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.

Establishment, characterization, and genetic profiling of patient-derived osteosarcoma cells from a patient with retinoblastoma

“…Manta software 24 was applied to predict somatic SVs, and, again, their functional consequence was predicted by the VEP tool. Copy-number alterations and tumor clonality were observed by Accucopy 25 and HATCHet 26 algorithms. The similarity between pairs of the samples in term of small variants were calculated by the fraction of shared somatic variants overall identified somatic variants.…”

eGADA: enhanced Genomic Alteration Detection Algorithm, a fast genomic segmentation algorithm based on Sparse Bayesian Learning

“…We also compared eGADA with BIC-seq2 6 , in segmenting the genomic data (each input data point is normalized coverage of a 500bp bin) of simulated and TCGA samples, and found eGADA can produce similar segmentation results (data not shown) while being much faster. We included it as part of Accucopy, a tumor-purity and CNA inference software 5 .…”