Selecting Oligonucleotide Probes for Whole-Genome Tiling Arrays with a Cross-Hybridization Potential

Abstract

Abstract-For designing oligonucleotide tiling arrays popular, current methods still rely on simple criteria like Hamming distance or longest common factors, neglecting base stacking effects which strongly contribute to binding energies. Consequently, probes are often prone to cross-hybridization which reduces the signal-to-noise ratio and complicates downstream analysis. We propose the first computationally efficient method using hybridization energy to identify specific oligonucleotide probes. Our Cross-Hybridization Potential (CHP) is computed with a Nearest Neighbor Alignment, which efficiently estimates a lower bound for the Gibbs free energy of the duplex formed by two DNA sequences of bounded length. It is derived from our simplified reformulation of t-gap insertion-deletionlike metrics. The computations are accelerated by a filter using weighted ungapped q-grams to arrive at seeds. The computation of the CHP is implemented in our software OSProbes, available under the GPL, which computes sets of viable probe candidates. The user can choose a trade-off between running time and quality of probes selected. We obtain very favorable results in comparison with prior approaches with respect to specificity and sensitivity for cross-hybridization and genome coverage with high-specificity probes. The combination of OSProbes and our Tileomatic method, which computes optimal tiling paths from candidate sets, yields globally optimal tiling arrays, balancing probe distance, hybridization conditions, and uniqueness of hybridization.

On-Chip Reconfigurable Hardware Accelerators for Popcount Computations

“…The methods proposed in [5] intensively compute popcounts for very large data sets and it is underlined that 2 International Journal of Reconfigurable Computing further performance increase can be possible in hardware accelerators of popcount algorithms. Similar problems arise in numerous bioinformatics applications such as [5][6][7][8][9][10][11][12]. For instance, in [9], Hamming distance filter for oligonucleotide probe candidate generation is built to select candidates below the given threshold.…”

“…Similar problems arise in numerous bioinformatics applications such as [5][6][7][8][9][10][11][12]. For instance, in [9], Hamming distance filter for oligonucleotide probe candidate generation is built to select candidates below the given threshold. The Hamming distance ( , ) between two vectors and is the number of positions they differ in.…”