Design of a combinatorial DNA microarray for protein-DNA interaction studies

BMC Bioinformatics. 2006 Oct 3;7:429. doi: 10.1186/1471-2105-7-429.


Background: Discovery of precise specificity of transcription factors is an important step on the way to understanding the complex mechanisms of gene regulation in eukaryotes. Recently, double-stranded protein-binding microarrays were developed as a potentially scalable approach to tackle transcription factor binding site identification.

Results: Here we present an algorithmic approach to experimental design of a microarray that allows for testing full specificity of a transcription factor binding to all possible DNA binding sites of a given length, with optimally efficient use of the array. This design is universal, works for any factor that binds a sequence motif and is not species-specific. Furthermore, simulation results show that data produced with the designed arrays is easier to analyze and would result in more precise identification of binding sites.

Conclusion: In this study, we present a design of a double stranded DNA microarray for protein-DNA interaction studies and show that our algorithm allows optimally efficient use of the arrays for this purpose. We believe such a design will prove useful for transcription factor binding site identification and other biological problems.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • 3' Flanking Region
  • 5' Flanking Region
  • Algorithms
  • Base Sequence
  • Binding Sites
  • Computational Biology
  • Computer Simulation
  • DNA / metabolism*
  • DNA Probes / genetics
  • DNA-Binding Proteins / metabolism
  • Oligonucleotide Array Sequence Analysis / methods*
  • Oligonucleotides / genetics
  • Saccharomyces cerevisiae Proteins / metabolism
  • Telomere-Binding Proteins / metabolism
  • Transcription Factors / metabolism*


  • DNA Probes
  • DNA-Binding Proteins
  • Oligonucleotides
  • RAP1 protein, S cerevisiae
  • Saccharomyces cerevisiae Proteins
  • Telomere-Binding Proteins
  • Transcription Factors
  • DNA