Nano-GLADIATOR: real-time detection of copy number alterations from nanopore sequencing data

Abstract Motivation The past few years have seen the emergence of nanopore-based sequencing technologies which interrogate single molecule of DNA and generate reads sequentially. Results In this paper, we demonstrate that, thanks to the sequentiality of the nanopore process, the data generated in the first tens of minutes of a typical MinION/GridION run can be exploited to resolve the alterations of a human genome at a karyotype level with a resolution in the order of tens of Mb, while the data produced in the first 6–12 h allow to obtain a resolution comparable to currently available array-based technologies, and thanks to a novel probabilistic approach are capable to predict the allelic fraction of genomic alteration with high accuracy. To exploit the unique characteristics of nanopore sequencing data we developed a novel software tool, Nano-GLADIATOR, that is capable to perform copy number variants/alterations detection and allelic fraction prediction during the sequencing run (‘On-line’ mode) and after experiment completion (‘Off-line’ mode). We tested Nano-GLADIATOR on publicly available (‘Off-line’ mode) and on novel whole genome sequencing dataset generated with MinION device (‘On-line’ mode) showing that our tool is capable to perform real-time copy number alterations detection obtaining good results with respect to other state-of-the-art tools. Availability and implementation Nano-GLADIATOR is freely available at https://sourceforge.net/projects/nanogladiator/. Supplementary information Supplementary data are available at Bioinformatics online.

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DEFOR: depth- and frequency-based somatic copy number alteration detector

Bioinformatics ◽

10.1093/bioinformatics/btz170 ◽

2019 ◽

Vol 35 (19) ◽

pp. 3824-3825 ◽

Cited By ~ 1

Author(s):

He Zhang ◽

Xiaowei Zhan ◽

James Brugarolas ◽

Yang Xie

Keyword(s):

Exome Sequencing ◽

Copy Number ◽

Copy Number Alteration ◽

High Throughput Sequencing ◽

Supplementary Information ◽

Copy Number Alterations ◽

Sequencing Data ◽

Sequencing Technology ◽

Somatic Copy Number Alterations ◽

Somatic Copy Number Alteration

Abstract Motivation Detection of somatic copy number alterations (SCNAs) using high-throughput sequencing has become popular because of rapid developments in sequencing technology. Existing methods do not perform well in calling SCNAs for the unstable tumor genomes. Results We developed a new method, DEFOR, to detect SCNAs in tumor samples from exome-sequencing data. The evaluation showed that DEFOR has a higher accuracy for SCNA detection from exome sequencing compared with the five existing tools. This advantage is especially apparent in unstable tumor genomes with a large proportion of SCNAs. Availability and implementation DEFOR is available at https://github.com/drzh/defor. Supplementary information Supplementary data are available at Bioinformatics online.

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Multiresolution correction of GC bias and application to identification of copy number alterations

Bioinformatics ◽

10.1093/bioinformatics/btz174 ◽

2019 ◽

Vol 35 (20) ◽

pp. 3890-3897 ◽

Cited By ~ 2

Author(s):

Ho Jang ◽

Hyunju Lee

Keyword(s):

Copy Number ◽

Regression Models ◽

Low Frequency ◽

Supplementary Information ◽

Copy Number Alterations ◽

Sequencing Data ◽

Translation Invariant ◽

Cancer Genomes ◽

Gc Bias ◽

Genomic Regions

Abstract Motivation Whole-genome sequencing (WGS) data are affected by various sequencing biases such as GC bias and mappability bias. These biases degrade performance on detection of genetic variations such as copy number alterations. The existing methods use a relation between the GC proportion and depth of coverage (DOC) of markers by means of regression models. Nonetheless, severity of the GC bias varies from sample to sample. We developed a new method for correction of GC bias on the basis of multiresolution analysis. We used a translation-invariant wavelet transform to decompose biased raw signals into high- and low-frequency coefficients. Then, we modeled the relation between GC proportion and DOC of the genomic regions and constructed new control DOC signals that reflect the GC bias. The control DOC signals are used for normalizing genomic sequences by correcting the GC bias. Results When we applied our method to simulated sequencing data with various degrees of GC bias, our method showed more robust performance on correcting the GC bias than the other methods did. We also applied our method to real-world cancer sequencing datasets and successfully identified cancer-related focal alterations even when cancer genomes were not normalized to normal control samples. In conclusion, our method can be employed for WGS data with different degrees of GC bias. Availability and implementation The code is available at http://gcancer.org/wabico. Supplementary information Supplementary data are available at Bioinformatics online.

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

BMC Bioinformatics ◽

10.1186/s12859-020-03924-5 ◽

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.

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High resolution copy number inference in cancer using short-molecule nanopore sequencing

10.1101/2020.12.28.424602 ◽

2020 ◽

Author(s):

Timour Baslan ◽

Sam Kovaka ◽

Fritz J. Sedlazeck ◽

Yanming Zhang ◽

Robert Wappel ◽

...

