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

Abstract

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.

Figure 1

Probe design from the shortest path on a graph. The de Bruijn graph for all possible DNA base doublets and one possible solution for a shortest path represented as a pseudo-Eulerian cycle (bold edges). The reverse complement solution is represented by dashed edges in the graph and also the inner cycle sequence. "Cutting" the circular sequence while retaining one overlapping base results in two sequences of total length 12 (containing all doublets) as compared to the length of all non-overlapping concatenated doublets 2 * 4² = 32. Cutting the circular sequence at different points allows screening multiple replicates and helps identify biases in sequence recognition preferences. Reverse complement strands for the replicates are not shown.

Figure 2

Distribution of putative PBM probe hits for Rap1. Frequency of array probe hits distributed by number of potential binding sites per probe. All sequences one or two mutations away from the consensus sequence are assumed to bind.

Figure 3

Distribution of putative PBM probe hits for TBP. Frequency of array probe hits distributed by number of potential binding sites per probe. All sequences one or two mutations away from the consensus sequence are assumed to bind.

Figure 4

Distribution of putative PBM probe hits for 100 random transcription factor binding sites of length 10. Frequency of array probe hits distributed by number of potential binding sites per probe. The data is averaged over 100 random 10-mer binding sites. For each 10-mer, all sequences one or two mutations away from the consensus sequence are assumed to bind.

Figure 5

Robustness of designed array and Gibbs Sampler to addition of noise. Starting with a set of 10-mer Rap1 TRANSFAC binding sites, the effect of added noise is measured as correlation of the original PWM with that derived from 100 Gibbs Sampler-runs. Each level of noise is represented by the standard box-and-whisker plot. In the 0–50% noise range, the boxes are so small that they are essentially represented by a single line.