Abstract Chris Papageorgio¹, Robert Harrison², Farahnaz B. Rahmatpanah³, Kristen Taylor³, Wade Da-vis⁴ and Charles W. Caldwell³

¹Department of Internal Medicine/Division of Hematology Oncology, Ellis Fischel Cancer Center, University of Missouri School of Medicine, Columbia, MO 65203, USA; ²Department of Computer Science, P.O. Box 3994, Atlanta GA 30302, USA; ³Department of Pathology and Anatomical Sciences, Ellis Fischel Cancer Center, University of Missouri School of Medicine, Columbia, MO 65203, USA; ⁴Department of Medical Research, University of Missouri School of Medicine, Columbia, MO 65203, USA

Abstract

The computational aspects of the problem in this paper involve, firstly, selective mapping of methylated DNA clones according to methylation level and, secondly, extracting motif information from all the mapped elements in the absence of prior probability distribution. Our novel implementation of algorithms to map and maximize expectation in this setting has generated data that appear to be distinct for each lymphoma subtype examined. A “clone” represents a polymerase chain reaction (PCR) product (on average ~500 bp) which belongs to a microarray of 8544 such sequences preserving CpG-rich islands (CGIs) [1]. Accumulating evidence indicates that cancers including lymphomas demonstrate hypermethylation of CGIs “silencing” an increasing number of tumor suppressor (TS) genes which can lead to tumorigenesis.

Discussion
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