A computational method that infers Allele-Specific Copy Number alterations from low-coverage low-purity tumor sequencing data.
Introduction
Accucopy(Fan et al., 2021) is a CNA-calling method that extends our previous Accurity model to predict both total (TCN) and allele-specific copy numbers (ASCN) for the tumor genome. Accucopy adopts a tiered Gaussian mixture model coupled with an innovative autocorrelation-guided EM algorithm to find the optimal solution quickly. The Accucopy model utilizes information from both total sequencing coverage and allelic sequencing coverage. Through benchmark in both simulated and real-sequencing samples, we demonstrate that Accucopy is more accurate than existing methods
Accucopy’s main strength is in handling low coverage and/or low tumor-purity samples. Its docker images (Accucopy & Accurity) have been downloaded more than 1000 times from over 10 countries.
BACKGROUND:
Copy number alterations (CNAs), due to their large impact on the genome, have been an important contributing factor to oncogenesis and metastasis. Detecting genomic alterations from the shallow-sequencing data of a low-purity tumor sample remains a challenging task.
RESULTS:
We introduce Accucopy, a method to infer total copy numbers (TCNs) and allele-specific copy numbers (ASCNs) from challenging low-purity and low-coverage tumor samples. Accucopy adopts many robust statistical techniques such as kernel smoothing of coverage differentiation information to discern signals from noise and combines ideas from time-series analysis and the signal-processing field to derive a range of estimates for the period in a histogram of coverage differentiation information. Statistical learning models such as the tiered Gaussian mixture model, the expectation–maximization algorithm, and sparse Bayesian learning were customized and built into the model. Accucopy is implemented in C++ /Rust, packaged in a docker image, and supports non-human samples, more at http://www.yfish.org/software/.
CONCLUSIONS:
We describe Accucopy, a method that can predict both TCNs and ASCNs from low-coverage low-purity tumor sequencing data. Through comparative analyses in both simulated and real-sequencing samples, we demonstrate that Accucopy is more accurate than Sclust, ABSOLUTE, and Sequenza.
@article{Fan2021Accucopy,title={Accucopy: Accurate and Fast Inference of Allele-specific Copy Number Alterations from Low-coverage Low-purity Tumor Sequencing Data},author={Fan, X and Luo, G and Huang, YS},journal={BMC Bioinformatics},doi={10.1186/s12859-020-03924-5},url={https://doi.org/10.1186/s12859-020-03924-5},year={2021},publisher={BMC},dimensions={true},}
