From developing new computational
methods to applying them at scale, cancer
genomics experts are using Terra to access
data, share tools, and accelerate research
“Terra makes it easy to onboard new computational scientists, helping them hit the ground running on their research projects.”
Brendan Reardon, Computational Scientist
Brendan Reardon and colleagues in the Van Allen Lab developed the Molecular Oncology Almanac (MOAlmanac), a clinical interpretation algorithm paired with a novel underlying knowledge base to enable integrative interpretation of genomic and transcriptional cancer data for point-of-care treatment decision-making and translational hypothesis generation. The manuscript describes the MOAlmanac method and shows how, when applied to a prospective precision oncology trial cohort, this method nominated a median of two therapies per patient and identified therapeutic strategies administered in 46% of patient profiles.
The Van Allen Lab team operates a website that enables researchers to browse the MOAlmanac knowledge base, as well as a portal built on top of Terra that provides a simplified interface for running the MOAlmanac analysis workflow.
The Van Allen Lab’s mission is to drive precision cancer medicine through clinical computational oncology. This research group emphasizes translational science with immediate potential clinical application. Their focus is on computational cancer genomics, the application of massively parallel sequencing to precision cancer medicine, and resistance to cancer therapeutics.
Example data for one participant including variant call statistics and annotations illustrating the method requirements
Reproducible workflow for running the MOAlmanac clinical interpretation algorithm, as presented in the manuscript
This public workspace includes the MOAlmanac workflow preconfigured to run on the example data with the analysis parameters used in the study, so anyone can clone the workspace and reproduce the analysis on their own data.
Resources for accessing and analysing controlled-access TCGA datasets
Resources for accessing and analysing controlled-access TARGET datasets
Explore tumor single cell RNA-seq data to visualize evidence for copy number variations