Accucopy: accurate and fast inference of allele-specific copy number alterations for heterogeneous tumor samples from high-throughput sequencing data

Fan, X.; Chen, L.; Liu, F.; Huang, Y.

doi:10.1101/2020.01.02.892364

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bioRxiv · 10.1101/2020.01.02.892364

Accucopy: accurate and fast inference of allele-specific copy number alterations for heterogeneous tumor samples from high-throughput sequencing data

Fan, X.·Chen, L.·Liu, F.·Huang, Y.

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Abstract

BackgroundCopy 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. ResultsWe 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 (EM) algorithm, and Sparse Bayesian Learning (SBL) 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/. ConclusionsWe 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.

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Fan, X., Chen, L., Liu, F., Huang, Y.. 2020-01-03. Accucopy: accurate and fast inference of allele-specific copy number alterations for heterogeneous tumor samples from high-throughput sequencing data. https://doi.org/10.1101/2020.01.02.892364

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