GAPWM: A genetic algorithm method for optimizing a position weight matrix

Bioinformatics

Volumn 23, Issue 10, 2007, Pages 1188-1194

  Li, Leping ^a   Liang, Yu ^a   Bass, Robert L ^a  

^a NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

OCTAMER TRANSCRIPTION FACTOR 4; PROTEIN P53; TRANSCRIPTION FACTOR SOX2;

ANALYTICAL ERROR; AREA UNDER THE CURVE; ARTICLE; BINDING SITE; CALCULATION; COMPUTER PROGRAM; CONTROLLED STUDY; FALSE POSITIVE RESULT; GENETIC ALGORITHM; GENETIC CONSERVATION; HUMAN; MOLECULAR MODEL; MOUSE; NONHUMAN; POSITION WEIGHT MATRIX; PRIORITY JOURNAL; PROTEIN MOTIF; RECEIVER OPERATING CHARACTERISTIC; SCORING SYSTEM; SENSITIVITY AND SPECIFICITY; SEQUENCE ANALYSIS;

EID: 34447340240     PISSN: 13674803     EISSN: 13674811     Source Type: Journal    
DOI: 10.1093/bioinformatics/btm080     Document Type: Article

References (33)

1. Bailey,T.L. and Elkan,C. (1994) Fitting a mixture model by expectation maximization to discover motifs in biopolymers. In Proceedings of Second International conference on Intelligent Systems for Molecular Biology. AAAI Press, Menlo Park, CA, pp. 28-36.
2. Boyer,L.A. et al. (2005) Core transcriptional regulatory circuitry in human embryonic stem cells. Cell, 122, 947-956.
3. Cartharius,K. et al. (2005) MatInspector and beyond: Promoter analysis based on transcription factor binding sites. Bioinformatics 21, 2933-2942.
4. Ellrott,K. et al. (2002) Identifying transcription factor binding sites through Markov chain optimization. Bioinformatics, 18, S100-S109.
5. ENCODE Project Consortium (2004) The ENCODE (ENCyclopedia Of DNA Elements) Project. Science, 306, 636-640.
6. Frith,M.C. et al. (2004) Detection of functional DNA motifs via statistical over-representation. Nucleic Acids Res., 32, 1372-1378.
7. Goldberg,D.E. (1989) Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA, USA.
8. Huang,W. et al. (2006) Optimized mixed Markov models for motif identification. BMC Bioinformatics, 7, 279-296.
9. Hertz,G.Z. and Stormo,G.D. (1999) Identifying DNA and protein patterns with statistically significant alignments of multiple sequences. Bioinformatics, 15, 563-577.
10. Hughes,J.D. et al. (2000) Computational identification of cis-regulatory elements associated with groups of functionally related genes in Saccharomyces cerevisiae. J. Mol. Biol., 296, 1205-1214.
11. Ji,H. et al. (2006) A comparative analysis of genome-wide chromatin immunoprecipitation data for mammalian transcription factors. Nucleic Acids Res., 34, e146-e157.
12. Kel,A.E. et al. (2003) MATCH: A tool for searching transcription factor binding sites in DNA sequences. Nucleic Acids Res., 31 3576-3579.
13. Kim,T.H. and Ren,B. (2006) Genome-wide analysis of protein-DNA interactions. Annu. Rev. Genomics Hum. Genet., 7, 81-102.
14. Knuppel,R. et al. (1994) TRANSFAC retrieval program: A network model database of eukaryotic transcription regulating sequences and proteins. J. Comput. Biol., 1, 191-198.
15. Li,L. et al. (2001) Gene selection for sample classification based on gene expression data: Study of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics, 17, 1131-1142.
16. Liu,X. et al. (2001) BioProspector: Discovering conserved DNA motifs in upstream regulatory regions of co-expressed genes. Pac. Symp. Biocomput., 6, 127-138.
17. Liu,X.S. et al. (2002) An algorithm for finding protein-DNA binding sites with applications to chromatin-immunoprecipitation microarray experiments. Nat. Biotechnol., 20, 835-839.
18. Loh,Y.H. et al. (2006) The Oct4 and Nanog transcription network regulates pluripotency in mouse embryonic stem cells. Nat. Genet., 38, 431-440.
19. Matys,V. et al. (2006) TRANSFAC and its module TRANSCompel: transcriptional gene regulation in eukaryotes. Nucleic Acids Res., 34, D108-D110.
20. Mendes,N.D. et al. (2006) MUSA: A parameter free algorithm for the identification of biologically significant motifs. Bioinformatics, 22, 2996-3002.
21. Naughton,B.T. et al. (2006) A graph-based motif detection algorithm models complex nucleotide dependencies in transcription factor binding sites. Nucleic Acids Res., 34, 5730-5739.
22. Ng,P. et al. (2005) Gene identification signature (GIS) analysis for transcriptome characterization and genome annotation. Nat. Methods, 2, 105-211.
23. Pavesi,G. et al. (2004) Weeder Web: Discovery of transcription factor binding sites in a set of sequences from co-regulated genes. Nucleic Acids Res., 32, W199-W203.
24. Quandt,K. et al. (1995) MatInd and MatInspector: New fast and versatile tools for detection of consensus matches in nucleotide sequence data. Nucleic Acids Res., 23, 4878-4884.
25. Sandelin,A. et al. (2004) JASPAR: An open-access database for eukaryotic transcription factor binding profiles. Nucleic Acids Res. 32, D91-D94.
26. Stormo,G.D. (2000) DNA binding sites: Representation and discovery. Bioinformatics, 16, 16-23.
27. Thijs,G. et al. (2001) A higher-order background model improves the detection of promoter regulatory elements by Gibbs sampling. Bioinformatics, 17, 1113-1122.
28. Tompa,M. et al. (2005) Assessing computational tools for the discovery of transcription factor binding sites. Nat. Biotechnol., 23, 137-144.
29. Wasserman,W.W. and Sandelin,A. (2004) Applied bioinformatics for the identification of regulatory elements. Nat. Genet. Rev., 5 276-287.
30. Wei,C.L. et al. (2006) A global map of p53 transcription-factor binding sites in the human genome. Cell, 124, 207-219.
31. Wei,Z. and Jensen,S.T. (2006) GAME: Detecting cis-regulatory elements using a genetic algorithm. Bioinformatics, 22, 1577-1584.
32. Won,K.J. et al. (2004) Training HMM structure with genetic algorithm for biological sequence analysis. Bioinformatics, 20 3613-3619.
33. Zhou,Q. and Liu,J.S. (2004) Modeling within-motif dependence for transcription factor binding site predictions. Bioinformatics, 20, 909-916.