scholarly journals Allele-specific multi-sample copy number segmentation

2017 ◽  
Author(s):  
Edith M. Ross ◽  
Kerstin Haase ◽  
Peter Van Loo ◽  
Florian Markowetz
Keyword(s):  
Copy Number ◽  
Genomic Data ◽  
Phylogenetic Inference ◽  
R Package ◽  
Copy Number Alterations ◽  
Allele Specific ◽  
Multiple Samples

AbstractMotivationAllele-specific copy number alterations are commonly used to trace the evolution of tumours. A key step of the analysis is to segment genomic data into regions of constant copy number. For precise phylogenetic inference, breakpoints shared between samples need to be aligned to each other.ResultsHere we present asmultipcf, an algorithm for allele-specific segmentation of multiple samples that infers private and shared segment boundaries of phylogenetically related samples. The output of this algorithm can directly be used for allele-specific copy number calling using ASCAT.Availabilityasmultipcf is available as part of the ASCAT R package (version 2.5) from github.com/Crick-CancerGenomics/ascat

scholarly journals Allele-specific multi-sample copy number segmentation in ASCAT

2020 ◽  
Author(s):  
Edith M Ross ◽  
Kerstin Haase ◽  
Peter Van Loo ◽  
Florian Markowetz
Keyword(s):  
Copy Number ◽  
Genomic Data ◽  
Phylogenetic Inference ◽  
R Package ◽  
Copy Number Alterations ◽  
Allele Specific ◽  
Multiple Samples

Abstract Motivation Allele-specific copy number alterations are commonly used to trace the evolution of tumours. A key step of the analysis is to segment genomic data into regions of constant copy number. For precise phylogenetic inference, breakpoints shared between samples need to be aligned to each other. Results Here we present asmultipcf, an algorithm for allele-specific segmentation of multiple samples that infers private and shared segment boundaries of phylogenetically related samples. The output of this algorithm can directly be used for allele-specific copy number calling using ASCAT. Availability asmultipcf is available as part of the ASCAT R package (version ≥ 2.5) from github.com/Crick-CancerGenomics/ascat/

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

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Xinping Fan ◽  
Guanghao Luo ◽  
Yu S. Huang
Keyword(s):  
Copy Number ◽  
Bayesian Learning ◽  
Kernel Smoothing ◽  
Gaussian Mixture ◽  
Copy Number Alterations ◽  
Sequencing Data ◽  
Copy Numbers ◽  
Allele Specific ◽  
Tumor Sequencing ◽  
Low Coverage

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.

822 Allele-specific aberrations and two dimensional disparity of copy number alterations in breast cancer

2010 ◽  
Vol 8 (5) ◽  
pp. 207
Author(s):  
F. Kaveh ◽  
H. Edvardsen ◽  
A.L. Børresen-Dale ◽  
V.N. Kristensen ◽  
H.K. Solvang
Keyword(s):  
Breast Cancer ◽  
Copy Number ◽  
Two Dimensional ◽  
Copy Number Alterations ◽  
Allele Specific

Abstract 1650: Deconvolution of copy number alterations combining bulk and single-cell genomic data

2019 ◽  
Author(s):  
Haoyun Lei ◽  
Bochuan Lyu ◽  
E. Michael Gertz ◽  
Alejandro A. Schaeffer ◽  
Xulian Shi ◽  
...  
Keyword(s):  
Single Cell ◽  
Copy Number ◽  
Genomic Data ◽  
Copy Number Alterations

scholarly journals ReadDepth: A Parallel R Package for Detecting Copy Number Alterations from Short Sequencing Reads

PLoS ONE ◽  
2011 ◽  
Vol 6 (1) ◽  
pp. e16327 ◽  
Author(s):  
Christopher A. Miller ◽  
Oliver Hampton ◽  
Cristian Coarfa ◽  
Aleksandar Milosavljevic
Keyword(s):  
Copy Number ◽  
R Package ◽  
Copy Number Alterations

scholarly journals cognac: rapid generation of concatenated gene alignments for phylogenetic inference from large whole genome sequencing datasets

2020 ◽  
Author(s):  
Ryan D. Crawford ◽  
Evan S. Snitkin
Keyword(s):  
Phylogenetic Analysis ◽  
Genome Sequencing ◽  
Software Package ◽  
Genomic Data ◽  
Phylogenetic Inference ◽  
R Package ◽  
Core Gene ◽  
Whole Genome ◽  
Rapid Generation ◽  
User Friendly

AbstractThe quantity of genomic data is expanding at an increasing rate. Tools for phylogenetic analysis which scale to the quantity of available data are required. We present cognac, a user-friendly software package to rapidly generate concatenated gene alignments for phylogenetic analysis. We applied this tool to generate core gene alignments for very large genomic datasets, including a dataset of over 11,000 genomes from the genus Escherichia containing 1,353 genes, which was constructed in less than 17 hours. We have released cognac as an R package (https://github.com/rdcrawford/cognac) with customizable parameters for adaptation to diverse applications.

scholarly journals Accucopy: Accurate and Fast Inference of Allele-specific Copy Number Alterations from Low-coverage Low-purity Tumor Sequencing Data

2020 ◽  
Author(s):  
Xinping Fan ◽  
Guanghao Luo ◽  
Yu S. Huang
Keyword(s):  
Copy Number ◽  
Bayesian Learning ◽  
Kernel Smoothing ◽  
Gaussian Mixture ◽  
Copy Number Alterations ◽  
Sequencing Data ◽  
Copy Numbers ◽  
Allele Specific ◽  
Tumor Sequencing ◽  
Low Coverage

AbstractBackgroundCopy 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.

scholarly journals Rawcopy: Improved copy number analysis with Affymetrix arrays

2015 ◽  
Author(s):  
Markus Mayrhofer ◽  
Bjorn Viklund ◽  
Anders Isaksson
Keyword(s):  
Copy Number ◽  
Signal To Noise Ratio ◽  
R Package ◽  
Copy Number Analysis ◽  
Genome Wide ◽  
Microarray Sample ◽  
Allele Specific ◽  
Log Ratio ◽  
Reference Samples ◽  
Total Copy Number

Rawcopy is an R package for processing of Affymetrix CytoScan HD, CytoScan 750k and SNP 6.0 microarray raw intensities (CEL files). It uses data from a large number of reference samples to produce log ratio for total copy number analysis and B-allele frequency for allele-specific copy number and heterozygosity analysis. Rawcopy achieves higher signal-to-noise ratio than commonly used free and proprietary alternatives, leading to improved identification of copy number alterations. In addition, Rawcopy visualises each microarray sample for assessment of technical quality, patient identity and genome-wide absolute copy number states.

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