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Volume 22 Issue 4
August 2007

    Zhuo ZHOU, Zhi-xun DOU, Chen ZHANG, Hou-qing YU, Yi-jie LIU, Cui-zhu ZHANG and You-jia CAO. A Strategy to Optimize the Oligo-Probes for Microarray-based Detection of Viruses*[J]. Virologica Sinica, 2007, 22(4): 326-335.
    Citation: Zhuo ZHOU, Zhi-xun DOU, Chen ZHANG, Hou-qing YU, Yi-jie LIU, Cui-zhu ZHANG, You-jia CAO. A Strategy to Optimize the Oligo-Probes for Microarray-based Detection of Viruses* .VIROLOGICA SINICA, 2007, 22(4) : 326-335.
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    一种病毒检测芯片探针的优化设计

    • 1.

      生物活性材料教育部重点实验室，南开大学生命科学学院，天津 300071; 2天津市微生物功能基因组学重点实验室，南开大学生命科学学院，天津 300071; 3School of Computing, National University of Singapore, 3 Science Drive 2, Singapore 117543

    • 通讯作者： 曹又佳, caoyj@nankai.edu.cn
    • 收稿日期： 2007-01-08
      录用日期： 2007-05-20
    • DNA微阵列是一种具有广阔应用前景的病毒病原检测技术。然而，目前的检测阵列通常应用于部分特定的病毒变异型，因此可能产生假阴性或者不明确的结果。而覆盖所有病毒变异型的微阵列需要设计更多的探针，这将导致点阵密度及阵列生产费用的大幅增高。本文探索和开发了一种新的寡聚核苷酸探针设计策略。我们以HIV-1 tat基因为例设计了阵列探针，并通过计算机运算检验了所优化参数的有效性。结果表明，与现有方法相比，本设计所使用的寡聚核苷酸探针数显著减少，而检测特异性和探针杂交效率并不受影响。采用这种减少寡聚核苷酸探针的微阵列设计方法能够提高DNA微阵列检测能力，并且能够极大的降低微阵列芯片的制造费用。这种芯片探针设计策略和方法可以推广应用于其他病毒的检测，有很高的应用潜力。

    A Strategy to Optimize the Oligo-Probes for Microarray-based Detection of Viruses*

    • 1.

      Department of Biochemistry and Molecular Biology, College of Life Sciences, Nankai University, Tianjin 300071, China

    • 2.

      The Key Laboratory of Bioactive Materials, Ministry of Education, Nankai University, Tianjin 300071, China

    • 3.

      School of Computing, National University of Singapore, 3 Science Drive 2, 117543, Singapore

    • 4.

      Tianjin Key Laboratory of Microbial Functional Genomics, College of Life Sciences, Nankai University, Tianjin 300071, China

    • Corresponding author: You-jia CAO, caoyj@nankai.edu.cn
    • Received Date: 08 January 2007
      Accepted Date: 20 May 2007

      Fund Project: NSFC grant 30270308Tianjin grant 05YFJZJC01301NSFC grant 30370053

    • DNA microarrays have been acknowledged to represent a promising approach for the detection of viral pathogens. However, the probes designed for current arrays could cover only part of the given viral variants, that could result in false-negative or ambiguous data. If all the variants are to be covered, the requirement for more probes would render much higher spot density and thus higher cost of the arrays. Here we have developed a new strategy for oligonucleotide probe design. Using type I human immunodeficiency virus (HIV-1) tat gene as an example, we designed the array probes and validated the optimized parameters in silico. Results show that the oligo number is significantly reduced comparing with the existing methods, while specificity and hybridization efficiency remain intact. The adoption of this method in reducing the oligo numbers could increase the detection capacity for DNA microarrays, and would significantly lower the manufacturing cost for making array chips.
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      1. Altschul S F, Madden T L, Schaffer A A, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res, 1997, 25 (17): 3389-3402.
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      2. Boriskin Y S, Rice P S, Stabler R A, et al. DNA microarrays for virus detection in cases of central nervous system infection. J Clin Microbiol, 2004, 42 (12): 5811-5818.
          doi: 10.1128/JCM.42.12.5811-5818.2004

      3. Bystricka D, Lenz O, Mraz I, et al. Oligonucleotide-based microarray: a new improvement in microarray detection of plant viruses. J Virol Methods, 2005, 128 (1-2): 176-182.
          doi: 10.1016/j.jviromet.2005.04.009

      4. Chizhikov V, Wagner M, Ivshina A, et al. Detection and genotyping of human group A rotaviruses by oligonucleotide microarray hybridization. J Clin Microbiol, 2002, 40 (7): 2398-2407.
          doi: 10.1128/JCM.40.7.2398-2407.2002

      5. He Z, Wu L, Li X, et al. Empirical establishment of oligonucleotide probe design criteria. Appl Environ Microbiol, 2005, 71 (7): 3753-3760.
          doi: 10.1128/AEM.71.7.3753-3760.2005

