F Li1, G D Stormo. 1. Department of Genetics, Washington University School of Medicine, St Louis, MO 63110, USA. lif@ural.wustl.edu
Abstract
MOTIVATION: High density DNA oligo microarrays are widely used in biomedical research. Selection of optimal DNA oligos that are deposited on the microarrays is critical. Based on sequence information and hybridization free energy, we developed a new algorithm to select optimal short (20-25 bases) or long (50 or 70 bases) oligos from genes or open reading frames (ORFs) and predict their hybridization behavior. Having optimized probes for each gene is valuable for two reasons. By minimizing background hybridization they provide more accurate determinations of true expression levels. Having optimum probes minimizes the number of probes needed per gene, thereby decreasing the cost of each microarray, raising the number of genes on each chip and increasing its usage. RESULTS: In this paper we describe algorithms to optimize the selection of specific probes for each gene in an entire genome. The criteria for truly optimum probes are easily stated but they are not computable at all levels currently. We have developed an heuristic approach that is efficiently computable at all levels and should provide a good approximation to the true optimum set. We have run the program on the complete genomes for several model organisms and deposited the results in a database that is available on-line (http://ural.wustl.edu/~lif/probe.pl). AVAILABILITY: The program is available upon request.
MOTIVATION: High density DNA oligo microarrays are widely used in biomedical research. Selection of optimal DNA oligos that are deposited on the microarrays is critical. Based on sequence information and hybridization free energy, we developed a new algorithm to select optimal short (20-25 bases) or long (50 or 70 bases) oligos from genes or open reading frames (ORFs) and predict their hybridization behavior. Having optimized probes for each gene is valuable for two reasons. By minimizing background hybridization they provide more accurate determinations of true expression levels. Having optimum probes minimizes the number of probes needed per gene, thereby decreasing the cost of each microarray, raising the number of genes on each chip and increasing its usage. RESULTS: In this paper we describe algorithms to optimize the selection of specific probes for each gene in an entire genome. The criteria for truly optimum probes are easily stated but they are not computable at all levels currently. We have developed an heuristic approach that is efficiently computable at all levels and should provide a good approximation to the true optimum set. We have run the program on the complete genomes for several model organisms and deposited the results in a database that is available on-line (http://ural.wustl.edu/~lif/probe.pl). AVAILABILITY: The program is available upon request.
Authors: Andrea Barczak; Madeleine Willkom Rodriguez; Kristina Hanspers; Laura L Koth; Yu Chuan Tai; Benjamin M Bolstad; Terence P Speed; David J Erle Journal: Genome Res Date: 2003-06-12 Impact factor: 9.043
Authors: Rui Mei; Earl Hubbell; Stefan Bekiranov; Mike Mittmann; Fred C Christians; Mei-Mei Shen; Gang Lu; Joy Fang; Wei-Min Liu; Tom Ryder; Paul Kaplan; David Kulp; Teresa A Webster Journal: Proc Natl Acad Sci U S A Date: 2003-09-19 Impact factor: 11.205
Authors: Stefan A Haas; Marc Hild; Anthony P H Wright; Torsten Hain; Driss Talibi; Martin Vingron Journal: Nucleic Acids Res Date: 2003-10-01 Impact factor: 16.971