Stability selection for regression-based models of transcription factor-DNA binding specificity

Bioinformatics

Volumn 29, Issue 13, 2013, Pages

  Mordelet, Fantine ^a   Horton, John ^a   Hartemink, Alexander J ^a,b,c   Engelhardt, Barbara E ^a,c,d   Gordân, Raluca ^a,b,d  

^a DUKE UNIVERSITY   (United States)

^b Duke University   (United States)

^c Duke University   (United States)

^d DUKE UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DNA; PROTEIN BINDING; SACCHAROMYCES CEREVISIAE PROTEIN; TRANSCRIPTION FACTOR;

ALGORITHM; BINDING SITE; CHEMISTRY; GENOME; HUMAN; METABOLISM; PROTEIN MICROARRAY; STATISTICAL MODEL; SUPPORT VECTOR MACHINE; ARTICLE; PROTEIN BINDING;

ALGORITHMS; BINDING SITES; DNA; GENOME; HUMANS; LINEAR MODELS; PROTEIN ARRAY ANALYSIS; PROTEIN BINDING; SACCHAROMYCES CEREVISIAE PROTEINS; SUPPORT VECTOR MACHINES; TRANSCRIPTION FACTORS;

EID: 84879970323     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btt221     Document Type: Conference Paper

References (51)

