%0 Journal Article
%T Accucopy: accurate and fast inference of allele-specific copy number alterations from low-coverage low-purity tumor sequencing data.
%A Fan X
%A Luo G
%A Huang YS
%J BMC Bioinformatics
%V 22
%N 1
%D Jan 2021 15
%M 33451280
%F 3.307
%R 10.1186/s12859-020-03924-5
%X <B>BACKGROUND: </B>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.<BR><B>RESULTS: </B>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/ .<BR><B>CONCLUSIONS: </B>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.