WebaCGH

An Interactive Online Tool for the Analysis and Display of Array Comparative Genomic Hybridisation Data

Abstract

Gene copy number variations occur both in normal cells and in numerous pathologies including cancer and developmental diseases. Array comparative genomic hybridisation (aCGH) is an emerging technology that allows detection of chromosomal gains and losses in a high-resolution format. When aCGH is performed on cDNA and oligonucleotide microarrays, the impact of DNA copy number on gene transcription profiles may be directly compared. We have created an online software tool, WebaCGH, that functions to (i) upload aCGH and gene transcription results from multiple experiments; (ii) identify significant aberrant regions using a local Z-score threshold in user-selected chromosomal segments subjected to smoothing with moving averages; and (iii) display results in a graphical format with full genome and individual chromosome views. In the individual chromosome display, data can be zoomed in/out in both dimensions (i.e. ratio and physical location) and plotted features can have ‘mouse over’ linking to outside databases to identify loci of interest. Uploaded data can be stored indefinitely for subsequent retrieval and analysis. WebaCGH was created as a Java™-based web application using the open-source database MySQL®.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. 1.

    Iafrate AJ, Feuk L, Rivera MN, et al. Detection of large-scale variation in the human genome. Nat Genet 2004 Sep; 36(9): 949–51

    PubMed  Article  CAS  Google Scholar 

  2. 2.

    Sebat J, Lakshmi B, Troge J, et al. Large-scale copy number polymorphism in the human genome. Science 2004 Jul 23; 305(5683): 525–8

    PubMed  Article  CAS  Google Scholar 

  3. 3.

    Albertson DG, Pinkel D. Genomic microarrays in human genetic disease and cancer. Hum Mol Genet 2003 Oct 15; 12 Spec. no. 2: R145–52

    PubMed  Article  CAS  Google Scholar 

  4. 4.

    Kallioniemi A, Kallioniemi OP, Sudar D, et al. Comparative genomic hybridization for molecular cytogenetic analysis of solid tumors. Science 1992 Oct 30; 258(5083): 818–21

    PubMed  Article  CAS  Google Scholar 

  5. 5.

    Solinas-Toldo S, Lampel S, Stilgenbauer S, et al. Matrix-based comparative genomic hybridization: biochips to screen for genomic imbalances. Genes Chromosomes Cancer 1997 Dec; 20(4): 399–407

    PubMed  Article  CAS  Google Scholar 

  6. 6.

    Pinkel D, Segraves R, Sudar D, et al. High resolution analysis of DNA copy number variation using comparative genomic hybridization to microarrays. Nat Genet 1998 Oct; 20(2): 207–11

    PubMed  Article  CAS  Google Scholar 

  7. 7.

    Pollack JR, Perou CM, Alizadeh AA, et al. Genome-wide analysis of DNA copy-number changes using cDNA microarrays. Nat Genet 1999 Sep; 23(1): 41–6

    PubMed  Article  CAS  Google Scholar 

  8. 8.

    Bignell GR, Huang J, Greshock J, et al. High-resolution analysis of DNA copy number using oligonucleotide microarrays. Genome Res 2004 Feb; 14(2): 287–95

    PubMed  Article  CAS  Google Scholar 

  9. 9.

    Pollack JR, Sorlie T, Perou CM, et al. Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptional program of human breast tumors. Proc Natl Acad Sci U S A 2002 Oct 1; 99(20): 12963–8

    PubMed  Article  CAS  Google Scholar 

  10. 10.

    Pinkel D, Albertson DG. Comparative genomic hybridization. Annu Rev Genomics Hum Genet 2005; 6: 331–54

    PubMed  Article  CAS  Google Scholar 

  11. 11.

    Geschwind DH, Gregg J, Boone K, et al. Klinefelter’s syndrome as a model of anomalous cerebral laterality: testing gene dosage in the X chromosome pseudoautosomal region using a DNA microarray. Dev Genet 1998; 23(3): 215–29

    PubMed  Article  CAS  Google Scholar 

  12. 12.

