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Predicting DNA methylation susceptibility using CpG flanking sequences

  • S. Kim(corresponding author)
    ,
  • M. Li
    ,
  • H. Pair
    ,
  • K. Nephew
    ,
  • ,
  • R. Kramer
*Corresponding author for this work
  • Center for Genomics and Bioinformatics
    ,
  • School of Informatics
    ,
  • Indiana University Bloomington
    ,
  • University of Missouri
    ,
  • Department of Computer Sciences
    ,
  • Ohio State University
Scholary Output:
Chapter in Book/Report/Conference proceeding
Conference contribution

Related Event

Title

13th Pacific Symposium on Biocomputing, PSB 2008

Event type

Conference

Date

01/04/2008 - 01/08/2008

Location

Kohala Coast, HIUnited States

Abstract

DNA methylation is a type of chemical modification of DNA that adds a methyl group to DNA at the fifth carbon of the cytosine pyrimidine ring. In normal cells, methylation of CpG dinucleotides is extensively found across the genome. However, specific DNA regions known as the CpG islands, short CpG dinucleotide-rich stretches (500bp-2000bp), are commonly unmethylated. During tumorigenesis, on the other hand, global de-methylation and CpG island hypermethylation are widely observed. De novo hypermethylation at CpG dinucleotides is typically associated with loss of expression of flanking genes, thus it is believed to be an alternative to mutation and deletion in the inactivation of tumor suppressor genes. In this paper, we report that sequences flanking CpG sites can be used for predicting DNA methylation levels. DNA methylation levels were measured by utilizing a new high throughput sequencing technology (454) to sequence bisulfite treated DNA from four types of primary leukemia and lymphoma cells and normal peripheral blood lymphocytes. After measuring methylation levels at each CpG site, we used 30 bp flanking sequences to characterize methylation susceptibility in terms of character compositions and built predictive models for DNA methylation susceptibility, achieving up to 75% prediction accuracy in 10-fold cross validation tests. Our study is first of its kind to build predictive models for methylation susceptibility by utilizing CpG site specific methylation levels.

Publication Information

Output type

Scholary Output:
Chapter in Book/Report/Conference proceeding
Conference contribution

Original language

English (US)

Pages from-to (Number of pages)

Pages 315-326 (12 pages)

Publication milestones

  • Published - 2008

Publication status

Published - 2008

Publisher

World Scientific, United States

Publication series

  • Publication series name: Pacific Symposium on Biocomputing 2008, PSB 2008
9812776087, 9789812776082

Publication IDs

  • Scopus: 40549094047
  • PubMed: 18229696

Host publication title

Pacific Symposium on Biocomputing 2008, PSB 2008

Publication metrics

Metrics

SciVal
FWCI
1.58
SciVal
Author count
8
SciVal
citations
17
SciVal
Paper percentile
71
Fractional count
1
Fractional count
0.13
Fractional count
7
Fractional count
0.88
Fractional count
1
Fractional count
1
Scopus
citations

PlumX

Captures
57
Citation count
22

Funding Details

FunderFunding number
NCI
R01CA085289