Mutation at the Human D1S80 Minisatellite Locus

Little is known about the general biology of minisatellites. The purpose of this study is to examine repeat mutations from the D1S80 minisatellite locus by sequence analysis to elucidate the mutational process at this locus. This is a highly polymorphic minisatellite locus, located in the subtelomeric region of chromosome 1. We have analyzed 90,000 human germline transmission events and found seven (7) mutations at this locus. The D1S80 alleles of the parentage trio, the child, mother, and the alleged father were sequenced and the origin of the mutation was determined. Using American Association of Blood Banks (AABB) guidelines, we found a male mutation rate of 1 . 0 4 × 1 0− 4 and a female mutation rate of 5 . 1 8 × 1 0− 5 with an overall mutation rate of approximately 7 . 7 7 × 1 0− 5. Also, in this study, we found that the identified mutations are in close proximity to the center of the repeat array rather than at the ends of the repeat array. Several studies have examined the mutational mechanisms of the minisatellites according to infinite allele model (IAM) and the one-step stepwise mutation model (SMM). In this study, we found that this locus fits into the one-step mutation model (SMM) mechanism in six out of seven instances similar to STR loci.

Towards the prediction of mutations in genomic sequences

Bio-systems are inherently complex information processing systems. Furthermore, physiological complexities of biological systems limit the formation of a hypothesis in terms of behavior and the ability to test hypothesis. More importantly the identification and classification of mutation in patients are centric topics in today's cancer research. Next generation sequencing (NGS) technologies can provide genome-wide coverage at a single nucleotide resolution and at reasonable speed and cost. The unprecedented molecular characterization provided by NGS offers the potential for an individualized approach to treatment. These advances in cancer genomics have enabled scientists to interrogate cancer-specific genomic variants and compare them with the normal variants in the same patient. Analysis of this data provides a catalog of somatic variants, present in tumor genome but not in the normal tissue DNA. In this dissertation, we present a new computational framework to the problem of predicting the number of mutations on a chromosome for a certain patient, which is a fundamental problem in clinical and research fields. We begin this dissertation with the development of a framework system that is capable of utilizing published data from a longitudinal study of patients with acute myeloid leukemia (AML), who's DNA from both normal as well as malignant tissues was subjected to NGS analysis at various points in time. By processing the sequencing data at the time of cancer diagnosis using the components of our framework, we tested it by predicting the genomic regions to be mutated at the time of relapse and, later, by comparing our results with the actual regions that showed mutations (discovered at relapse time). We demonstrate that this coupling of the algorithm pipeline can drastically improve the predictive abilities of searching a reliable molecular signature. Arguably, the most important result of our research is its superior performance to other methods like Radial Basis Function Network, Sequential Minimal Optimization, and Gaussian Process. In the final part of this dissertation, we present a detailed significance, stability and statistical analysis of our model. A performance comparison of the results are presented. This work clearly lays a good foundation for future research for other types of cancer.^