scholarly journals ACEseq – allele specific copy number estimation from whole genome sequencing

2017 ◽  
Author(s):  
Kortine Kleinheinz ◽  
Isabell Bludau ◽  
Daniel Hübschmann ◽  
Michael Heinold ◽  
Philip Kensche ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Bias Correction ◽  
Copy Number ◽  
Replication Timing ◽  
Whole Genome ◽  
Cell Content ◽  
Copy Number Estimation ◽  
Allele Specific ◽  
Gc Bias

ACEseq is a computational tool for allele-specific copy number estimation in tumor genomes based on whole genome sequencing. In contrast to other tools it features GC-bias correction, unique replication timing-bias correction and integration of structural variant (SV) breakpoints for improved genome segmentation. ACEseq clearly outperforms widely used state-of-the art methods, provides a fully automated estimation of tumor cell content and ploidy, and additionally computes homologous recombination deficiency scores.

scholarly journals Allele-Specific Quantification of Structural Variations in Cancer Genomes

2016 ◽  
Author(s):  
Yang Li ◽  
Shiguo Zhou ◽  
David C. Schwartz ◽  
Jian Ma
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Copy Number ◽  
Graphical Model ◽  
Genome Structure ◽  
Cancer Genome ◽  
Whole Genome ◽  
Structural Variations ◽  
Cancer Genomes ◽  
Allele Specific

AbstractOne of the hallmarks of cancer genome is aneuploidy, resulting in abnormal copy numbers of alleles. Structural variations (SVs) can further modify the aneuploid cancer genomes into a mixture of rearranged genomic segments with extensive range of somatic copy number alterations (CNAs). Indeed, aneuploid cancer genomes have significantly higher rate of CNAs and SVs. However, although methods have been developed to identify SVs and allele-specific copy number of genome (ASCNG) separately, no existing algorithm can simultaneously analyze SVs and ASCNG. Such integrated approach is particularly important to fully understand the complexity of cancer genomes. Here we introduce a new algorithm called Weaver to provide allele-specific quantification of SVs and CNAs in aneuploid cancer genomes. Weaver uses a probabilistic graphical model by utilizing cancer whole genome sequencing data to simultaneously estimate the digital copy number and inter-connectivity of SVs. Our simulation evaluation, comparison with single-molecule Optical Mapping analysis, and real data applications (including MCF-7, HeLa, and TCGA whole genome sequencing samples) demonstrated that Weaver is highly accurate and can greatly refine the analysis of complex cancer genome structure.

scholarly journals Challenges and Approaches to Genotyping Repetitive DNA

2019 ◽  
Vol 10 (1) ◽  
pp. 417-430 ◽  
Author(s):  
Elizabeth A. Morton ◽  
Ashley N. Hall ◽  
Elizabeth Kwan ◽  
Calvin Mok ◽  
Konstantin Queitsch ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Repetitive Dna ◽  
Complex Traits ◽  
Copy Number ◽  
Model Organisms ◽  
Whole Genome ◽  
Copy Number Estimation ◽  
Rdna Copy ◽  
Rdna Copy Number

Individuals within a species can exhibit vast variation in copy number of repetitive DNA elements. This variation may contribute to complex traits such as lifespan and disease, yet it is only infrequently considered in genotype-phenotype associations. Although the possible importance of copy number variation is widely recognized, accurate copy number quantification remains challenging. Here, we assess the technical reproducibility of several major methods for copy number estimation as they apply to the large repetitive ribosomal DNA array (rDNA). rDNA encodes the ribosomal RNAs and exists as a tandem gene array in all eukaryotes. Repeat units of rDNA are kilobases in size, often with several hundred units comprising the array, making rDNA particularly intractable to common quantification techniques. We evaluate pulsed-field gel electrophoresis, droplet digital PCR, and Nextera-based whole genome sequencing as approaches to copy number estimation, comparing techniques across model organisms and spanning wide ranges of copy numbers. Nextera-based whole genome sequencing, though commonly used in recent literature, produced high error. We explore possible causes for this error and provide recommendations for best practices in rDNA copy number estimation. We present a resource of high-confidence rDNA copy number estimates for a set of S. cerevisiae and C. elegans strains for future use. We furthermore explore the possibility for FISH-based copy number estimation, an alternative that could potentially characterize copy number on a cellular level.

