Skip to content


  • Research Article
  • Open Access

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

  • 1Email author,
  • 2,
  • 1,
  • 3,
  • 2 and
  • 1
EURASIP Journal on Bioinformatics and Systems Biology20072007:43670

  • Received: 1 March 2007
  • Accepted: 23 June 2007
  • Published:


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.


  • Breast Cancer
  • Breast Cancer Cell Line
  • Target Detection
  • Code Method
  • miRNA Sequence


Authors’ Affiliations

GE Global Research, One Research Circle, Niskayuna, NY 12309, USA
Gen*NY*Sis Center for Excellence in Cancer Genomics, University at Albany, State University of New York, One Discovery Drive, Rensselaer, NY 12144, USA
Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA


  1. Fire A, Xu S, Montgomery MK, Kostas SA, Driver SE, Mello CC: Potent and specific genetic interference by double-stranded RNA in caenorhabditis elegans. Nature 1998, 391(6669):806-811. 10.1038/35888View ArticleGoogle Scholar
  2. Hannon GJ, Rossi JJ: Unlocking the potential of the human genome with RNA interference. Nature 2004, 431(7006):371-378. 10.1038/nature02870View ArticleGoogle Scholar
  3. Kourtidis A, Eifert C, Conklin DS: RNAi applications in target validation. In Systems Biology, Applications and Perspectives, Ernst Schering Foundation Symposium Proceedings. Volume 61. Edited by: Bringmann P, Butcher EC, Parry G, Weiss B. Springer, New York, NY, USA; 2007:1-21.Google Scholar
  4. Lewis BP, Shih I-H, Jones-Rhoades MW, Bartel DP, Burge CB: Prediction of mammalian microRNA targets. Cell 2003, 115(7):787-798. 10.1016/S0092-8674(03)01018-3View ArticleGoogle Scholar
  5. Lewis BP, Burge CB, Bartel DP: Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 2005, 120(1):15-20. 10.1016/j.cell.2004.12.035View ArticleGoogle Scholar
  6. Rusinov V, Baev V, Minkov IN, Tabler M: MicroInspector: a web tool for detection of miRNA binding sites in an RNA sequence. Nucleic Acids Research 2005, 33(web server):W696-W700. 10.1093/nar/gki364View ArticleGoogle Scholar
  7. Calin GA, Liu C-G, Sevignani C, et al.: MicroRNA profiling reveals distinct signatures in B cell chronic lymphocytic leukemias. Proceedings of the National Academy of Sciences of the United States of America 2004, 101(32):11755-11760. 10.1073/pnas.0404432101View ArticleGoogle Scholar
  8. Esquela-Kerscher A, Slack FJ: Oncomirs—microRNAs with a role in cancer. Nature Reviews Cancer 2006, 6(4):259-269. 10.1038/nrc1840View ArticleGoogle Scholar
  9. Grünwald P, Myung IJ, Pitt M (Eds): Advances in Minimum Description Length: Theory and Applications. MIT Press, Cambridge, Mass, USA; 2005.Google Scholar
  10. Evans SC: Kolmogorov complexity estimation and application for information system security, Ph.D. dissertation. 2003.Google Scholar
  11. Evans SC, Barnett B, Bush SF, Saulnier GJ: Minimum description length principles for detection and classification of FTP exploits. Proceedings of IEEE Military Communications Conference (MILCOM '04), Monterey, Calif, USA, October-November 2004 1: 473-479.Google Scholar
  12. Evans SC, Torres A, Miller J: MicroRNA target motif detection using OSCR. In Tech. Rep. GRC223. GE Research, Niskayuna, NY, USA; 2006.Google Scholar
  13. Li M, Vitányi P: Introduction to Kolmogorov Complexity and Applications. Springer, New York, NY, USA; 1997.View ArticleMATHGoogle Scholar
  14. Szpankowski W, Ren W, Szpankowski L: An optimal DNA segmentation based on the MDL principle. International Journal of Bioinformatics Research and Applications 2005, 1(1):3-17. 10.1504/IJBRA.2005.006899View ArticleGoogle Scholar
  15. Tobus I, Korodi G, Rissanen J: DNA sequence compression using the normalized maximum likelihood model for discrete regression. Proceedings of Data Compression Conference (DCC '03), Snowbird, Utah, USA, March 2003 253-262.Google Scholar
  16. Apostolico A, Lonardi S: Some theory and practice of greedy off-line textual substitution. Proceedings of Data Compression Conference (DCC '98), Snowbird, Utah, USA, March 1998 119-128.Google Scholar
  17. Nevill-Manning CG, Witten IH: Identifying hierarchical structure in sequences: a linear-time algorithm. Journal of Artificial Intelligence Research 1997, 7: 67-82.MATHGoogle Scholar
  18. Cherniavsky N, Lander R: Grammar-based compression of DNA sequences. DIMACS Working Group on The Burrows—Wheeler Transform, Piscataway, NJ, USA, August 2004Google Scholar
