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Assessing computational tools for the discovery of transcription factor binding sites


The prediction of regulatory elements is a problem where computational methods offer great hope. Over the past few years, numerous tools have become available for this task. The purpose of the current assessment is twofold: to provide some guidance to users regarding the accuracy of currently available tools in various settings, and to provide a benchmark of data sets for assessing future tools.

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Figure 1: Representative statistics comparing the accuracy of the 13 tools assessed in this analysis.

Bob Crimi


  1. Pevzner, P. & Sze, S.-H. Combinatorial approaches to finding subtle signals in DNA sequences. in Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology (ed. Altman, R. et al.). 269–278 (AAAI Press, Menlo Park, CA, 2000).

    Google Scholar 

  2. Sinha, S. & Tompa, M. Performance comparison of algorithms for finding transcription factor binding sites. in 3rd IEEE Symposium on Bioinformatics and Bioengineering (ed. Bourbakis, N.G.). 214–220 (IEEE Computer Society, New York, 2003).

    Google Scholar 

  3. Burset, M. & Guigó, R. Evaluation of gene structure prediction programs. Genomics 34, 353–367 (1996).

    Article  CAS  Google Scholar 

  4. Burge, C. & Karlin, S. Prediction of complete gene structures in human genomic DNA. J. Mol. Biol. 268, 78–94 (1997).

    Article  CAS  Google Scholar 

  5. Reese, M.G. et al. Genome annotation assessment in Drosophila melanogaster. Genome Res. 10, 483–501 (2000).

    Article  CAS  Google Scholar 

  6. Ashburner, M. A biologist's view of the Drosophila genome annotation assessment project. Genome Res. 10, 391–393 (2000).

    Article  CAS  Google Scholar 

  7. Hughes, J.D., Estep, P.W., Tavazoie, S. & Church, G.M. Computational identification of cis-regulatory elements associated with functionally coherent groups of genes in Saccharomyces cerevisiae. J. Mol. Biol. 296, 1205–1214 (2000).

    Article  CAS  Google Scholar 

  8. Workman, C.T. & Stormo, G.D. ANN-Spec: a method for discovering transcription factor binding sites with improved specificity. in Pacific Symposium on Biocomputing (ed. Altman, R., Dunker, A.K., Hunter, L. & Klein, T.E.). 467–478 (Stanford University, Stanford, CA, 2000).

    Google Scholar 

  9. Hertz, G.Z. & Stormo, G.D. Identifying DNA and protein patterns with statistically significant alignments of multiple sequences. Bioinformatics 15, 563–577 (1999).

    Article  CAS  Google Scholar 

  10. Frith, M.C., Hansen, U., Spouge, J.L. & Weng, Z. Finding functional sequence elements by multiple local alignment. Nucleic Acids Res. 32, 189–200 (2004).

    Article  CAS  Google Scholar 

  11. Ao, W., Gaudet, J., Kent, W.J., Muttumu, S. & Mango, S.E. Environmentally induced foregut remodeling by PHA-4/FoxA and DAF-12/NHR. Science 305, 1743–1746 (2004).

    Article  CAS  Google Scholar 

  12. Bailey, T.L. & Elkan, C. The value of prior knowledge in discovering motifs with MEME. in Proceedings of the Third International Conference on Intelligent Systems for Molecular Biology. 21–29 (AAAI Press, Menlo Park, CA, 1995).

    Google Scholar 

  13. Eskin, E. & Pevzner, P. Finding composite regulatory patterns in DNA sequences. Bioinformatics (Supplement 1) 18, S354–S363 (2002).

    Article  Google Scholar 

  14. Thijs, G. et al. A higher-order background model improves the detection of promoter regulatory elements by Gibbs sampling. Bioinformatics 17, 1113–1122 (2001).

    Article  CAS  Google Scholar 

  15. van Helden, J., Andre, B. & Collado-Vides, J. Extracting regulatory sites from the upstream region of yeast genes by computational analysis of oligonucleotide frequencies. J. Mol. Biol. 281, 827–842 (1998).