ACE: absolute copy number estimation from low-coverage whole-genome sequencing data

2018 ◽  
Vol 35 (16) ◽  
pp. 2847-2849 ◽  
Author(s):  
Jos B Poell ◽  
Matias Mendeville ◽  
Daoud Sie ◽  
Arjen Brink ◽  
Ruud H Brakenhoff ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Copy Number ◽  
Supplementary Information ◽  
Whole Genome ◽  
Copy Number Estimation ◽  
Tumor Purity ◽  
Absolute Copy Number ◽  
Copy Numbers ◽  
Low Coverage

Abstract Summary Chromosomal copy number aberrations can be efficiently detected and quantified using low-coverage whole-genome sequencing, but analysis is hampered by the lack of knowledge on absolute DNA copy numbers and tumor purity. Here, we describe an analytical tool for Absolute Copy number Estimation, ACE, which scales relative copy number signals from chromosomal segments to optimally fit absolute copy numbers, without the need for additional genetic information, such as SNP data. In doing so, ACE derives an estimate of tumor purity as well. ACE facilitates analysis of large numbers of samples, while maintaining the flexibility to customize models and generate output of single samples. Availability and implementation ACE is freely available via www.bioconductor.org and at www.github.com/tgac-vumc/ACE. Supplementary information Supplementary data are available at Bioinformatics online.

0306 Exploring the feasibility of using copy number variants as genetic markers through large-scale whole genome sequencing experiments

2016 ◽  
Vol 94 (suppl_5) ◽  
pp. 146-146
Author(s):  
D. M. Bickhart ◽  
L. Xu ◽  
J. L. Hutchison ◽  
J. B. Cole ◽  
D. J. Null ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genetic Markers ◽  
Genome Sequencing ◽  
Copy Number ◽  
Large Scale ◽  
Copy Number Variants ◽  
Whole Genome

scholarly journals Whole-genome sequencing from the New Zealand Saccharomyces cerevisiae population reveals the genomic impacts of novel microbial range expansion

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Peter Higgins ◽  
Cooper A Grace ◽  
Soon A Lee ◽  
Matthew R Goddard
Keyword(s):  
Saccharomyces Cerevisiae ◽  
New Zealand ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Range Expansion ◽  
Copy Number ◽  
Small Scale ◽  
Whole Genome ◽  
Copy Number Changes ◽  
The Impact

Abstract Saccharomyces cerevisiae is extensively utilized for commercial fermentation, and is also an important biological model; however, its ecology has only recently begun to be understood. Through the use of whole-genome sequencing, the species has been characterized into a number of distinct subpopulations, defined by geographical ranges and industrial uses. Here, the whole-genome sequences of 104 New Zealand (NZ) S. cerevisiae strains, including 52 novel genomes, are analyzed alongside 450 published sequences derived from various global locations. The impact of S. cerevisiae novel range expansion into NZ was investigated and these analyses reveal the positioning of NZ strains as a subgroup to the predominantly European/wine clade. A number of genomic differences with the European group correlate with range expansion into NZ, including 18 highly enriched single-nucleotide polymorphism (SNPs) and novel Ty1/2 insertions. While it is not possible to categorically determine if any genetic differences are due to stochastic process or the operations of natural selection, we suggest that the observation of NZ-specific copy number increases of four sugar transporter genes in the HXT family may reasonably represent an adaptation in the NZ S. cerevisiae subpopulation, and this correlates with the observations of copy number changes during adaptation in small-scale experimental evolution studies.

Rare instances of non-random dropout with the monochrome multiplex qPCR assay for mitochondrial DNA copy number

2021 ◽  
Author(s):  
Stephanie Y Yang ◽  
Charles E Newcomb ◽  
Stephanie L Battle ◽  
Anthony YY Hsieh ◽  
Hailey L Chapman ◽  
...  
Keyword(s):  
Mitochondrial Dna ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Copy Number ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Dna Copy Number ◽  
Loop Primer ◽  
Mitochondrial Dna Copy Number ◽  
D Loop

Mitochondrial DNA copy number (mtDNA-CN) is a proxy for mitochondrial function and has been of increasing interest to the mitochondrial research community. There are several ways to measure mtDNA-CN, ranging from whole genome sequencing to qPCR. A recent article from the Journal of Molecular Diagnostics described a novel method for measuring mtDNA-CN that is both inexpensive and reproducible. However, we show that certain individuals, particularly those with very low qPCR mtDNA measurements, show poor concordance between qPCR and whole genome sequencing measurements. After examining whole genome sequencing data, this seems to be due to polymorphisms within the D-loop primer region. Non-concordant mtDNA-CN was observed in all instances of polymorphisms at certain positions in the D-loop primer regions, however, not all positions are susceptible to this effect. In particular, these polymorphisms appear disproportionately in individuals with the L, T, and U mitochondrial haplogroups, indicating non-random dropout.

