Join GitHub today
GitHub is home to over 28 million developers working together to host and review code, manage projects, and build software together.Sign up
ichorCNA is a tool for estimating the fraction of tumor in cell-free DNA from ultra-low-pass whole genome sequencing (ULP-WGS, 0.1x coverage).
ichorCNA uses a probabilistic model, implemented as a hidden Markov model (HMM), to simultaneously segment the genome, predict large-scale copy number alterations, and estimate the tumor fraction of a ultra-low-pass whole genome sequencing sample (ULP-WGS). ichorCNA is optimized for low coverage (~0.1x) sequencing of samples and has been benchmarked using patient and healthy donor cfDNA samples.
ichorCNA can be used to inform the presence or absence of tumor-derived DNA and to guide the decision to perform whole exome or deeper whole genome sequencing. Furthermore, the quantitative estimate of tumor fraction can we used to calibrate the desired depth of sequencing to reach statistical power for identifying mutations in cell-free DNA. Finally, ichorCNA can be use to detect large-scale copy number alterations from large cohorts by taking advantage of the cost-effective approach of ultra-low-pass sequencing.
The methodology and probabilistic model are described in:
Adalsteinsson, Ha, Freeman, et al. Scalable whole-exome sequencing of cell-free DNA reveals high concordance with metastatic tumors. (2017) Nature Communications Nov 6;8(1):1324. doi: 10.1038/s41467-017-00965-y