Motivation

Array-comparative genomic hybridization (aCGH) technology enables rapid, high-resolution analysis of genomic rearrangements. With the use of it, genome copy number changes and rearrangement breakpoints can be detected and analyzed at resolutions down to a few kilobases. An exon array CGH approach proposed recently accurately measures copy-number changes of individual exons in the human genome. The crucial and highly non-trivial starting task is the design of an array, i.e. the choice of appropriate set of oligos. The success of the whole high-level analysis depends on the design quality.

Materials and Methods

The dataset, we used in our study, come from 60 arrays hybridized with DNA from subjects with epilepsy, autism, heart defects and mental disorders. Each experiment was performed on the 180 K exon targeted oligonucleotide array. Based on those data we generated several smaller (optimized) designs by selecting various subsets of oligos from original one. Aiming in testing the robustness of segmentation we enhance the DNAcopy method by incorporating parametrized noise model. Finally, we used this algorithm to perform the comparison among reduced designs.

Results

For each probe we computed, using a set of Kolmogorov-Smirnov tests, cumulative properties, which reflects the oligo suitability in the context of its surrounding. To investigate the influence of design optimization strategy on segmentation robustness several approaches for probes selection were tested, including uniform sampling and most/least suitable oligo removal. Some of those methods reduced the nr of probes with a little loss of segmentation robustness. One can benefit from this strategy especially for targeted arrays used for the diagnosis of specific chromosomal aberrations.

Discussion

The investigation shows that while optimizing the design it is crucial to find a tradeoff between keeping uniform distribution and selecting the best performing probes. We discovered that the results of designs comparisons greatly depends on the definition of distance between two segmentations. Finally, we found new robustness measure very useful in evaluation of optimized design performance of rearrangement detection, and its resistance to the noise, in comparison to the original array.

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