Tileomatic: Design of oligonucleotide arrays

What is Tileomatic

Tileomatic is a software to design optimal spaced oligonucleotide tiling arrays. Tileomatic balances the three main conflicting objectives in tiling array design—oligonucleotide probe spacing, probe quality and hybridization conditions—to arrive at a globally optimal solution. It is most effective for spaced tiling arrays where variations in spacing can reduce variations in hybridization conditions and avoid having to use low-quality of cross-hybridizing probes. Candidate oligonucleotide probe sets are pre-computed with our OSProbes software

How do I use Tileomatic?

Tileomatic is implemented in a webservice at http://tileomatic.molgen.mpg.de/ It works in a two-phase process: Candidate oligonucleotide probe sets are pre-computed with our OSProbes software. As this requires a considerable computational effort for large genomes, we make a range of model-organisms and candidate sets available; you can request addition of candidate sets with different parameters or for difference model parameters. Given a candidate set, the final array can be efficiently computed with our algorithm in the second phase in two easy steps if you select the new array design menu.

For further information see the main website at http://tileomatic.molgen.mpg.de/ or contact Alexander Schliep (alexander@schlieplab.org). This software is a result of or used in the following projects: Tiling.

Team

Members: Alexander Schliep, Alexander Schliep, Christoph Hafemeister. Collaborators: Roland Krause (University of Luxembourg).

Publications

Hafemeister et al.. Selecting oligonucleotide probes for whole-genome tiling arrays with a cross-hybridization potential. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2011, 8:6, 1642–1652.

Schliep et al.. Efficient algorithms for the computational design of optimal tiling arrays. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2008, 557–567.

Schliep et al.. Efficient Computational Design of Tiling Arrays Using a Shortest Path Approach. In Algorithms in Bioinformatics, Springer Berlin / Heidelberg, Volume 4645/2007, 383–394, 2007.