Approaches for Sensitive, High Performance Clustering of Gene Expressions. (Electronic Thesis or Dissertation). Retrieved from https://etd.ohiolink.edu/

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Moler, James. "Optimizing Approaches for Sensitive, High Performance Clustering of Gene Expressions." Electronic Thesis or Dissertation. Miami University, 2011. OhioLINK Electronic Theses and Dissertations Center. 22 Jun 2017.

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Moler, James "Optimizing Approaches for Sensitive, High Performance Clustering of Gene Expressions." Electronic Thesis or Dissertation. Miami University, 2011. https://etd.ohiolink.edu/

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