scholarly journals Copy-number-aware differential analysis of quantitative DNA sequencing data

2012 ◽  
Vol 22 (12) ◽  
pp. 2489-2496 ◽  
Author(s):  
M. D. Robinson ◽  
D. Strbenac ◽  
C. Stirzaker ◽  
A. L. Statham ◽  
J. Song ◽  
...  
Keyword(s):  
Dna Sequencing ◽  
Copy Number ◽  
Differential Analysis ◽  
Sequencing Data

scholarly journals Assessing the performance of methods for copy number aberration detection from single-cell DNA sequencing data

2020 ◽  
Vol 16 (7) ◽  
pp. e1008012 ◽  
Author(s):  
Xian F. Mallory ◽  
Mohammadamin Edrisi ◽  
Nicholas Navin ◽  
Luay Nakhleh
Keyword(s):  
Dna Sequencing ◽  
Single Cell ◽  
Copy Number ◽  
Copy Number Aberration ◽  
Sequencing Data ◽  
Aberration Detection

scholarly journals Single-cell copy number calling and event history reconstruction

2020 ◽  
Author(s):  
Jack Kuipers ◽  
Mustafa Anıl Tuncel ◽  
Pedro Ferreira ◽  
Katharina Jahn ◽  
Niko Beerenwinkel
Keyword(s):  
Dna Sequencing ◽  
Single Cell ◽  
Copy Number ◽  
Driving Forces ◽  
Simulated Data ◽  
Read Depth ◽  
Cancer Diagnostics ◽  
Whole Genome ◽  
Copy Number Alterations ◽  
Sequencing Data

Copy number alterations are driving forces of tumour development and the emergence of intra-tumour heterogeneity. A comprehensive picture of these genomic aberrations is therefore essential for the development of personalised and precise cancer diagnostics and therapies. Single-cell sequencing offers the highest resolution for copy number profiling down to the level of individual cells. Recent high-throughput protocols allow for the processing of hundreds of cells through shallow whole-genome DNA sequencing. The resulting low read-depth data poses substantial statistical and computational challenges to the identification of copy number alterations. We developed SCICoNE, a statistical model and MCMC algorithm tailored to single-cell copy number profiling from shallow whole-genome DNA sequencing data. SCICoNE reconstructs the history of copy number events in the tumour and uses these evolutionary relationships to identify the copy number profiles of the individual cells. We show the accuracy of this approach in evaluations on simulated data and demonstrate its practicability in applications to a xenograft breast cancer sample.

scholarly journals CoDEX2: full-spectrum copy number variation detection by high-throughput DNA sequencing

2017 ◽  
Author(s):  
Yuchao Jiang ◽  
Rujin Wang ◽  
Eugene Urrutia ◽  
Ioannis N. Anastopoulos ◽  
Katherine L. Nathanson ◽  
...  
Keyword(s):  
Dna Sequencing ◽  
High Throughput ◽  
Copy Number ◽  
Copy Number Variations ◽  
Negative Control ◽  
Sequencing Data ◽  
Full Spectrum ◽  
Number Variation ◽  
High Throughput Dna Sequencing ◽  
Low Sensitivity

AbstractHigh-throughput DNA sequencing enables detection of copy number variations (CNVs) on the genome-wide scale with finer resolution compared to array-based methods, but suffers from biases and artifacts that lead to false discoveries and low sensitivity. We describe CODEX2, a statistical framework for full-spectrum CNV profiling that is sensitive for variants with both common and rare population frequencies and that is applicable to study designs with and without negative control samples. We demonstrate and evaluate CODEX2 on whole-exome and targeted sequencing data, where biases are the most prominent. CODEX2 outperforms existing methods and, in particular, significantly improves sensitivity for common CNVs.

scholarly journals Methods for Copy Number Aberration Detection from Single-cell DNA Sequencing Data

2019 ◽  
Author(s):  
Xian Fan ◽  
Mohammadamin Edrisi ◽  
Nicholas Navin ◽  
Luay Nakhleh
Keyword(s):  
Dna Sequencing ◽  
Single Cell ◽  
Copy Number ◽  
Sequencing Data ◽  
Computationally Efficient ◽  
New Methods ◽  
Sequencing Technologies ◽  
Copy Numbers ◽  
Aberration Detection ◽  
Evolutionary Trajectories

