Starting with copy number estimates for each marker (either taken directly from the vendor’s input file or calculated previously), the goal is to derive a list of regions where adjacent markers share the same copy number. PGS offers two algorithms for region detection: Genomic segmentation and Hidden Markov Model (HMM). Basically both algorithms examine trends across multiple adjacent markers. The genomic segmentation algorithm identifies breakpoints in the data, i.e., changes in copy number between two neighboring regions. The HMM algorithm looks for discrete changes of whole number copy number states (e.g., 0, 1, 2 … with no limit on the upper limit) and will find regions with those numbers of copies. Therefore, the HMM model performs better in cases of homogenous samples when the copy numbers can be anticipated (such as clinical syndromes with underlying chromosome or germ-line gene aberrations). Genomic segmentation is preferred for heterogeneous samples with unpredictable copy numbers (such as cancer because tissue biopsies often contain “contaminating” healthyCopy Number Analysis in Partek® Genomics Suite™ 6.6 10 tissue, and cancer cells are quite heterogeneous with respect to multiple chromosome aberrations). The number of copies of each marker created in the previous step will now be used to detect the genomic regions with copy number variation, i.e., to identify amplifications and deletions across the genome.

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