We have successfully demonstrated the feasibility of using the NGS platform to multiplex genetic tests for several rare diseases and the use of cloud computing for bioinformatics analysis as a relatively low-cost solution for implementing NGS in clinical laboratories.

Insertional mutagenesis screens in mice are used to identify individual genes that drive tumor formation. In these screens, candidate cancer genes are identified if their genomic location is proximal to a common insertion site (CIS) defined by high rates of transposon or retroviral insertions in a given genomic window. In this article, we describe a new method for defining CISs based on a Poisson distribution, the Poisson Regression Insertion Model, and show that this new method is an improvement over previously described methods. We also describe a modification of the method that can identify pairs and higher orders of co-occurring common insertion sites. We apply these methods to two data sets, one generated in a transposon-based screen for gastrointestinal tract cancer genes and another based on the set of retroviral insertions in the Retroviral Tagged Cancer Gene Database. We show that the new methods identify more relevant candidate genes and candidate gene pairs than found using previous methods. Identification of the biologically relevant set of mutations that occur in a single cell and cause tumor progression will aid in the rational design of single and combinatorial therapies in the upcoming age of personalized cancer therapy.

BackgroundThe introduction of next generation sequencing (NGS) has revolutionized molecular diagnostics, though several challenges remain limiting the widespread adoption of NGS testing into clinical practice. One such difficulty includes the development of a robust bioinformatics pipeline that can handle the volume of data generated by high-throughput sequencing in a cost-effective manner. Analysis of sequencing data typically requires a substantial level of computing power that is often cost-prohibitive to most clinical diagnostics laboratories.FindingsTo address this challenge, our institution has developed a Galaxy-based data analysis pipeline which relies on a web-based, cloud-computing infrastructure to process NGS data and identify genetic variants. It provides additional flexibility, needed to control storage costs, resulting in a pipeline that is cost-effective on a per-sample basis. It does not require the usage of EBS disk to run a sample.ConclusionsWe demonstrate the validation and feasibility of implementing this bioinformatics pipeline in a molecular diagnostics laboratory. Four samples were analyzed in duplicate pairs and showed 100% concordance in mutations identified. This pipeline is currently being used in the clinic and all identified pathogenic variants confirmed using Sanger sequencing further validating the software.