Chinese Science Bulletin

, Volume 53, Issue 6, pp 817–824 | Cite as

The research progress of tiling array technology and applications

Review Bionformatics

Abstract

Tiling array technology was improved from microarray technology. Over the past five years, tiling array has become an important tool for gathering genome information. Its features of high density and high throughput allow people to probe into life from the whole-genome level. This paper is a survey of tiling array technology and its applications. In addition, some typical algorithms for identifying expressed probe signals are described and compared.

Keywords

tiling array bioinformatics high throughput signal identification 

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References

  1. 1.
    Royce T E, Rozowsky J S, Bertone P, et al. Issues in the analysis of oligonucleotide tiling microarrays for transcript mapping. Trends Genet, 2004, 21: 466–475CrossRefGoogle Scholar
  2. 2.
    Johnson J M, Edwards S, Shoemaker D, et al. Dark matter in the genome: evidence of widespread transcription detected by microarray tiling experiments. Trends Genet, 2004, 21: 93–102CrossRefGoogle Scholar
  3. 3.
    Whitchurch A K. Gene expression microarray. IEEE Potentials, 2002, 21: 30–34CrossRefGoogle Scholar
  4. 4.
    Moore T R. Making chips to probe genes. IEEE Spectrum, 2001, 38:54–60CrossRefGoogle Scholar
  5. 5.
    Hughes T R, Mao M, Jones A R, et al. Expression profiling using microarrays fabricated by an ink-jet oligonucleotide synthesizer. Nat Biotechnol, 2001, 19: 342–377CrossRefGoogle Scholar
  6. 6.
    Chou C C, Chen C H, Lee T T, et al. Optimization of probe length and the number of probes per gene for optimal microarray analysis of gene expression. Nucleic Acids Res, 2004, 32: e99CrossRefGoogle Scholar
  7. 7.
    Liebich J, Schadt C W, Chong S C, et al. Improvement of oligonucleotide probe design criteria for functional gene microarrays in enviromental applications. Appl Environ Microarray, 2006, 72: 1688–1691CrossRefGoogle Scholar
  8. 8.
    He Z L, Wu L Y, Li X Y, et al. Empirical establishment of oligonucleotide probe design criteria. Appl Environ Microarray, 2005, 71(7):3753–3760CrossRefGoogle Scholar
  9. 9.
    Li X Y, He Z L, Zhou J Z. Selection of optimal oligonucleotide probes for microarrays using multiple criteria, global alignment and parameter estimation. Nucleic Acids Res, 2005, 33: 6114–6123CrossRefGoogle Scholar
  10. 10.
    Kane M D, Jatkoe T A, Stumpf C R. et al. Assessment of sensitivity and specificity of oligonucleotide (50mer) microarrays. Nucleic Acids Res, 2000, 2: 4552–4557CrossRefGoogle Scholar
  11. 11.
    Pearson W R, Lipman D. Improved tools for biological sequence comparison. Proc Natl Acad Sci USA, 1988, 85: 2444–2448CrossRefGoogle Scholar
  12. 12.
    Altschul S F, Gish W, Miller W, et al. Basic local alignment search tool. J Mol Biol, 1990, 215: 403–410Google Scholar
  13. 13.
    Rinn J L, Euskirchen G, Bertone P, et al. The transcriptional activity of human chromosome 22. Genes Dev, 2003, 17: 529–540CrossRefGoogle Scholar
  14. 14.
    Mockler T C, Chan S, Sundaresan A, et al. Applications of DNA tiling arrays for whole-genome analysis. Genomics, 2005, 85: 1–15CrossRefGoogle Scholar
  15. 15.
    Shoemaker D D, Schadt E E, Armour C D, et al. Experimental annotation of the human genome using microarray technology. Nature, 2001, 409: 922–927CrossRefGoogle Scholar
  16. 16.
    Kampa D, Cheng J, Kapranove P, et al. Novel RNAs identified from an in-depth analysis of the transcriptome of human chromosome 21 and 22. Genome Res, 2004, 12: 331–342CrossRefGoogle Scholar
  17. 17.
    Kapranov P, Cawley S E, Drenkow J, et al. Large_scale transcriptional activity in chromosomes 21 and 22. Science, 2002, 296:916–919CrossRefGoogle Scholar
  18. 18.
    Chen J J, Sun M, Kent W J, et al. Over 20% of human transcripts might form sense-antisense pairs. Necleic Acids Res, 2004, 32:4812–4820CrossRefGoogle Scholar
  19. 19.
    Schadt E E, Edwards S W, Debraj G, et al. A comprehensive transcript index of the human genome generated using microarrays and computational approaches. Genome Biol, 2004, 5: R73CrossRefGoogle Scholar
  20. 20.