    Article  CAS  Google Scholar 

  16. van Helden, J., Rios, A.F. & Collado-Vides, J. Discovering regulatory elements in noncoding sequences by analysis of spaced dyads. Nucleic Acids Res. 28, 1808–1818 (2000).

    Article  CAS  Google Scholar 

  17. Régnier, M. & Denise, A. Rare events and conditional events on random strings. Discrete Math. Theor. Comput. Sci. 6, 191–214 (2004).

    Google Scholar 

  18. Favorov, A.V., Gelfand, M.S., Gerasimova, A.V., Mironov, A.A. & Makeev, V.J. Gibbs sampler for identification of symmetrically structured, spaced DNA motifs with improved estimation of the signal length and its validation on the ArcA binding sites. in Proceedings of BGRS 2004 (BGRS, Novosibirsk, 2004).

    Google Scholar 

  19. Pavesi, G., Mereghetti, P., Mauri, G. & Pesole, G. Weeder Web: discovery of transcription factor binding sites in a set of sequences from co-regulated genes. Nucleic Acids Res. 32, W199–W203 (2004).

    Article  CAS  Google Scholar 

  20. Sinha, S. & Tompa, M. YMF: a program for discovery of novel transcription factor binding sites by statistical overrepresentation. Nucleic Acids Res. 31, 3586–3588 (2003).

    Article  CAS  Google Scholar 

  21. Wingender, E., Dietze, P., Karas, H. & Knüppel, R. TRANSFAC: a Database on transcription factors and their DNA binding sites. Nucleic Acids Res. 24, 238–241 (1996).

    Article  CAS  Google Scholar 

  22. Moult, J., Fidelis, K., Zemla, A. & Hubbard, T. Critical assessment of methods of protein structure prediction (CASP)-round V. Proteins 53, 334–339 (2003).

    Article  CAS  Google Scholar 

  23. Sinha, S., Blanchette, M. & Tompa, M. PhyME: A probabilistic algorithm for finding motifs in sets of orthologous sequences. BMC Bioinformat. 5, 170 (2004).

    Article  Google Scholar 

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We thank Mathieu Blanchette, Ari Frank, Phil Green, Susan Hewitt, S.N. Maheshwari, Larry Ruzzo, Terry Speed, Gary Stormo and the organizers and participants of the 2002 Bellairs Workshop on Computational Biology for their important contributions to this project. Martin Tompa and Nan Li were supported by National Science Foundation (NSF) grant DBI-0218798 and by National Institutes of Health (NIH) grant R01 HG02602. Alexander Favorov, Andrei Mironov and Vsevolod Makeev were supported by Howard Hughes Medical Institute grant 55000309, Ludwig Cancer Research Institute grant CRDF RBO-1268-MO-02, Russian Fund of Basic Research grant 04-07-90270 and support from the Russian Academy of Sciences Presidium Program in Molecular and Cellular Biology, project no. 10. Yutao Fu, Martin C. Frith and Zhiping Weng were supported by NSF grant DBI-0116574 and NIH NHGRI grant 1R01HG03110. Giulio Pavesi and Graziano Pesole were supported by the Italian Ministry of University and Scientific Research's Fondo Italiano per la Ricerca di Base project 'Bioinformatica per la Genomica e la Proteomica' and by Telethon. Nicolas Simonis and Jacques van Helden were supported by the European Communities grant QLRI-199-01333, by the Action de Recherches Concertées de la Communauté Française de Belgique and by the Government of the Brussels Region. Saurabh Sinha was supported by a Keck Foundation Fellowship. Gert Thijs and Bart De Moor were supported by Geconcerteerde Onderzoeks-Acties Mefisto-666 and Ambiorics, InterUniversity Attraction Pole V-22, and several funded projects of the Institut voor de aanmoediging van Innovatie door Wetenshap en Technologie in Vlaanderen, Fonds voor Wetenshappelijk Onderzoek, and European Union. Zhou Zhu is a Howard Hughes Medical Institute predoctoral fellow. Zhou Zhu and George Church were supported by the Department of Energy and the Lipper Foundation.

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Tompa, M., Li, N., Bailey, T. et al. Assessing computational tools for the discovery of transcription factor binding sites. Nat Biotechnol 23, 137–144 (2005).

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