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- Title
Optimized design and assessment of whole genome tiling arrays.
- Authors
Stefan Gräf; Fiona G. G. Nielsen; Stefan Kurtz; Martijn A. Huynen; Ewan Birney; Henk Stunnenberg; Paul Flicek
- Abstract
Motivation: Recent advances in microarray technologies have made it feasible to interrogate whole genomes with tiling arrays and this technique is rapidly becoming one of the most important high-throughput functional genomics assays. For large mammalian genomes, analyzing oligonucleotide tiling array data is complicated by the presence of non-unique sequences on the array, which increases the overall noise in the data and may lead to false positive results due to cross-hybridization. The ability to create custom microarrays using maskless array synthesis has led us to consider ways to optimize array design characteristics for improving data quality and analysis. We have identified a number of design parameters to be optimized including uniqueness of the probe sequences within the whole genome, melting temperature and self-hybridization potential. Results: We introduce the uniqueness score, U, a novel quality measure for oligonucleotide probes and present a method to quickly compute it. We show that U is equivalent to the number of shortest unique substrings in the probe and describe an efficient greedy algorithm to design mammalian whole genome tiling arrays using probes that maximize U. Using the mouse genome, we demonstrate how several optimizations influence the tiling array design characteristics. With a sensible set of parameters, our designs cover 78% of the mouse genome including many regions previously considered ‘untilable’ due to the presence of repetitive sequence. Finally, we compare our whole genome tiling array designs with commercially available designs. Availability: Source code is available under an open source license from http://www.ebi.ac.uk/~graef/arraydesign/ Contact: flicek@ebi.ac.uk
- Publication
Bioinformatics, 2007, Vol 23, Issue 13, pi195
- ISSN
1367-4803
- Publication type
Academic Journal
- DOI
10.1093/bioinformatics/btm200