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)


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 BLAST FDR , 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 BLAST FDR . BLAST FDR 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, BLAST FDR retrieved only 0.27 irrelevant sequences per query compared to 7.44 for BLAST.


False Discovery Rate Nucleic Acid Research Test Database Training Database Good Retrieval Performance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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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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