Chromosomal abnormalities are a source of common genetic variation and disease. The specific alterations responsible for a given disease are often difficult to identify, especially when the mutation is present in only a subset of somatic cells. In this manuscript, novel algorithmic approaches and visualization techniques are presented, which advance current capabilities for detection and prioritization of genetic abnormalities in microarray and next-generation sequencing data. The triPOD software implements a new algorithm, the Parent-of-Origin-based Detection (POD) method, and specializes in the detection of low-level mosaic abnormalities in microarray data. An additional mosaicism detection algorithm is described, which is based on the detection of local deviations from the baseline distribution of missing genotypes. The R package, HapShare, is useful for visualizing and estimating haplotype sharing in pedigrees. These new algorithms allow for improved detection and interpretation of genetic abnormalities.
|School:||The Johns Hopkins University|
|School Location:||United States -- Maryland|
|Source:||DAI-B 74/12(E), Dissertation Abstracts International|
|Subjects:||Molecular biology, Genetics, Bioinformatics|
|Keywords:||Abnormality detection, DNA mixtures, Genomic data, Mosaicism, Next-generation sequencing|
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