scholarly journals Detection and characterization of copy number variants based on whole-genome sequencing by DNBSEQ platforms

2019 ◽  
Author(s):  
Junhua Rao ◽  
Lihua Peng ◽  
Fang Chen ◽  
Hui Jiang ◽  
Chunyu Geng ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Copy Number ◽  
Copy Number Variants ◽  
Copy Number Variant ◽  
Whole Genome ◽  
Genome Wide ◽  
Wide Range ◽  
Distribution Sensitivity ◽  
Cnv Detection

AbstractBackgroundNext-generation sequence (NGS) has rapidly developed in past years which makes whole-genome sequencing (WGS) becoming a more cost- and time-efficient choice in wide range of biological researches. We usually focus on some variant detection via WGS data, such as detection of single nucleotide polymorphism (SNP), insertion and deletion (Indel) and copy number variant (CNV), which playing an important role in many human diseases. However, the feasibility of CNV detection based on WGS by DNBSEQ™ platforms was unclear. We systematically analysed the genome-wide CNV detection power of DNBSEQ™ platforms and Illumina platforms on NA12878 with five commonly used tools, respectively.ResultsDNBSEQ™ platforms showed stable ability to detect slighter more CNVs on genome-wide (average 1.24-fold than Illumina platforms). Then, CNVs based on DNBSEQ™ platforms and Illumina platforms were evaluated with two public benchmarks of NA12878, respectively. DNBSEQ™ and Illumina platforms showed similar sensitivities and precisions on both two benchmarks. Further, the difference between tools for CNV detection was analyzed, and indicated the selection of tool for CNV detection could affected the CNV performance, such as count, distribution, sensitivity and precision.ConclusionThe major contribution of this paper is providing a comprehensive guide for CNV detection based on WGS by DNBSEQ™ platforms for the first time.

scholarly journals MBRS-59. SINGLE-CELL WHOLE-GENOME SEQUENCING DISSECTS INTRA-TUMOURAL GENOMIC HETEROGENEITY AND CLONAL EVOLUTION IN CHILDHOOD MEDULLOBLASTOMA

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii408-iii408
Author(s):  
Marina Danilenko ◽  
Masood Zaka ◽  
Claire Keeling ◽  
Stephen Crosier ◽  
Rafiqul Hussain ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Single Cell ◽  
Genome Sequencing ◽  
Copy Number ◽  
Single Cell Analysis ◽  
Mutational Analysis ◽  
Single Cells ◽  
Clonal Evolution ◽  
Whole Genome ◽  
Clinically Significant

Abstract Medulloblastomas harbor clinically-significant intra-tumoral heterogeneity for key biomarkers (e.g. MYC/MYCN, β-catenin). Recent studies have characterized transcriptional heterogeneity at the single-cell level, however the underlying genomic copy number and mutational architecture remains to be resolved. We therefore sought to establish the intra-tumoural genomic heterogeneity of medulloblastoma at single-cell resolution. Copy number patterns were dissected by whole-genome sequencing in 1024 single cells isolated from multiple distinct tumour regions within 16 snap-frozen medulloblastomas, representing the major molecular subgroups (WNT, SHH, Group3, Group4) and genotypes (i.e. MYC amplification, TP53 mutation). Common copy number driver and subclonal events were identified, providing clear evidence of copy number evolution in medulloblastoma development. Moreover, subclonal whole-arm and focal copy number alterations covering important genomic loci (e.g. on chr10 of SHH patients) were detected in single tumour cells, yet undetectable at the bulk-tumor level. Spatial copy number heterogeneity was also common, with differences between clonal and subclonal events detected in distinct regions of individual tumours. Mutational analysis of the cells allowed dissection of spatial and clonal heterogeneity patterns for key medulloblastoma mutations (e.g. CTNNB1, TP53, SMARCA4, PTCH1) within our cohort. Integrated copy number and mutational analysis is underway to establish their inter-relationships and relative contributions to clonal evolution during tumourigenesis. In summary, single-cell analysis has enabled the resolution of common mutational and copy number drivers, alongside sub-clonal events and distinct patterns of clonal and spatial evolution, in medulloblastoma development. We anticipate these findings will provide a critical foundation for future improved biomarker selection, and the development of targeted therapies.

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