A Note on Optimal Sampling Strategy for Structural Variant Detection Using Optical Mapping
This work addresses the need for efficient clinical tools in genomics by providing an incremental optimization approach for sampling in structural variant detection.
The paper tackles the problem of determining the optimal sampling strategy for detecting structural variants using optical mapping technologies, showing that if the technology can sample most chromosomal fragments, relatively little biological material is needed for high-confidence detection.
Structural variants compose the majority of human genetic variation, but are difficult to assess using current genomic sequencing technologies. Optical mapping technologies, which measure the size of chromosomal fragments between labeled markers, offer an alternative approach. As these technologies mature towards becoming clinical tools, there is a need to develop an approach for determining the optimal strategy for sampling biological material in order to detect a variant at some threshold. Here we develop an optimization approach using a simple, yet realistic, model of the genomic mapping process using a hyper-geometric distribution and {probabilistic} concentration inequalities. Our approach is both computationally and analytically tractable and includes a novel approach to getting tail bounds of hyper-geometric distribution. We show that if a genomic mapping technology can sample most of the chromosomal fragments within a sample, comparatively little biological material is needed to detect a variant at high confidence.