Supporting data for "Enhancing Knowledge Discovery from Cancer Genomics Data with Galaxy"
Dataset type: Software
Data released on March 07, 2017
The field of cancer genomics has demonstrated the power of massively parallel sequencing techniques to inform on the genes and specific alterations that drive tumor onset and progression. Although large comprehensive sequence data sets continue to be made increasingly available, data analysis remains an ongoing challenge, particularly for laboratories lacking dedicated resources and bioinformatics expertise.
To address this, we have produced a collection of Galaxy tools that represent many popular algorithms for detecting somatic genetic alterations from cancer genome and exome data. We developed new methods for parallelization of these tools within Galaxy to accelerate runtime and have demonstrated their usability and summarized their runtimes on multiple cloud service providers. Some tools represent extensions or refinement of existing toolkits to yield visualizations suited to cohort-wide cancer genomic analysis. For example, we present Oncocircos and Oncoprintplus, which generate data-rich summaries of exome-derived somatic mutation. Workflows that integrate these to achieve data integration and visualizations are demonstrated on a cohort of 96 diffuse large B-cell lymphomas and enabled the discovery of multiple candidate lymphoma-related genes. Our toolkit is available from our GitHub repository as Galaxy tool and dependency definitions and has been deployed using virtualization on multiple platforms including Docker.
Read the peer-reviewed publication(s):
Albuquerque, M. A., Grande, B. M., Ritch, E. J., Pararajalingam, P., Jessa, S., Krzywinski, M., … Morin, R. D. (2017). Enhancing knowledge discovery from cancer genomics data with Galaxy. GigaScience, 6(5). doi:10.1093/gigascience/gix015