Negative Binomial Optimization for Biomedical Structural Variant Signal Reconstruction

Abstract

Structural variants (SVs) - novel adjacencies in an individual's genome - lead to genomic diversity across all organisms. When DNA fragments of an unknown genome are compared to a reference genome, errors in sequencing and mapping obscure true genomic rearrangements. When the sequencing coverage is low, this may lead to high false positive rates in predicted SVs. In this paper, we propose a novel maximum likelihood approach to SV prediction incorporating low-coverage sequencing data and coverage distribution. Specifically, we address mean and variance assumptions proposed by Poisson models and develop a Negative Binomial framework which reflects a more accurate representation of DNA fragments in an individual's genome. We incorporate both sparsity and inheritance in our model with an ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> penalty and linear constraints, respectively. We validate our model on both simulated and real genomic data of related individuals. Moreover, our results indicate an improvement on thresholding observations of candidate variants.