The genomes of tumors acquire somatic mutations that may provide insights into their mechanisms of action and potential cancer treatments. A key challenge is identifying biologically important sequence variation in these genomes.
Custom Ranked Analysis of VAriants
Toolkit with modular architecture and
a wide variety of tools developed both by the CRAVAT team and the broader variant analysis community
tool and services for high-throughput scoring
and annotation of cancer mutations.
Machine learning method to identify drivers and to distinguish tumor suppressor
genes and oncogenes, based on large cohort studies of primary human cancers.
CHASM. A machine learning method that predicts missense mutations likely to drive tumor growth and progression. Read a JHU magazine article about CHASM.
MOCA. A model-free approach to find patterns of coordinated alterations in cancer genomics data sets. See this TCGA research highlight.
Leukemia 2011; Nature 2011; Cancer Research 2011; Science 2011; Cancer Biology Therapy 2010; Cancer Research 2009