    Price TS, Regan R, Mott R, et al. SW-ARRAY: a dynamic programming solution for the identification of copy-number changes in genomic DNA using array comparative genome hybridization data. Nucleic Acids Res 2005 Jun 16; 33(11): 3455–64

    PubMed  Article  CAS  Google Scholar 

  13. 13.

    Chen W, Erdogan F, Ropers HH, et al. CGHPRO: a comprehensive data analysis tool for array CGH. BMC Bioinformatics 2005 Apr 5; 6(1): 85

    PubMed  Article  Google Scholar 

  14. 14.

    Hupe P, Stransky N, Thiery JP, et al. Analysis of array CGH data: from signal ratio to gain and loss of DNA regions. Bioinformatics 2004 Dec 12; 20(18): 3413–22

    PubMed  Article  CAS  Google Scholar 

  15. 15.

    Myers CL, Dunham MJ, Kung SY, et al. Accurate detection of aneuploidies in array CGH and gene expression microarray data. Bioinformatics 2004 Dec 12; 20(18): 3533–43

    PubMed  Article  CAS  Google Scholar 

  16. 16.

    Urzúa U, Frankenberger C, Gangi L, et al. Microarray comparative genomic hybridization profile of a murine model for epithelial ovarian cancer reveals genomic imbalances resembling human ovarian carcinomas. Tumour Biol 2005 Aug 9; 26(5): 236–44

    PubMed  Article  Google Scholar 

  17. 17.

    Vaquerizas JM, Dopazo J, Diaz-Uriarte R. DNMAD: web-based diagnosis and normalization for microarray data. Bioinformatics 2004 Dec 12; 20(18): 3656–8

    PubMed  Article  CAS  Google Scholar 

  18. 18.

    Lage JM, Leamon JH, Pejovic T, et al. Whole genome analysis of genetic alterations in small DNA samples using hyperbranched strand displacement amplification and array-CGH. Genome Res 2003 Feb; 13(2): 294–307

    PubMed  Article  CAS  Google Scholar 

  19. 19.

    Colantuoni C, Henry G, Zeger S, et al. SNOMAD (Standardization and NOrmalization of MicroArray Data): web-accessible gene expression data analysis. Bioinformatics 2002 Nov; 18(11): 1540–1

    PubMed  Article  CAS  Google Scholar 

  20. 20.

    Cheadle C, Vawter MP, Freed WJ, et al. Analysis of microarray data using Z score transformation. J Mol Diagn 2003 May; 5(2): 73–81

    PubMed  Article  CAS  Google Scholar 

  21. 21.

    Masayesva BG, Ha P, Garrett-Mayer E, et al. Gene expression alterations over large chromosomal regions in cancers include multiple genes unrelated to malignant progression. Proc Natl Acad Sci U S A 2004; 101: 8715–20

    PubMed  Article  CAS  Google Scholar 

  22. 22.

    Furge KA, Dykema KJ, Ho C, et al. Comparison of array-based comparative genomic hybridization with gene expression-based regional expression biases to identify genetic abnormalities in hepatocellular carcinoma. BMC Genomics 2005; 6: 67

    PubMed  Article  Google Scholar 

Download references

Acknowledgements

This article is dedicated to the memory of José Urzúa. We thank Seymour Davies and John Powell for help in data management. Dr Urzúa was an Exchange Scientist at the Laboratory of Molecular Technology supported by a fellowship from the Oncology Research Faculty Development Program, Office of International Affairs of the National Cancer Institute. This work has been funded in whole or in part with federal funds from the National Cancer Institute, National Institutes of Health under contract no. N01-C0-12400.

The authors declare no conflicts of interest regarding the contents of this manuscript.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Dr Ulises Urzúa.

Additional information

Availability: WebaCGH is freely accessible at http://129.43.22.27/WebaCGH/welcome.htm

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Frankenberger, C., Wu, X., Harmon, J. et al. WebaCGH. Appl-Bioinformatics 5, 125–130 (2006). https://doi.org/10.2165/00822942-200605020-00009

Download citation

Keywords

  • Comparative Genomic Hybridisation
  • aCGH Data
  • Comparative Genomic Hybridisation Data
  • Cancer Genome Anatomy Project
  • Gene Transcription Profile