False Discovery Rate for Homology Searches

  • Hyrum D. Carroll
  • Alex C. Williams
  • Anthony G. Davis
  • John L. Spouge
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8213)

Abstract

While many different aspects of retrieval algorithms (e.g., BLAST) have been studied in depth, the method for determining the retrieval threshold has not enjoyed the same attention. Furthermore, with genetic databases growing rapidly, the challenges of multiple testing are escalating. In order to improve search sensitivity, we propose the use of the false discovery rate (FDR) as the method to control the number of irrelevant (“false positive”) sequences. In this paper, we introduce BLASTFDR, an extended version of BLAST that uses a FDR method for the threshold criterion. We evaluated five different multiple testing methods on a large training database and chose the best performing one, Benjamini-Hochberg, as the default for BLASTFDR. BLASTFDR achieves 14.1% better retrieval performance than BLAST on a large (5,161 queries) test database and 26.8% better retrieval score for queries belonging to small superfamilies. Furthermore, BLASTFDR retrieved only 0.27 irrelevant sequences per query compared to 7.44 for BLAST.

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References

  1. 1.
    Altschul, S., Gertz, E., Agarwala, R., Schäffer, A., Yu, Y.: PSI-BLAST pseudocounts and the minimum description length principle. Nucleic Acids Research 37(3), 815–824 (2009)CrossRefGoogle Scholar
  2. 2.
    Altschul, S.F., Madden, T.L., Schäffer, A.A., Zhang, J., Zhang, Z., Miller, W., Lipman, D.J.: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Research 25(17), 3389–3402 (1997)CrossRefGoogle Scholar
  3. 3.
    Benjamini, Y., Hochberg, Y.: Controlling the False Discovery Rate: a Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society, Series B 57, 289–300 (1995)MathSciNetMATHGoogle Scholar
  4. 4.
    Bonferroni, C.E.: Il calcolo delle assicurazioni su gruppi di teste. Tipografia del Senato (1935)Google Scholar
  5. 5.
    Carroll, H.D., Kann, M.G., Sheetlin, S.L., Spouge, J.L.: Threshold Average Precision (TAP-k): A Measure of Retrieval Efficacy Designed for Bioinformatics. Bioinformatics 26(14), 1708–1713 (2010)CrossRefGoogle Scholar
  6. 6.
    Chandonia, J., Hon, G., Walker, N., Lo Conte, L., Koehl, P., Levitt, M., Brenner, S.: The ASTRAL Compendium in 2004. Nucleic Acids Research 32(Database Issue), D189–D192 (2004)Google Scholar
  7. 7.
    Gonzalez, M., Pearson, W.: Homologous over-extension: a challenge for iterative similarity searches. Nucleic Acids Research 38(7), 2177–2189 (2010)CrossRefGoogle Scholar
  8. 8.
    Gribskov, M., Robinson, N.: Use of receiver operating characteristic (ROC) analysis to evaluate sequence matching. Computers and Chemistry 20(1), 25–33 (1996)CrossRefGoogle Scholar
  9. 9.
    Hochberg, Y.: A sharper Bonferroni procedure for multiple tests of significance. Biometrika 75(4), 800–802 (1988)MathSciNetCrossRefMATHGoogle Scholar
  10. 10.
    Holm, S.: A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 65–70 (1979)Google Scholar
  11. 11.
    Hommel, G.: A stagewise rejective multiple test procedure based on a modified Bonferroni test. Biometrika 75(2), 383–386 (1988)CrossRefMATHGoogle Scholar
  12. 12.
    Storey, J.: A direct approach to false discovery rates. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 64(3), 479–498 (2002)MathSciNetCrossRefMATHGoogle Scholar
  13. 13.
    Wheeler, T.J., Clements, J., Eddy, S.R., Hubley, R., Jones, T.A., Jurka, J., Smit, A.F., Finn, R.D.: Dfam: a database of repetitive DNA based on profile hidden Markov models. Nucleic Acids Research 41(D1), D70–D82 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Hyrum D. Carroll
    • 1
  • Alex C. Williams
    • 1
  • Anthony G. Davis
    • 1
  • John L. Spouge
    • 2
  1. 1.Department of Computer ScienceMiddle Tennessee State UniversityMurfreesboroUnited States of America
  2. 2.National Center for Biotechnology InformationBethesdaUnited States of America

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