Algorithmic discovery of methylation "hot spots" in DNA from lymphoma patients

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 approximately 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.