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Group testing with DNA chips: generating designs and decoding experiments

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons50523

Schliep,  Alexander
Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50480

Rahmann,  Sven
Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Zitation

Schliep, A., Torney, D. C., & Rahmann, S. (2003). Group testing with DNA chips: generating designs and decoding experiments. In PROCEEDINGS OF THE 2nd IEEE COMPUTER SOCIETY BIOINFORMATICS CONFERENCE (CSB '03) (pp. 84-93). IEEE.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0010-8B32-5
Zusammenfassung
DNA microarrays are a valuable tool for massively parallel DNA-DNA hybridization experiments. Currently, most applications rely on the existence of sequence-specific oligonucleotide probes. In large families of closely related target sequences, such as different virus subtypes, the high degree of similarity often makes it impossible to find a unique probe for every target. Fortunately, this is unnecessary. We propose a microarray design methodology based on a group testing approach. While probes might bind to multiple targets simultaneously, a properly chosen probe set can still unambiguously distinguish the presence of one target set from the presence of a different target set. Our method is the first one that explicitly takes cross-hybridization and experimental errors into account while accommodating several targets. The approach consists of three steps: (1) Pre-selection of probe candidates, (2) Generation of a suitable group testing design, and (3) Decoding of hybridization results to infer presence or absence of individual targets. Our results show that this approach is very promising, even for challenging data sets and experimental error rates of up to 5%. On a data set of 28S rDNA sequences we were able to identify 660 sequences, a substantial improvement over a prior approach using unique probes which only identified 408 sequences.