Stochastic modeling of mitochondrial polymerase gamma replication and novel algorithms to enrich rare disease alleles and detect tumor somatic mutations in deep sequencing data

Abstract

The activity of polymerase ã (pol ã) is complicated. To understand how its kinetics values affect the final function of the pol ã, I created a stochastic model of pol ã replication on the single nucleotide incorporation level. Using this model, I analyzed replication pauses of both wild-type and pathogenic mutated pol ã and discovered that the pausing time is proportional to the number of disassociations occurring in each forward step of the pol ã, and studied mitochondrial toxicity caused by nucleoside analogs in antiretroviral treatment.

To enrich the yield of rare disease alleles, a probability-based approach, SampleSeq, has been developed to select samples for a targeted resequencing experiment that outperforms over sampling based on genotypes at associated SNPs from GWAS data. To detect somatic mutations, novel algorithms have been developed to detect base substitution and loss of heterozygosity, using next-generation sequencing data for normal-tumor sample pairs.