 Methodology articleRestriction Landmark Genomic Scanning (RLGS) spot identification by second generation virtual RLGS in multiple genomes with multiple enzyme combinationsDominic J Smiraglia1 1 Department of Cancer Genetics, and Comprehensive Cancer Center, Roswell Park Cancer Institute, Buffalo, NY, USA 2 Department of Computer Science and Engineering, and Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio, USA 3 Department of Pharmacology and Therapeutics, McGill University, Montreal, Quebec, Canada 4 Department of Molecular Virology, Immunology, and Medical Genetics, and Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio, USA 5 Department of Veterinary Biosciences, and Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio, USA 6 Department of Pharmacology, Davis Heart and Lung Research Institute, and Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio, USA 7 Department of Neurological Surgery and The Brain Tumor Research Center, and Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California, USA 8 Department of Biomedical Informatics, Davis Heart and Lung Research Institute, and Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio, USA 9 Department of Pathology and Laboratory Medicine, Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, California, USA 10 Department of Molecular Biology Institute, Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, California, USA 11 Department of Cell and Molecular Biology, and Comprehensive Cancer Center, Roswell Park Cancer Institute, Buffalo, NY, USA 12 Department of Pediatrics, McGill University, Montreal, Quebec, Canada 13 Department of Human Genetics, McGill University, Montreal, Quebec, Canada
BMC Genomics 2007, 8:446doi:10.1186/1471-2164-8-446
AbstractBackgroundRestriction landmark genomic scanning (RLGS) is one of the most successfully applied methods for the identification of aberrant CpG island hypermethylation in cancer, as well as the identification of tissue specific methylation of CpG islands. However, a limitation to the utility of this method has been the ability to assign specific genomic sequences to RLGS spots, a process commonly referred to as "RLGS spot cloning." ResultsWe report the development of a virtual RLGS method (vRLGS) that allows for RLGS spot identification in any sequenced genome and with any enzyme combination. We report significant improvements in predicting DNA fragment migration patterns by incorporating sequence information into the migration models, and demonstrate a median Euclidian distance between actual and predicted spot migration of 0.18 centimeters for the most complex human RLGS pattern. We report the confirmed identification of 795 human and 530 mouse RLGS spots for the most commonly used enzyme combinations. We also developed a method to filter the virtual spots to reduce the number of extra spots seen on a virtual profile for both the mouse and human genomes. We demonstrate use of this filter to simplify spot cloning and to assist in the identification of spots exhibiting tissue-specific methylation. ConclusionThe new vRLGS system reported here is highly robust for the identification of novel RLGS spots. The migration models developed are not specific to the genome being studied or the enzyme combination being used, making this tool broadly applicable. The identification of hundreds of mouse and human RLGS spot loci confirms the strong bias of RLGS studies to focus on CpG islands and provides a valuable resource to rapidly study their methylation. |
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