AbstractSingle-cell DNA sequencing technologies are enabling the study of mutations and their evolutionary trajectories in cancer. Somatic copy number aberrations (CNAs) have been implicated in the development and progression of various types of cancer. A wide array of methods for CNA detection has been either developed specifically for or adapted to single-cell DNA sequencing data. Understanding the strengths and limitations that are unique to each of these methods is very important for obtaining accurate copy number profiles from single-cell DNA sequencing data. Here we review the major steps that are followed by these methods when analyzing such data, and then review the strengths and limitations of the methods individually. In terms of segmenting the genome into regions of different copy numbers, we categorize the methods into three groups, select a representative method from each group that has been commonly used in this context, and benchmark them on simulated as well as real datasets. While single-cell DNA sequencing is very promising for elucidating and understanding CNAs, even the best existing method does not exceed 80% accuracy. New methods that significantly improve upon the accuracy of these three methods are needed. Furthermore, with the large datasets being generated, the methods must be computationally efficient.

scholarly journals Remarkably stable copy-number profiles in osteosarcoma revealed using single-cell DNA sequencing

2021 ◽  
Author(s):  
Sanjana Rajan ◽  
Simone Zaccaria ◽  
Matthew V. Cannon ◽  
Maren Cam ◽  
Amy C. Gross ◽  
...  
Keyword(s):  
Dna Sequencing ◽  
Single Cell ◽  
Genomic Instability ◽  
Copy Number ◽  
Fitness Landscape ◽  
Clonal Evolution ◽  
Response To Therapy ◽  
Tumor Evolution ◽  
Sequencing Data ◽  
Recurrent Mutations

AbstractOsteosarcoma is an aggressive malignancy characterized by high genomic complexity. Identification of few recurrent mutations in protein coding genes suggests that somatic copy-number aberrations (SCNAs) are the genetic drivers of disease. Models around genomic instability conflict-it is unclear if osteosarcomas result from pervasive ongoing clonal evolution with continuous optimization of the fitness landscape or an early catastrophic event followed by stable maintenance of an abnormal genome. We address this question by investigating SCNAs in 12,019 tumor cells obtained from expanded patient tissues using single-cell DNA sequencing, in ways that were previously impossible with bulk sequencing. Using the CHISEL algorithm, we inferred allele- and haplotype-specific SCNAs from whole-genome single-cell DNA sequencing data. Surprisingly, we found that, despite extensive genomic aberrations, cells within each tumor exhibit remarkably homogeneous SCNA profiles with little sub-clonal diversification. Longitudinal analysis between two pairs of patient samples obtained at distant time points (early detection, relapse) demonstrated remarkable conservation of SCNA profiles over tumor evolution. Phylogenetic analysis suggests that the bulk of SCNAs was acquired early in the oncogenic process, with few new events arising in response to therapy or during adaptation to growth in distant tissues. These data suggest that early catastrophic events, rather than sustained genomic instability, drive formation of these extensively aberrant genomes. Overall, we demonstrate the power of combining single-cell DNA sequencing with an allele- and haplotype-specific SCNA inference algorithm to resolve longstanding questions regarding genetics of tumor initiation and progression, questioning the underlying assumptions of genomic instability inferred from bulk tumor data.

scholarly journals The Tangent copy-number inference pipeline for cancer genome analyses

2019 ◽  
Author(s):  
Barbara Tabak ◽  
Gordon Saksena ◽  
Coyin Oh ◽  
Galen F. Gao ◽  
Barbara Hill Meyers ◽  
...  
Keyword(s):  
Dna Sequencing ◽  
Copy Number ◽  
Signal To Noise Ratio ◽  
Snp Array ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
Array Data ◽  
Link Type ◽  
Genome Analyses

