Rapid advances in cancer treatment have led to the generation of large sums of data. Collecting, organizing, and understanding this rapidly evolving and growing amount of data is a challenge to the modern bioinformatician and/or medical informatician. As big data continues to become a greater presence in the cancer treatment landscape it is imperative to maintain a robust and flexible data pipeline. GenomOncology’s API and analytics tools provide such a solution. Our tools allow for institutions to integrate both our data curation and analytics functionalities into their existing infrastructure.
GenomOncology Application Programming Interface (API) provides a REST-based interface into the GenomOncology Knowledge Management System (KMS). Examples of functionality provided by the KMS and API include...
The annotator is a high-throughput, parallel processing tool that injects annotations into a copy of a VCF file. Highly configurable command-line tool that should fit into most data pipelines without customization.
Previous implementations of this flexible architecture has been loaded with billionss of annotation data points. Seamlessly providing hundreds of data points for files ranging from small-panel VCFs to whole exome VCFs. The Annotations API which drives the annotator is additionally able to perform a variety of bioinformatic calculations such as HGVS, exon distance, exon numbering, mutation type, etc. The GenomOncology base annotations include datasets such as ExAC, EVS, dbSNFP, dbSNP, ClinVar, GNOMAD, 1000 Genomes, SIFT, COSMIC, Mutation Taster, etc. The flexibility and extensibility of our system allows for the loading of any additional licensed materials into the system with custom parsers (VCF, TSV, CSV, JSON, XML, etc)
GenomAnalytics is a visualization and statistical analysis software suite composed of reusable widgets allowing users to design analyses and dashboards without any coding required. GenomAnalytics utilizes GenomOncology's Knowledge Management System to enable users to analyze any molecular, clinical, demographic, or recommendation data housed within. The system incorporates basic summarization widgets; clinical analysis widgets such as survival curves; data specific widgets such as heat maps for expression data and statistical clustering; and grouping widgets. Integration with R and Python-based data science tools is available, along with an integrated Jupyter Notebook environment.