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

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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References

  1. 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. 10.1038/35888

    Article  Google Scholar 

  2. 2.

    Hannon GJ, Rossi JJ: Unlocking the potential of the human genome with RNA interference. Nature 2004, 431(7006):371-378. 10.1038/nature02870

    Article  Google Scholar 

  3. 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. 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)01018-3

    Article  Google Scholar 

  5. 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.2004.12.035

    Article  Google Scholar 

  6. 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/gki364

    Article  Google Scholar 

  7. 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.0404432101

    Article  Google Scholar 

  8. 8.

    Esquela-Kerscher A, Slack FJ: Oncomirs—microRNAs with a role in cancer. Nature Reviews Cancer 2006, 6(4):259-269. 10.1038/nrc1840

    Article  Google Scholar 

  9. 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. 10.

    Evans SC: Kolmogorov complexity estimation and application for information system security, Ph.D. dissertation. 2003.

    Google Scholar 

  11. 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. 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. 13.

    Li M, Vitányi P: Introduction to Kolmogorov Complexity and Applications. Springer, New York, NY, USA; 1997.

    Google Scholar 

  14. 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.2005.006899

    Article  Google Scholar 

  15. 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. 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. 17.

    Nevill-Manning CG, Witten IH: Identifying hierarchical structure in sequences: a linear-time algorithm. Journal of Artificial Intelligence Research 1997, 7: 67-82.

    MATH  Google Scholar 

  18. 18.

    Cherniavsky N, Lander R: Grammar-based compression of DNA sequences. DIMACS Working Group on The Burrows—Wheeler Transform, Piscataway, NJ, USA, August 2004

    Google Scholar 

  19. 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/18.12.1696

    Article  Google Scholar 

  20. 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.

  21. 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. 22.

    Gács P, Tromp JT, Vitányi PMB: Algorithmic statistics. IEEE Transactions on Information Theory 2001, 47(6):2443-2463. 10.1109/18.945257

    Article  MATH  Google Scholar 

  23. 23.

    Cover TM, Thomas JA: Elements of Information Theory. Wiley-Interscience, New York, NY, USA; 1991.

    Google Scholar 

  24. 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/ng865

    Article  Google Scholar 

  25. 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.1291905

    Article  Google Scholar 

  26. 26.

    Doench JG, Sharp PA: Specificity of microRNA target selection in translational repression. Genes & Development 2004, 18(5):504-511. 10.1101/gad.1184404

    Article  Google Scholar 

  27. 27.

    Brennecke J, Stark A, Russell RB, Cohen SM: Principles of microRNA-target recognition. PLoS Biology 2005, 3(3):e85. 10.1371/journal.pbio.0030085

    Article  Google Scholar 

  28. 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 2003

    Google Scholar 

  29. 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.2006.02.037

    Article  Google Scholar 

  30. 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.1206349

    Article  Google Scholar 

  31. 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.1207361

    Article  Google Scholar 

  32. 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/nature03315

    Article  Google Scholar 

  33. 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/25.17.3389

    Article  Google Scholar 

  34. 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/gki021

    Google Scholar 

  35. 35.

    http://microrna.sanger.ac.uk/sequences/index.shtml

  36. 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/gkj112

    Article  Google Scholar 

  37. 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. 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/nature02370

    Article  Google Scholar 

  39. 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/ng1810

    Article  Google Scholar 

  40. 40.

    http://snp500cancer.nci.nih.gov/

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Correspondence to Scott C Evans.

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Evans, S.C., Kourtidis, A., Markham, T.S. et al. MicroRNA Target Detection and Analysis for Genes Related to Breast Cancer Using MDLcompress. J Bioinform Sys Biology 2007, 43670 (2007). https://doi.org/10.1155/2007/43670

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Keywords

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