Accucopy: accurate and fast inference of allele-specific copy number alterations from low-coverage low-purity tumor sequencing data.

Abstract:

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.

journal_name

BMC Bioinformatics

journal_title

BMC bioinformatics

authors

Fan X,Luo G,Huang YS

doi

10.1186/s12859-020-03924-5

subject

Has Abstract

pub_date

2021-01-15 00:00:00

pages

23

issue

1

issn

1471-2105

pii

10.1186/s12859-020-03924-5

journal_volume

22

pub_type

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