AbstractMotivationSomatic copy-number alterations (SCNAs) play an important role in cancer development. Systematic noise in sequencing and array data present a significant challenge to the inference of SCNAs for cancer genome analyses. As part of The Cancer Genome Atlas (TCGA), the Broad Institute Genome Characterization Center developed the Tangent copy-number inference pipeline to generate copy-number profiles using single-nucleotide polymorphism (SNP) array and whole-exome sequencing (WES) data from over 10,000 pairs of tumors and matched normal samples. Here, we describe the Tangent pipeline, which begins with DNA sequencing data in the form of .bam files or raw SNP array probe-level intensity data, and ends with segmented copy-number calls to facilitate the identification of novel genes potentially targeted by SCNAs. We also describe a modification of Tangent, Pseudo-Tangent, which enables denoising through comparisons between tumor profiles when few normal samples are available.ResultsTangent Normalization offers substantial signal-to-noise ratio (SNR) improvements compared to conventional normalization methods in both SNP array and WES analyses. The improvement in SNRs is achieved primarily through noise reduction with minimal effect on signal. Pseudo-Tangent also reduces noise when few normal samples are available. Tangent and Pseudo-Tangent are broadly applicable and enable more accurate inference of SCNAs from DNA sequencing and array data.Availability and ImplementationTangent is available at https://github.com/coyin/tangent and as a Docker image (https://hub.docker.com/r/coyin/tangent). Tangent is also the normalization method for the Copy Number pipeline in Genome Analysis Toolkit 4 (GATK4)[email protected], [email protected], [email protected]

scholarly journals gcn.MOPS: Accelerating cn.MOPS with GPU

10.29007/hb5r ◽  
2019 ◽  
Author(s):  
Mohammad Alkhamis ◽  
Amirali Baniasadi
Keyword(s):  
Dna Sequencing ◽  
Performance Improvement ◽  
Copy Number ◽  
R Package ◽  
Copy Number Variations ◽  
Alternative Mechanism ◽  
Memory Usage ◽  
Sequencing Data ◽  
Next Generation Dna Sequencing ◽  
Speedup Factor

cn.MOPS is a frequently cited model-based algorithm used to quantitatively detect copy-number variations in next-generation, DNA-sequencing data. Previous work has implemented the algorithm as an R package and has achieved considerable yet limited performance improvement by employing multi-CPU parallelism (maximum achievable speedup was experimentally determined to be 9.24). In this paper, we propose an alternative mechanism of process acceleration. Using one CPU core and a GPU device in the proposed solution, gcn.MOPS, we achieve a speedup factor of 159 and reduce memory usage by more than half compared to cn.MOPS running on one CPU core.

scholarly journals Accurate quantification of copy-number aberrations and whole-genome duplications in multi-sample tumor sequencing data

2018 ◽  
Author(s):  
Simone Zaccaria ◽  
Benjamin J. Raphael
Keyword(s):  
Dna Sequencing ◽  
Primary Tumor ◽  
Copy Number ◽  
Tumor Evolution ◽  
Sequencing Data ◽  
Multiple Tumor ◽  
Whole Genome Duplications ◽  
Genome Duplications ◽  
Allele Specific ◽  
Accurate Quantification

Copy-number aberrations (CNAs) and whole-genome duplications (WGDs) are frequent somatic mutations in cancer. Accurate quantification of these mutations from DNA sequencing of bulk tumor samples is complicated by varying tumor purity, admixture of multiple tumor clones with distinct mutations, and high aneuploidy. Standard methods for CNA inference analyze tumor samples individually, but recently DNA sequencing of multiple samples from a cancer patient - e.g. from multiple regions of a primary tumor, matched primary/metastases, or multiple time points - has become common. We introduce a new algorithm, Holistic Allele-specific Tumor Copy-number Heterogeneity (HATCHet), that infers allele and clone-specific CNAs and WGDs jointly across multiple tumor samples from the same patient, and that leverages the relationships between clones in these samples. HATCHet provides a fresh perspective on CNA inference and includes several algorithmic innovations that overcome the limitations of existing methods, resulting in a more robust approach even for single-sample analysis. We also develop MASCoTE (Multiple Allele-specific Simulation of Copy-number Tumor Evolution), a framework for generating realistic simulated multi-sample DNA sequencing data with appropriate corrections for the differences in genome lengths between the normal and tumor clone(s) present in mixed samples. HATCHet outperforms current state-of-the-art methods on 256 simulated tumor samples from 64 patients, half with WGD. HATCHet's analysis of 49 primary tumor and metastasis samples from 10 prostate cancer patients reveals subclonal CNAs in only 29 of these samples, compared to the published reports of extensive subclonal CNAs in all samples. HATCHet's inferred CNAs are also more consistent with the reports of polyclonal origin and limited heterogeneity of metastasis in a subset of patients. HATCHet's analysis of 35 primary tumor and metastasis samples from 4 pancreas cancer patients reveals subclonal CNAs in 20 samples, WGDs in 3 patients, and tumor subclones that are shared across primary and metastases samples from the same patient - none of which were described in published analysis of this data. HATCHet substantially improves the analysis of CNAs and WGDs, leading to more reliable studies of tumor evolution in primary tumors and metastases.

