SPARSE SIGNAL RECOVERY METHODS FOR VARIANT DETECTION IN NEXT-GENERATION SEQUENCING DATA

Recent advances in high-throughput sequencing technologies have led to the collection of vast quantities of genomic data. Structural variants (SVs) - rearrangements of the genome larger than one letter such as inversions, insertions, deletions, and duplications - are an important source of genetic variation and have been implicated in some genetic diseases. However, inferring SVs from sequencing data has proven to be challenging because true SVs are rare and are prone to low-coverage noise. In this paper, we attempt to mitigate the deleterious effects of low-coverage sequences by following a maximum likelihood approach to SV prediction. Specifically, we model the noise using Poisson statistics and constrain the solution with a sparsity-promoting l(1) penalty since SV instances should be rare. In addition, because offspring SVs inherit SVs from their parents, we incorporate familial relationships in the optimization problem formulation to increase the likelihood of detecting true SV occurrences. Numerical results are presented to validate our proposed approach.