A statistical model for investigating binding probabilities of DNA nucleotide sequences using microarrays

Biometrics

Volumn 58, Issue 4, 2002, Pages 981-988

  Lee, Mei Ling Ting ^a,b,c   Bulyk, Martha L ^b   Whitmore, G A ^d   Church, George M ^b  

^a BRIGHAM AND WOMEN S HOSPITAL   (United States)

^b HARVARD MEDICAL SCHOOL   (United States)

^c HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

^d MCGILL UNIVERSITY   (Canada)

Author keywords

Binding probability; Dependence; Log linear model; Log probability model; Microarrays; Multistage ANOVA; Protein; Transcription factor

Indexed keywords

ANALYSIS OF VARIANCE (ANOVA); BIOCHEMISTRY; DNA; DNA SEQUENCES; MICROARRAYS; NUCLEOTIDES; REGRESSION ANALYSIS; TRANSCRIPTION;

BINDING AFFINITIES; BINDING PROBABILITY; DEPENDENCE; DNA NUCLEOTIDES; LOG-PROBABILITY MODEL; LOGLINEAR MODEL; MULTI-STAGES; MULTISTAGE ANOVA; PROBABILITY MODELLING; STATISTIC MODELING;

TRANSCRIPTION FACTORS;

DNA; NUCLEOTIDE; TRANSCRIPTION FACTOR;

BIOMETRY; DNA;

ANALYSIS OF VARIANCE; BINDING AFFINITY; BINDING SITE; BIOMETRY; DNA BINDING; DNA MICROARRAY; DNA SEQUENCE; INTERMETHOD COMPARISON; MEASUREMENT; PROBABILITY; PROTEIN BINDING; PROTEIN DNA INTERACTION; REVIEW; SEQUENCE ANALYSIS; STATISTICAL MODEL; ZINC FINGER MOTIF;

EID: 0036966431     PISSN: 0006341X     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.0006-341X.2002.00981.x     Document Type: Review

References (13)

1. Agresti, A. (1996). An Introduction to Categorical Data Analysis. New York: Wiley.
2. Bulyk, M. L., Huang, X., Choo, Y., and Church, G. M. (2001). Exploring the DNA-binding specificities of zinc fingers with DNA microarrays. Proceedings of the National Academy of Sciences (USA) 98, 7158-7163.
3. Bulyk, M. L., Johnson, P. L. F., and Church, G. M. (2002). Nucleotides of transcription factor binding sites exert interdependent effects on the binding affinities of transcription factors. Nucleic Acids Research 30, 12551261.
4. Choo, Y. and Klug, A. (1994). Toward a code for the interactions of zinc fingers with DNA: Selection of randomized fingers displayed on phage. Proceedings of the National Academy of Sciences, USA 91, 11163-11167.
5. Lee, M.-L. T., Kuo, F. C., Whitmore, G. A., and Sklar, J. L. (2000). Importance of replication in microarray gene expression studies: Statistical methods and evidence from repetitive cDNA hybridizations. Proceedings of the National Academy of Sciences 97, 9834-9839.
6. 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 Research 29, 2471-2478.
7. McCullagh, P. and Nelder, J. A. (1989). Generalized Linear Models, 2nd edition. London: Chapman and Hall.
8. Ponomarenko, M. P., Ponomarenko, J. V., Frolov, A. S., Podkolodnaya, O. A., Vorobyev, D. G., Kolchanov, N. A., and Overton, G. C. (1999). Oligonucleotide frequency matrices addressed to recognizing functional DNA sites. Bioinformatics 15, 631-643.
9. Quandt, K., Frech, K., Karas, H., Wingender, E., and Werner, T. (1995). MatInd and MatInspector: New fast and versatile tools for detection of consensus matches in nucleotide sequence data. Nucleic Acids Research 23, 4878-4884.
10. Staden, R. (1988). Methods to define and locate patterns of motifs in sequences. Comput. Appl. Biosci. 4, 53-60.
11. Stormo, G. D., Schneider, T. D., and Gold, L. (1986). Quantitative analysis of the relationship between nucleotide sequence and functional activity. Nucleic Acids Research 14, 6661-6679.
12. Wingender, E., Karas, H., and Knuppel, R. (1997). TRANSFAC database as a bridge between sequence data libraries and biological function. Proceedings of Pacific Symposium of Biocomputing, 477-485.
13. Zhang, M. Q. and Marr, T. G. (1993). A weight array method for splicing signal analysis. Computer Applications in Biosciences 9, 499-509.