scholarly journals Bamgineer: Introduction of simulated allele-specific copy number variants into exome and targeted sequence data sets

2017 ◽  
Author(s):  
Soroush Samadian ◽  
Jeff P. Bruce ◽  
Trevor J. Pugh
Keyword(s):  
Dna Sequencing ◽  
Copy Number ◽  
Sequence Data ◽  
Copy Number Variants ◽  
Original Data ◽  
Sequencing Data ◽  
Data Types ◽  
Insert Size ◽  
Allele Specific ◽  
Proof Of Principle

AbstractSomatic copy number variations (CNVs) play a crucial role in development of many human cancers. The broad availability of next-generation sequencing data has enabled the development of algorithms to computationally infer CNV profiles from a variety of data types including exome and targeted sequence data; currently the most prevalent types of cancer genomics data. However, systemic evaluation and comparison of these tools remains challenging due to a lack of ground truth reference sets. To address this need, we have developed Bamgineer, a tool written in Python to introduce user-defined haplotype-phased allele-specific copy number events into an existing Binary Alignment Mapping (BAM) file, with a focus on targeted and exome sequencing experiments. As input, this tool requires a read alignment file (BAM format), lists of non-overlapping genome coordinates for introduction of gains and losses (bed file), and an optional file defining known haplotypes (vcf format). To improve runtime performance, Bamgineer introduces the desired CNVs in parallel using queuing and parallel processing on a local machine or on a high-performance computing cluster. As proof-of-principle, we applied Bamgineer to a single high-coverage (mean: 220X) exome sequence file from a blood sample to simulate copy number profiles of 3 exemplar tumors from each of 10 tumor types at 5 tumor cellularity levels (20-100%, 150 BAM files in total). To demonstrate feasibility beyond exome data, we introduced read alignments to a targeted 5-gene cell-free DNA sequencing library to simulate EGFR amplifications at frequencies consistent with circulating tumor DNA (10, 1, 0.1 and 0.01%) while retaining the multimodal insert size distribution of the original data. We expect Bamgineer to be of use for development and systematic benchmarking of CNV calling algorithms by users using locally-generated data for a variety of applications. The source code is freely available at http://github.com/pughlab/bamgineer.Author summaryWe present Bamgineer, a software program to introduce user-defined, haplotype-specific copy number variants (CNVs) at any frequency into standard Binary Alignment Mapping (BAM) files. Copy number gains are simulated by introducing new DNA sequencing read pairs sampled from existing reads and modified to contain SNPs of the haplotype of interest. This approach retains biases of the original data such as local coverage, strand bias, and insert size. Deletions are simulated by removing reads corresponding to one or both haplotypes. In our proof-of-principle study, we simulated copy number profiles from 10 cancer types at varying cellularity levels typically encountered in clinical samples. We also demonstrated introduction of low frequency CNVs into cell-free DNA sequencing data that retained the bimodal fragment size distribution characteristic of these data. Bamgineer is flexible and enables users to simulate CNVs that reflect characteristics of locally-generated sequence files and can be used for many applications including development and benchmarking of CNV inference tools for a variety of data types.

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