Exome sequencing reveals pathogenic mutations in 91 strains of mice with Mendelian disorders.

Spontaneously arising mouse mutations have served as the foundation for understanding gene function for more than 100 years. We have used exome sequencing in an effort to identify the causative mutations for 172 distinct, spontaneously arising mouse models of Mendelian disorders, including a broad range of clinically relevant phenotypes. To analyze the resulting data, we developed an analytics pipeline that is optimized for mouse exome data and a variation database that allows for reproducible, user-defined data mining as well as nomination of mutation candidates through knowledge-based integration of sample and variant data. Using these new tools, putative pathogenic mutations were identified for 91 (53%) of the strains in our study. Despite the increased power offered by potentially unlimited pedigrees and controlled breeding, about half of our exome cases remained unsolved. Using a combination of manual analyses of exome alignments and whole-genome sequencing, we provide evidence that a large fraction of unsolved exome cases have underlying structural mutations. This result directly informs efforts to investigate the similar proportion of apparently Mendelian human phenotypes that are recalcitrant to exome sequencing.

The clinical trial landscape in oncology and connectivity of somatic mutational profiles to targeted therapies.

BACKGROUND: Precision medicine in oncology relies on rapid associations between patient-specific variations and targeted therapeutic efficacy. Due to the advancement of genomic analysis, a vast literature characterizing cancer-associated molecular aberrations and relative therapeutic relevance has been published. However, data are not uniformly reported or readily available, and accessing relevant information in a clinically acceptable time-frame is a daunting proposition, hampering connections between patients and appropriate therapeutic options. One important therapeutic avenue for oncology patients is through clinical trials. Accordingly, a global view into the availability of targeted clinical trials would provide insight into strengths and weaknesses and potentially enable research focus. However, data regarding the landscape of clinical trials in oncology is not readily available, and as a result, a comprehensive understanding of clinical trial availability is difficult. RESULTS: To support clinical decision-making, we have developed a data loader and mapper that connects sequence information from oncology patients to data stored in an in-house database, the JAX Clinical Knowledgebase (JAX-CKB), which can be queried readily to access comprehensive data for clinical reporting via customized reporting queries. JAX-CKB functions as a repository to house expertly curated clinically relevant data surrounding our 358-gene panel, the JAX Cancer Treatment Profile (JAX CTP), and supports annotation of functional significance of molecular variants. Through queries of data housed in JAX-CKB, we have analyzed the landscape of clinical trials relevant to our 358-gene targeted sequencing panel to evaluate strengths and weaknesses in current molecular targeting in oncology. Through this analysis, we have identified patient indications, molecular aberrations, and targeted therapy classes that have strong or weak representation in clinical trials. CONCLUSIONS: Here, we describe the development and disseminate system methods for associating patient genomic sequence data with clinically relevant information, facilitating interpretation and providing a mechanism for informing therapeutic decision-making. Additionally, through customized queries, we have the capability to rapidly analyze the landscape of targeted therapies in clinical trials, enabling a unique view into current therapeutic availability in oncology.

Pathway Commons at virtual cell: use of pathway data for mathematical modeling.

UNLABELLED: Pathway Commons is a resource permitting simultaneous queries of multiple pathway databases. However, there is no standard mechanism for using these data (stored in BioPAX format) to annotate and build quantitative mathematical models. Therefore, we developed a new module within the virtual cell modeling and simulation software. It provides pathway data retrieval and visualization and enables automatic creation of executable network models directly from qualitative connections between pathway nodes. AVAILABILITY AND IMPLEMENTATION: Available at Virtual Cell (http://vcell.org/). Application runs on all major platforms and does not require registration for use on the user's computer. Tutorials and video are available at user guide page.