      6. Ivshina A V, Vodeiko G M, Kuznetsov V A, et al. Mapping of genomic segments of influenza B virus strains by an oligonucleotide microarray method. J Clin Microbiol, 2004, 42 (12): 5793-5801.
          doi: 10.1128/JCM.42.12.5793-5801.2004

      7. Kane M D, Jatkoe T A, Stumpf C R, et al. Assessment of the sensitivity and specificity of oligonucleotide (50mer) microarrays. Nucleic Acids Res, 2000, 28 (22): 4552-4557.
          doi: 10.1093/nar/28.22.4552

      8. Korimbocus J, Scaramozzino N, Lacroix B, et al. DNA probe array for the simultaneous identification of herpesviruses, enteroviruses, and flaviviruses. J Clin Microbiol, 2005, 43 (8): 3779-3787.
          doi: 10.1128/JCM.43.8.3779-3787.2005

      9. Lee I, Dombkowski A A, Athey B D. Guidelines for incorporating non-perfectly matched oligonucleotides into target-specific hybridization probes for a DNA microarray. Nucleic Acids Res, 2004, 32 (2): 681-690.
          doi: 10.1093/nar/gkh196

      10. Markham N R, Zuker M. 2005. DINAMelt web server for nucleic acid melting prediction. Nucleic Acids Res, 33: W577-W581.
          doi: 10.1093/nar/gki591

      11. Oh T J, Kim C J, Woo S K, et al. Development and clinical evaluation of a highly sensitive DNA microarray for detection and genotyping of human papillomaviruses. J Clin Microbiol, 2004, 42 (7): 3272-3280.
          doi: 10.1128/JCM.42.7.3272-3280.2004

      12. Rimour S, Hill D, Militon C, et al. GoArrays: highly dynamic and efficient microarray probe design. Bioinformatics, 2005, 21 (7): 1094-1103.
          doi: 10.1093/bioinformatics/bti112

      13. Rouillard J M, Zuker M, Gulari E. OligoArray 2.0: design of oligonucleotide probes for DNA microarrays using a thermodynamic approach. Nucleic Acids Res, 2003, 31 (12): 3057-3062.
          doi: 10.1093/nar/gkg426

      14. Urakawa H, Fantroussi S E, Smidt H, et al. Optimization of single-base-pair mismatch discrimination in oligonucleotide microarrays. Appl Environ Microbiol, 2003, 69 (5): 2848-2856.
          doi: 10.1128/AEM.69.5.2848-2856.2003

      15. Wang D, Coscoy L, Zylberberg M, et al. Microarray-based detection and genotyping of viral pathogens. Proc Natl Acad Sci USA, 2002, 99 (24): 15687-15692.
          doi: 10.1073/pnas.242579699

      16. Wilson W J, Strout C L, DeSantis T Z, et al. Sequence-specific identification of 18 pathogenic microorganisms using microarray technology. Mol Cell Probes, 2002, 16 (2): 119-127.
          doi: 10.1006/mcpr.2001.0397

      17. Young R A. Biomedical discovery with DNA arrays. Cell, 2000, 102 (1): 9-15.
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          doi: 10.1093/nar/gkg595

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      A Strategy to Optimize the Oligo-Probes for Microarray-based Detection of Viruses*

        Corresponding author: You-jia CAO, caoyj@nankai.edu.cn
      • 1. Department of Biochemistry and Molecular Biology, College of Life Sciences, Nankai University, Tianjin 300071, China
      • 2. The Key Laboratory of Bioactive Materials, Ministry of Education, Nankai University, Tianjin 300071, China
      • 3. School of Computing, National University of Singapore, 3 Science Drive 2, 117543, Singapore
      • 4. Tianjin Key Laboratory of Microbial Functional Genomics, College of Life Sciences, Nankai University, Tianjin 300071, China
      • Received Date: 08 January 2007
      • Accepted Date: 20 May 2007
      Fund Project:  NSFC grant 30270308Tianjin grant 05YFJZJC01301NSFC grant 30370053

      Abstract: DNA microarrays have been acknowledged to represent a promising approach for the detection of viral pathogens. However, the probes designed for current arrays could cover only part of the given viral variants, that could result in false-negative or ambiguous data. If all the variants are to be covered, the requirement for more probes would render much higher spot density and thus higher cost of the arrays. Here we have developed a new strategy for oligonucleotide probe design. Using type I human immunodeficiency virus (HIV-1) tat gene as an example, we designed the array probes and validated the optimized parameters in silico. Results show that the oligo number is significantly reduced comparing with the existing methods, while specificity and hybridization efficiency remain intact. The adoption of this method in reducing the oligo numbers could increase the detection capacity for DNA microarrays, and would significantly lower the manufacturing cost for making array chips.

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