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Copy number variations have been linked to numerous genetic diseases including cancer, Parkinson's disease, pancreatitis, and lupus. While current best practices for CNV detection often require using microarrays for detecting large CNVs or multiplex ligation-dependent probe amplification (MLPA) for gene-sized CNVs, new methods have been developed with the goal of replacing both of these specialized assays with bioinformatic analysis applied to next-generation sequencing (NGS) data. Because NGS is already used by clinical labs to detect small coding variants, this approach reduces associated costs, resources, and analysis time. This chapter provides an overview of the various approaches to CNV detection via NGS data, and examines VS-CNV, a commercial tool developed by Golden Helix, which provides robust CNV calling capabilities for both gene panel and exome data.

This paper is part of an ongoing effort to facilitate wider acceptance and further development of the IEEE Std 1232-2010 Standard for Artificial Intelligence Exchange and Service Tie to All Test Environments (AI-ESTATE). To that end, we describe a tool named SAPPHIRE TM , which includes an implementation of AI-ESTATE in Java and a corresponding GUI tool that supports model creation and diagnostic inference of the standard's Bayes Network Model (BNM). In addition, we describe extensions to the BNM as well as additional reasoner services that allow for representation and inference over dynamic Bayesian networks (DBNs) for standards-based prognostics.

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