Keyword(s):

Copy Number ◽

Cost Effective ◽

Chromosome Analysis ◽

Ease Of Use ◽

Precision Oncology ◽

Nanopore Sequencing ◽

Dna Molecules ◽

Sequencing Data ◽

Short Read ◽

Short Read Sequencing

ABSTRACTGenome copy number is an important source of genetic variation in health and disease. In cancer, clinically actionable Copy Number Alterations (CNAs) can be inferred from short-read sequencing data, enabling genomics-based precision oncology. Emerging Nanopore sequencing technologies offer the potential for broader clinical utility, for example in smaller hospitals, due to lower instrument cost, higher portability, and ease of use. Nonetheless, Nanopore sequencing devices are limited in terms of the number of retrievable sequencing reads/molecules compared to short-read sequencing platforms. This represents a challenge for applications that require high read counts such as CNA inference. To address this limitation, we targeted the sequencing of short-length DNA molecules loaded at optimized concentration in an effort to increase sequence read/molecule yield from a single nanopore run. We show that sequencing short DNA molecules reproducibly returns high read counts and allows high quality CNA inference. We demonstrate the clinical relevance of this approach by accurately inferring CNAs in acute myeloid leukemia samples. The data shows that, compared to traditional approaches such as chromosome analysis/cytogenetics, short molecule nanopore sequencing returns more sensitive, accurate copy number information in a cost effective and expeditious manner, including for multiplex samples. Our results provide a framework for the sequencing of relatively short DNA molecules on nanopore devices with applications in research and medicine, that include but are not limited to, CNAs.

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Integrative DNA copy number detection and genotyping from sequencing and array-based platforms

10.1101/172700 ◽

2017 ◽

Cited By ~ 2

Author(s):

Zilu Zhou ◽

Weixin Wang ◽

Li-San Wang ◽

Nancy Ruonan Zhang

Keyword(s):

Copy Number ◽

Association Studies ◽

Snp Array ◽

Supplementary Information ◽

Detection Accuracy ◽

Sequencing Data ◽

Array Data ◽

Combining Data ◽

Allele Specific ◽

Cnv Detection

AbstractMotivationCopy number variations (CNVs) are gains and losses of DNA segments and have been associated with disease. Many large-scale genetic association studies are performing CNV analysis using whole exome sequencing (WES) and whole genome sequencing (WGS). In many of these studies, previous SNP-array data are available. An integrated cross-platform analysis is expected to improve resolution and accuracy, yet there is no tool for effectively combining data from sequencing and array platforms. The detection of CNVs using sequencing data alone can also be further improved by the utilization of allele-specific reads.ResultsWe propose a statistical framework, integrated Copy Number Variation detection algorithm (iCNV), which can be applied to multiple study designs: WES only, WGS only, SNP array only, or any combination of SNP and sequencing data. iCNV applies platform specific normalization, utilizes allele specific reads from sequencing and integrates matched NGS and SNP-array data by a Hidden Markov Model (HMM). We compare integrated two-platform CNV detection using iCNV to naive intersection or union of platforms and show that iCNV increases sensitivity and robustness. We also assess the accuracy of iCNV on WGS data only, and show that the utilization of allele-specific reads improve CNV detection accuracy compared to existing methods.Availabilityhttps://github.com/zhouzilu/[email protected], [email protected] informationSupplementary data are available at Bioinformatics online.

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SEG - A Software Program for Finding Somatic Copy Number Alterations in Whole Genome Sequencing Data of Cancer

Computational and Structural Biotechnology Journal ◽

10.1016/j.csbj.2018.09.001 ◽

2018 ◽

Vol 16 ◽

pp. 335-341 ◽

Cited By ~ 2

Author(s):

Mucheng Zhang ◽

Deli Liu ◽

Jie Tang ◽

Yuan Feng ◽

Tianfang Wang ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Copy Number ◽

Whole Genome Sequencing Data ◽

Whole Genome ◽

Copy Number Alterations ◽

Sequencing Data ◽

Software Program ◽

Somatic Copy Number Alterations

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Quantification of aneuploidy in targeted sequencing data using ASCETS

Bioinformatics ◽

10.1093/bioinformatics/btaa980 ◽

2020 ◽

Author(s):

Liam F Spurr ◽

Mehdi Touat ◽

Alison M Taylor ◽

Adrian M Dubuc ◽

Juliann Shih ◽

...

Keyword(s):

Copy Number ◽

Large Scale ◽

Genomic Analysis ◽

Targeted Sequencing ◽

Supplementary Information ◽

Supplementary Data ◽

Sequencing Data ◽

Copy Number Changes ◽

Panel Sequencing ◽

Chromosome Level

Abstract Summary The expansion of targeted panel sequencing efforts has created opportunities for large-scale genomic analysis, but tools for copy-number quantification on panel data are lacking. We introduce ASCETS, a method for the efficient quantitation of arm and chromosome-level copy-number changes from targeted sequencing data. Availability and implementation ASCETS is implemented in R and is freely available to non-commercial users on GitHub: https://github.com/beroukhim-lab/ascets, along with detailed documentation. Supplementary information Supplementary data are available at Bioinformatics online.

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