  19. Chen X, Li M, Ma B, Tromp J: DNACompress: fast and effective DNA sequence compression. Bioinformatics 2002, 18(12):1696-1698. 10.1093/bioinformatics/18.12.1696View ArticleGoogle Scholar
  20. Behzadi B, Le Fessant F: DNA compression challenge revisited: a dynamic programming approach. The 16th Annual Symposium on Combinatorial Pattern Matching (CPM '05), Jeju Island, Korea, 2005, Lecture Notes in Computer Science 3537: 190-200.Google Scholar
  21. Evans SC, Markham TS, Torres A, Kourtidis A, Conklin D: An improved minimum description length learning algorithm for nucleotide sequence analysis. Proceedings of IEEE 40th Asilomar Conference on Signals, Systems and Computers (ACSSC '06), Pacific Grove, Calif, USA, October-November 2006 1843-1850.Google Scholar
  22. Gács P, Tromp JT, Vitányi PMB: Algorithmic statistics. IEEE Transactions on Information Theory 2001, 47(6):2443-2463. 10.1109/18.945257View ArticleMATHGoogle Scholar
  23. Cover TM, Thomas JA: Elements of Information Theory. Wiley-Interscience, New York, NY, USA; 1991.View ArticleMATHGoogle Scholar
  24. Lai EC:MicroRNAs are complementary to UTR sequence motifs that mediate negative post-transcriptional regulation. Nature Genetics 2002, 30(4):363-364. 10.1038/ng865View ArticleGoogle Scholar
  25. Lai EC, Tam B, Rubin GM: Pervasive regulation of Drosophila Notch target genes by GY-box-, Brd-box-, and K-box-class microRNAs. Genes & Development 2005, 19(9):1067-1080. 10.1101/gad.1291905View ArticleGoogle Scholar
  26. Doench JG, Sharp PA: Specificity of microRNA target selection in translational repression. Genes & Development 2004, 18(5):504-511. 10.1101/gad.1184404View ArticleGoogle Scholar
  27. Brennecke J, Stark A, Russell RB, Cohen SM: Principles of microRNA-target recognition. PLoS Biology 2005, 3(3):e85. 10.1371/journal.pbio.0030085View ArticleGoogle Scholar
  28. Evans SC, Saulnier GJ, Bush SF: A new universal two part code for estimation of string kolmogorov complexity and algorithmic minimum sufficient statistic. DIMACS Workshop on Complexity and Inference, Piscataway, NJ, USA, June 2003Google Scholar
  29. Voorhoeve PM, le Sage C, Schrier M, et al.: A genetic screen implicates miRNA-372 and miRNA-373 as oncogenes in testicular germ cell tumors. Cell 2006, 124(6):1169-1181. 10.1016/j.cell.2006.02.037View ArticleGoogle Scholar
  30. Mackay A, Jones C, Dexter T, et al.: cDNA microarray analysis of genes associated with ERBB2 (HER2/ neu ) overexpression in human mammary luminal epithelial cells. Oncogene 2003, 22(17):2680-2688. 10.1038/sj.onc.1206349View ArticleGoogle Scholar
  31. Bertucci F, Borie N, Ginestier C, et al.: Identification and validation of an ERBB2 gene expression signature in breast cancers. Oncogene 2004, 23(14):2564-2575. 10.1038/sj.onc.1207361View ArticleGoogle Scholar
  32. Lim LP, Lau NC, Garrett-Engele P, et al.: Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature 2005, 433(7027):769-773. 10.1038/nature03315View ArticleGoogle Scholar
  33. Altschul SF, Madden TL, Schäffer AA, et al.: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Research 1997, 25(17):3389-3402. 10.1093/nar/25.17.3389View ArticleGoogle Scholar
  34. Mignone F, Grillo G, Licciulli F, et al.: UTRdb and UTRsite: a collection of sequences and regulatory motifs of the untranslated regions of eukaryotic mRNAs. Nucleic Acids Research 2005, 33(database):D141-D146. 10.1093/nar/gki021Google Scholar
  36. Griffiths-Jones S, Grocock RJ, van Dongen S, Bateman A, Enright AJ: miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Research 2006, 34(database):D140-D144. 10.1093/nar/gkj112View ArticleGoogle Scholar
  37. Huang X, Hardison RC, Miller W: A space-efficient algorithm for local similarities. Computer Applications in the Biosciences 1990, 6(4):373-381.Google Scholar
  38. Paddison PJ, Silva JM, Conklin DS, et al.: A resource for large-scale RNA-interference-based screens in mammals. Nature 2004, 428(6981):427-431. 10.1038/nature02370View ArticleGoogle Scholar
  39. Clop A, Marcq F, Takeda H, et al.: A mutation creating a potential illegitimate microRNA target site in the myostatin gene affects muscularity in sheep. Nature Genetics 2006, 38(7):813-818. 10.1038/ng1810View ArticleGoogle Scholar


© 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.