We have developed a methodology we call ROMA (representational oligonucleotide microarray analysis), for the detection of the genomic aberrations in cancer and normal humans. By arraying oligonucleotide probes designed from the human genome sequence, and hybridizing with “representations” from cancer and normal cells, we detect regions of the genome with altered “copy number.” We achieve an average resolution of 30 kb throughout the genome, and resolutions as high as a probe every 15 kb are practical. We illustrate the characteristics of probes on the array and accuracy of measurements obtained using ROMA. Using this methodology, we identify variation between cancer and normal genomes, as well as between normal human genomes. In cancer genomes, we readily detect amplifications and large and small homozygous and hemizygous deletions. Between normal human genomes, we frequently detect large (100 kb to 1 Mb) deletions or duplications. Many of these changes encompass known genes. ROMA will assist in the discovery of genes and markers important in cancer, and the discovery of loci that may be important in inherited predispositions to disease
In this work, we explore the use of representations in conjunction with DNA microarray technology to measure gene copy number changes in cancer. We demonstrate that arrays of DNA probes derived from low-complexity representations can be used to detect amplifications, deletions, and polymorphic differences when hybridized to representations of genomic DNA. The method is both reproducible and verifiable, and is applicable even to microscopic amounts of primary tumors. We also present a mathematical model for array performance that is useful for designing and understanding DNA microarray hybridization protocols. The future applications and challenges of this approach are discussed.
Analysis of the genetic changes in human tumors is often problematical because of the presence of normal stroma and the limited availability of pure tumor DNA. However, large amounts of highly reproducible ''representations'' of tumor and normal genomes can be made by PCR from nanogram amounts of restriction endonuclease cleaved DNA that has been ligated to oligonucleotide adaptors. We show here that representations are useful for many types of genetic analyses, including measuring relative gene copy number, loss of heterozygosity, and comparative genomic hybridization. Representations may be prepared even from sorted nuclei from fixed and archived tumor biopsies.
We developed a sensitive polymerase chain reaction (PCR) panel, suitable for the detection of seven common respiratory viruses, to study the prevalence of viruses in nasal swabs obtained from clinically stable asthmatic children (n = 21), non-physician diagnosed asthmatic children with exercise-induced bronchoconstriction (EIB) (n = 16), and nonasthmatic, non-EIB controls (n = 33). The PCR panel detected viruses in 43/70 (61.4%) specimens but there were no significant differences in prevalence of these viruses between the three groups of children. These results indicate that clinically stable asthmatic and nonasthmatic children frequently harbor viruses in the upper respiratory tract.
We describe a method to make physical maps of genomes using correlative hybridization patterns of probes to random pools of BACs. We derive thereby an estimated distance between probes, and then use this estimated distance to order probes. To test the method, we used BAC libraries from Schizzosaccharomyces pombe. We compared our data to the known sequence assembly, in order to assess accuracy. We demonstrate a small number of significant discrepancies between our method and the map derived by sequence assembly. Some of these discrepancies may arise because genome order within a population is not stable; imposing a linear order on a population may not be biologically meaningful.
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