    Bertone P, Stolc V, Royce T E, et al. Identification of novel transcribed sequences in human using high-resolution genomic Tiling Arrays. Science, 2004, 306: 2242–2246CrossRefGoogle Scholar
  21. 21.
    Cheng J, Kaparanov P, Drenkow J, et al. Transcriptional maps of 10 human chromosomes at 5-nucleotide resolution. Science, 2005, 308: 1149–1154CrossRefGoogle Scholar
  22. 22.
    Yamada K, Lim J, Dale J, et al. Empirical analysis of transcriptional activity in the Arabidopsis genome. Science, 2003, 302: 842–846CrossRefGoogle Scholar
  23. 23.
    Stolc V, Gauhar Z, Mason C, et al. A gene expression map for the euchromatic genome of Drosophila melanogaster. Science, 2004, 302:655–660CrossRefGoogle Scholar
  24. 24.
    Ota T, Suzuki Y, Nishikawa T. Complete sequencing and characterization of 21,243 full-length human cDNAs. Nat Genet, 2004, 36:40–45CrossRefGoogle Scholar
  25. 25.
    Saha S, Sparks A B, Rago C, et al. Using the transcriptome to annotate the genome. Nat Biotechnol, 2002, 20: 508–512CrossRefGoogle Scholar
  26. 26.
    Claverie J M. Fewer genes, more noncoding RNA. Science, 2005, 309:1529–1530CrossRefGoogle Scholar
  27. 27.
    Castle J, Engele P G, Armour C D, et al. Optimization of oligonucleotide arrays and RNA amplification protocols for analysis of transcript structure and alternative splicing. Genome Biol, 2003, 4:R66CrossRefGoogle Scholar
  28. 28.
    Flikka K, Yadetie F, Laegreid A, et al. XHM: A system for detection of potential cross hybridizations in DNA microarrays. BMC Bioinformatics, 2004, 5: 117–125CrossRefGoogle Scholar
  29. 29.
    Reilly C, Raghavan A, Bohjanen P. Global assessment of cross-hybridization for oligonucleotide arrays. J Biomole Tech, 2006, 17:163–172Google Scholar
  30. 30.
    Wu C, Carta R, Zhang L, et al. Sequence dependence of crosshybridization on short oligo microarrays. Nucleic Acids Res, 2005, 33(9): e84CrossRefGoogle Scholar
  31. 31.
    Halasz G, van Batenburg M F, Perusse J, et al. Detecting transcriptional active regions using genomic tiling arrays. Genome Biol, 2006, 7: R59CrossRefGoogle Scholar
  32. 32.
    Huber W, Toedling J, Steinmetz L M. Transcript mapping with high-density oligonucleotide tiling arrays. Bioinformatics, 22: 1963–1970Google Scholar
  33. 33.
    Emanuelsson O, Nagalakshmi U, Zheng D Y, et al. Assessing the performance of different high-density tiling microarray strategies for mapping transcribed regions of the human genome. Genome Res, 2006, 17: 886–897CrossRefGoogle Scholar
  34. 34.
    Ying L, Schadt E E, Svetnik V, et al. Identification of chromosomal regions containing transcribed sequences using microarrays and computational methods. In 2003 Proceedings of the American Statistical Association Alexandria, VA: American Statistical Association, 2003: 4672–4677Google Scholar
  35. 35.
    Rabiner L. A tutorial on hidden Markov models and selected application in speech recognition. Proc IEEE, 1989, 77: 257–286CrossRefGoogle Scholar
  36. 36.
    Du J, Rozowsky J S, Korbel J O, et al. A supervised hidden markov model framework for efficiently segmenting tiling array data in transcriptional and chIP-chip experiments:systematically incorporating validated biological knowledge. Bioinformatics, 2006, 22:3016–3024CrossRefGoogle Scholar
  37. 37.
    Munch K, Gardner P P, Arctander P, et al. A hidden Markov model approach for determing expression from genomic tiling microarrays. BMC Bioinformatics, 2006, 7: 1471–2105Google Scholar
  38. 38.
    Ji H K, Wong W H. TileMap: Create chromosomal map of tiling array hybridization. Bioinformatics, 2005, 21: 3629–3636CrossRefGoogle Scholar
  39. 39.
    Li W, Meyer C A, Liu X S. A hiddern Markov model for analyzing ChIP-chip experiments on genome tiling arrays and its application to p53 binding sequences. Bioinformatics, 2005, 21: 1274–1282Google Scholar
  40. 40.
    Viterbi A J. Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans Inform Theory, 1967, 13: 260–267CrossRefGoogle Scholar

Copyright information

© Science in China Press 2008

Authors and Affiliations

  1. 1.Supercomputing Center, Computer Network Information CenterChinese Academy of SciencesBeijingChina
  2. 2.Graduate University of Chinese Academy of SciencesBeijingChina
  3. 3.Beijing Genomics InstituteChinese Academy of SciencesBeijingChina

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