1. Agius, P. et al. (2010) High resolution models of transcription factor-DNA affinities improve in vitro and in vivo binding predictions. PLoS Comput. Biol., 6, e1000916.
2. Annala, M. et al. (2011) A linear model for transcription factor binding affinity prediction in protein binding microarrays. PLoS One, 6, e20059.
3. Bach, F.R. (2008) Bolasso: Nodel consistent LASSO estimation through the bootstrap. In: Cohen, W.W., McCallum, A. and Roweis, S.T. (eds.) Proceedings of the 25th International Conference on Machine Learning, New York, NY, USA.
4. Badis, G. et al. (2009) Diversity and complexity in DNA recognition by transcription factors. Science, 324, 1720-1723.
5. Barash, Y. et al. (2003) Modeling dependencies in protein-DNA binding sites. In: Proceedings of RECOMB 2003. New York, NY, USA, pp. 28-37.
6. Benos, P. et al. (2002) Additivity in protein-DNA interactions: How good an approximation is it?. Nucleic Acids Res., 30, 4442-4451.
7. Berger, M. et al. (2006) Compact, universal DNA microarrays to comprehensively determine transcription-factor binding site specificities. Nat. Biotechnol., 24, 1429-1435.
8. Berger, M.F. and Bulyk, M.L. (2009) Universal protein-binding microarrays for the comprehensive characterization of the DNA binding specificities of transcription factors. Nat. Protoc., 4, 393-411.
9. Bulyk, M.L. et al. (2002) Nucleotides of transcription factor binding sites exert interdependent effects on the binding affinities of transcription factors. Nucleic Acids Res., 30, 1255-1261.
10. Efron, B. et al. (2004) Least angle regression. Ann. Stat., 32, 407-499.
11. ENCODE Project Consortium. (2012) An integrated encyclopedia of DNA elements in the human genome. Nature, 489, 57-74.
12. Fong, A.P. et al. (2012) Genetic and epigenetic determinants of neurogenesis and myogenesis. Dev. Cell, 22, 721-735.
13. Gordâ n, R. et al. (2009) Distinguishing direct versus indirect transcription factor-DNA interactions. Genome Res., 19, 2090-2100.
14. Gordâ n, R. et al. (2011) Curated collection of yeast transcription factor DNA binding specificity data reveals novel structural and gene regulatory insights. Genome Biol., 12, R125.
15. Gordâ n, R. et al. (2013) Genomic regions flanking E-box binding sites influence DNA binding specificity of bHLH transcription factors through DNA shape. Cell Rep., 3, 1093-1104.
16. Harbison, C. et al. (2004) Transcriptional regulatory code of a eukaryotic genome. Nature, 431, 99-104.
17. Haury, A.C. et al. (2012) TIGRESS: Trustful inference of gene regulation using stability selection. BMC Syst. Biol., 6, 145.
18. Jauch, R. et al. (2012) The crystal structure of the Sox4 HMG domain-DNA complex suggests a mechanism for positional interdependence in DNA recognition. Biochem. J., 443, 39-47.
19. Jolma, A. et al. (2010) Multiplexed massively parallel SELEX for characterization of human transcription factor binding specificities. Genome Res., 20, 861-873.
20. Jolma, A. et al. (2013) DNA binding specificities of human transcription factors. Cell, 152, 327-339.
21. Kaplan, T. et al. (2011) Quantitative models of the mechanisms that control genomewide patterns of transcription factor binding during early Drosophila development. PLoS Genet., 7, e1001290.
22. Kent, N.A. et al. (2004) Cbf1p is required for chromatin remodeling at promoterproximal CACGTG motifs in yeast. J. Biol. Chem., 279, 27116-27123.
23. Lin, C.Y. et al. (2012) Transcriptional amplification in tumor cells with elevated c-Myc. Cell, 151, 56-67.
24. MacIsaac, K.D. et al. (2006) An improved map of conserved regulatory sites for Saccharomyces cerevisiae. BMC Bioinformatics, 7, 113.
25. Maerkl, S.J. and Quake, S.R. (2007) A systems approach to measuring the binding energy landscapes of transcription factors. Science, 315, 233-237.
26. Maldonado, S. and Weber, R. (2010) Feature selection for support vector regression via kernel penalization. In: IJCNN 2010. Barcelona, Spain, pp. 1-7.
27. Man, T.K. and Stormo, G.D. (2001) Non-independence of Mnt repressor-operator interaction determined by a new quantitative multiple fluorescence relative affinity (QuMFRA) assay. Nucleic Acids Res., 29, 2471-2478.
28. Meinshausen, N. and Bühlmann, P. (2010) Stability selection. J. R. Stat. Soc. Ser. B, 72, 417-473.
29. Munteanu, A. and Gordâ n, R. (2013) Distinguishing between genomic regions bound by paralogous transcription factors. Recomb2013. Lect. Notes Comp. Sci., 7821, 145.
30. Nguyen, M.H. and de la Torre, F. (2010) Optimal feature selection for support vector machines. Pattern Recogn., 43, 584-591.
31. Nishi, K. et al. (1995) The GCR1 requirement for yeast glycolytic gene expression is suppressed by dominant mutations in the SGC1 gene, which encodes a novel basic-helix-loop-helix protein. Mol. Cell. Biol., 15, 2646-2653.
32. Pique-Regi, R. et al. (2011) Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data. Genome Res., 21, 447-455.
33. Rohs, R. et al. (2010) Origins of specificity in protein-DNA recognition. Annu. Rev. Biochem., 79, 233-269.
34. Sharon, E. et al. (2008) A feature-based approach to modeling protein-DNA interactions. PLoS Comput. Biol., 4, e1000154.
35. Siddharthan, R. (2010) Dinucleotide weight matrices for predicting transcription factor binding sites: Generalizing the position weight matrix. PLoS One, 5, e9722.
36. Smola, A.J. and Schölkopf, B. (2004) A tutorial on support vector regression. Stat. Comput., 14, 199-222.
37. Staden, R. (1984) Computer methods to locate signals in nucleic acid sequences. Nucleic Acids Res., 12 (1 Pt 2), 505-519.
38. Stormo, G.D. (2000) DNA binding sites: Representation and discovery. Bioinformatics, 16, 16-23.
39. Tibshirani, R. (1996) Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B, 58, 267-288.
40. Tomovic, A. and Oakeley, E.J. (2007) Position dependencies in transcription factor binding sites. Bioinformatics, 23, 933-941.
41. Vapnik, V.N. (1998) Statistical Learning Theory. Wiley, New-York, NY.
42. Weirauch, M.T. et al. (2013) Evaluation of methods for modeling transcriptionfactor sequence specificity. Nat. Biotechnol., 31, 126-134.
43. Workman, C.T. et al. (2005) enoLOGOS: A versatile web tool for energy normalized sequence logos. Nucleic Acids Res., 33, W389-W392.
44. Yang, J.B. and Ong, C.J. (2010) Feature selection for support vector regression using probabilistic prediction. In: ACM SIGKDD. ACM, New York, NY, USA, pp. 343-352.
45. Zhao, Y. and Stormo, G.D. (2011) Quantitative analysis demonstrates most transcription factors require only simple models of specificity. Nat. Biotechnol., 29, 480-483.
46. Zhao, Y. et al. (2009) Inferring binding energies from selected binding sites. PLoS Comput. Biol., 5, e1000590.
47. Zhao, Y. et al. (2012) Improved models for transcription factor binding site identification using nonindependent interactions. Genetics, 191, 781-790.
48. Zhou, Q. and Liu, J.S. (2004) Modeling within-motif dependence for transcription factor binding site predictions. Bioinformatics, 20, 909-916.
49. Zhou, X. and O'Shea, E.K. (2011) Integrated approaches reveal determinants of genome-wide binding and function of the transcription factor Pho4. Mol. Cell, 42, 826-836.
50. Zhu, C. et al. (2009) High-resolution DNA binding specificity analysis of yeast transcription factors. Genome Res., 19, 556-566.
51. Zhurkin, V.B. et al. (1991) Static and statistical bending of DNA evaluated by Monte Carlo simulations. Proc. Natl Acad. Sci. USA, 88, 7046-7050.