Open Access

MicroRNA Target Detection and Analysis for Genes Related to Breast Cancer Using MDLcompress

  • Scott C Evans1Email author,
  • Antonis Kourtidis2,
  • T Stephen Markham1,
  • Jonathan Miller3,
  • Douglas S Conklin2 and
  • Andrew S Torres1
EURASIP Journal on Bioinformatics and Systems Biology20072007:43670

DOI: 10.1155/2007/43670

Received: 1 March 2007

Accepted: 23 June 2007

Published: 30 October 2007

Abstract

We describe initial results of miRNA sequence analysis with the optimal symbol compression ratio (OSCR) algorithm and recast this grammar inference algorithm as an improved minimum description length (MDL) learning tool: MDLcompress. We apply this tool to explore the relationship between miRNAs, single nucleotide polymorphisms (SNPs), and breast cancer. Our new algorithm outperforms other grammar-based coding methods, such as DNA Sequitur, while retaining a two-part code that highlights biologically significant phrases. The deep recursion of MDLcompress, together with its explicit two-part coding, enables it to identify biologically meaningful sequence without needlessly restrictive priors. The ability to quantify cost in bits for phrases in the MDL model allows prediction of regions where SNPs may have the most impact on biological activity. MDLcompress improves on our previous algorithm in execution time through an innovative data structure, and in specificity of motif detection (compression) through improved heuristics. An MDLcompress analysis of 144 over expressed genes from the breast cancer cell line BT474 has identified novel motifs, including potential microRNA (miRNA) binding sites that are candidates for experimental validation.

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Authors’ Affiliations

(1)
GE Global Research, One Research Circle
(2)
Gen*NY*Sis Center for Excellence in Cancer Genomics, University at Albany, State University of New York, One Discovery Drive
(3)
Human Genome Sequencing Center, Baylor College of Medicine

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Copyright

© General Electric Company. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.