Assigning polymorphic endogenous retrovirus integration sites using a mixture model
Year: 2015
Type: preprint
Abstract: Structural variation occurs in the genomes of individuals because of the different positions occupied by repetitive genome elements such as endogenous retroviruses, or ERVs. The presence or absence of ERVs can be determined by identifying the junction with the host genome using high-throughput sequence technology. The resulting data are a matrix giving the number of sequence reads assigned to each ERV-host junction sequence for each sampled individual. We present a novel two-component mixture of negative binomial distributions to model these counts and to assign a probability that a given ERV is present in a given individual, giving statistical and biological rationale for our choice. We explain ways in which our approach is superior to existing alternatives, including another form of two-component mixture model and the much more common approach of selecting a threshold count for declaring the presence of an ERV. We apply our method to a data set of ERV integrations in mule deer [Odocoileus hemious] from Oregon, Montana and Wyoming and blacktail deer from Oregon. In addition, we use the data to determine the relatedness of mule deer and find that mule deer from Oregon and Montana are more closely related to each other than they are to Wyoming animals based on the profile of shared virus integration sites.
Source: arXiv (Cornell University)
Authors: Le Bao, David R. Hunter, Mary Poss
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2265611497