Cancer mutation calling paper published

By | February 6, 2020

Cells within a tumor are genetically diverse, and low-frequency mutations can have important implications for understanding treatment resistance and tumor evolution. But detecting such mutations is difficult. We developed BATCAVE as an improved algorithm for detecting mutations within tumors by modeling a key aspect of tumor biology. Namely, each individual tumor has its own profile of mutation types that it tends to generate.

In our paper just published in NAR: Genomics and Bioinformatics, we show that our algorithm improves mutation identification and calibration, in both real and simulated data. Moreover, the algorithm is general and can be added to additional variant callers and extended to incorporate additional genomic context.

This project was entirely pushed forward by former PhD student Brian Mannakee. He developed an interest in cancer bioinformatics, trained himself in the tools, and conceived the algorithm. It was my privilege to help guide him through the project!