APMA Database for Affymetrix Target Sequences Mapping, Quality Assessment and Expression Data Mining

  • Yuriy Orlov
  • Jiangtao Zhou
  • Joanne Chen
  • Atif Shahab
  • Vladimir Kuznetsov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4774)


We have developed an online database APMA (Affymetrix Probe Mapping and Annotation) for interactive presentation, search and visualization of Affymetrix target sequences mapping and annotation <>. APMA contains revised genome localization of the Affymetrix U133 GeneChip initial (target) probe sequences. We designed APMA to use it as a filter before data analysis and data mining so that noise expression signals, false correlations and false gene expression patterns can be reduced. Discrepancies found in probeset annotation and target sequence mapping account for up to 30% of probesets, including about 25% of Affymetrix probesets derived from target sequences overlapped interspersed repeats and 1.8% of original target sequences with erroneous orientation of the sequences. 86% of U133 target sequences passed our quality-control filtering.


Affymetrix U133 database target sequences cross-hybridization mapping genome repeats errors classification recognition data mining 


  1. 1.
    Harbig, J., Sprinkle, R., Enkemann, S.A.: A sequence-based identification of the genes detected by probesets on the Affymetrix U133 plus 2.0 array. Nucleic Acids Res. 33(3), e31 (2005)CrossRefGoogle Scholar
  2. 2.
    Okoniewski, M.J., Miller, C.J.: Hybridization interactions between probesets in short oligo microarrays lead to spurious correlations. BMC Bioinformatics 7, 2761 (2006)CrossRefGoogle Scholar
  3. 3.
    Mecham, B.H., Wetmore, D.Z., Szallasi, Z., Sadovsky, Y., Kohane, I., Mariani, T.J.: Increased measurement accuracy for sequence-verified microarray probes. Physiol Genomics 18, 308–315 (2004)CrossRefGoogle Scholar
  4. 4.
    Gautier, L., Moller, M., Friis-Hansen, L., Knudsen, S.: Alternative mapping of probes to genes for Affymetrix chips. BMC Bioinformatics 5, 111 (2004)CrossRefGoogle Scholar
  5. 5.
    Leong, H.S., Yates, T., Wilson, C., Miller, C.J.: ADAPT: a database of affymetrix probesets and transcripts. Bioinformatics 21, 2552–2553 (2005)CrossRefGoogle Scholar
  6. 6.
    Dai, M., Wang, P., Boyd, A.D., Kostov, G., Athey, B., Jones, E.G., Bunney, W.E., Myers, R.M., Speed, T.P., Akil, H., Watson, S.J., Meng, F.: Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data. Nucleic Acids Res. 33, e175 (2005)CrossRefGoogle Scholar
  7. 7.
    Stalteri, M.A., Harrison, A.P.: Interpretation of multiple probe sets mapping to the same gene in Affymetrix GeneChips. BMC Bioinformatics 15, 8–13 (2007)Google Scholar
  8. 8.
    Orlov, Y.L., Zhou, J.T., Lipovich, L., Yong, H.C., Li, Y., Shahab, A., Kuznetsov, V.A.: A comprehensive quality assessment of the Affymetrix U133A&B probesets by an integrative genomic and clinical data analysis approach. In: Kolchanov, N.A. (ed.) Proceedings of the Fifth International Conference on Bioinformatics of Genome Regulation and Structure, Novosibirsk, Inst. of Cytology&Genetics, vol. 1, pp. 126–129 (2006)Google Scholar
  9. 9.
    Orlov, Y.L., Zhou, J., Lipovich, L.L., Shahab, A., Kuznetsov, V.A.: Quality assessment of the Affymetrix U133A&B probesets by target sequence mapping and expression data analysis. In: Silico Biol. (in press)Google Scholar
  10. 10.
    Zhang, Y., Liu, X.S., Liu, Q.R., Wei, L.: Genome-wide in silico identification and analysis of cis natural antisense transcripts (cis-NATs) in ten species. Nucleic Acids Res. 34, 3465–3475 (2006)CrossRefGoogle Scholar
  11. 11.
    Kuznetsov, V.A., Zhou, J.T., George, J., Orlov, Y.L.: Genome-wide co-expression patterns of human cis-antisense gene pairs. In: Kolchanov, N.A. (ed.) Proceedings of the Fifth International Conference on Bioinformatics of Genome Regulation and Structure, Novosibirsk, Inst. of Cytology&Genetics, vol. 1, pp. 90–93 (2006)Google Scholar
  12. 12.
    Ivshina, A.V., George, J., Senko, O.V., Mow, B., Putti, T.C., Smeds, J., Lindahl, T., Pawitan, Y., Hall, P., Nordgren, H., Wong, J.E., Liu, E.T., Bergh, J., Kuznetsov, V.A., Miller, L.D.: Genetic reclassification of histologic grade delineates new clinical subtypes of breast cancer. Cancer Res. 66, 10292–10301 (2006)CrossRefGoogle Scholar
  13. 13.
    Chua, A.L.-S., Ivshina, A.V., Kuznetsov, V.A.: Pareto-Gamma Statistics reveals global rescaling in transcriptomes of low and high aggressive breast cancer phenotypes. In: Rajapakse, J.C., Wong, L., Acharya, R. (eds.) PRIB 2006. LNCS (LNBI), vol. 4146, pp. 49–59. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  14. 14.
    MAS 5.0 algorithm. Statistical Algorithms Description Document. Santa Clara, CA: Affymetrix, Inc. (2002),
  15. 15.
    Tusher, V.G., Tibshirani, R., Chu, G.: Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. USA 98, 5116–5121 (2001)zbMATHCrossRefGoogle Scholar
  16. 16.
    Liu, G., Loraine, A.E., Shigeta, R., Cline, M., Cheng, J., Valmeekam, V., Sun, S., Kulp, D., Siani-Rose, M.A.: NetAffx: Affymetrix probesets and annotations. Nucleic Acids Res. 31, 82–86 (2003)CrossRefGoogle Scholar
  17. 17.
    Shi, L., Reid, L.H., Jones, W.D., et al.: The MicroArray Quality Control (MAQC) project shows inter- and intra-platform reproducibility of gene expression measurements. Nat. Biotechnology 24, 1151–1161 (2006)CrossRefGoogle Scholar
  18. 18.
    Lu, X., Zhang, X.: The effect of GeneChip gene definitions on the microarray study of cancers. Bioessays 28, 739–746 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Yuriy Orlov
    • 1
  • Jiangtao Zhou
    • 1
  • Joanne Chen
    • 2
  • Atif Shahab
    • 1
    • 2
  • Vladimir Kuznetsov
    • 1
  1. 1.Genome Institute of Singapore, 60 Biopolis Street, Genome, 138672Singapore
  2. 2.Bioinformatics Institute, 30 Biopolis Street, Matrix, 138671Singapore

Personalised recommendations