Digital PCR (dPCR) and qPCR Mediated Determination of Transgene Copy Number in the Forage Legume White Clover (Trifolium Repens).

Obtaining data on transgene copy number is an integral step in the generation of transgenic plants. Techniques such as Southern blot, segregation analysis, and quantitative PCR (qPCR) have routinely been used for this task, in a range of species. More recently, use of Digital PCR (dPCR) has become prevalent, with a measurement accuracy higher than qPCR reported. Here, the relative merits of qPCR and dPCR for transgene copy number estimation in white clover were investigated. Furthermore, given that single copy reference genes are desirable for estimating gene copy number by relative quantification, and that no single-copy genes have been reported in this species, a search and evaluation of suitable reference genes in white clover was undertaken. Results demonstrated a higher accuracy of dPCR relative to qPCR for copy number estimation in white clover. Two genes, Pyruvate dehydrogenase (PDH), and an ATP-dependent protease, identified as single-copy genes, were used as references for copy number estimation by relative quantification. Identification of single-copy genes in white clover will enable the application of relative quantification for copy number estimation of other genes or transgenes in the species. The results generated here validate the use of dPCR as a reliable strategy for transgene copy number estimation in white clover, and provide resources for future copy number studies in this species.

Challenges and Approaches to Genotyping Repetitive DNA

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.

SCOPE: a normalization and copy number estimation method for single-cell DNA sequencing

Whole genome single-cell DNA sequencing (scDNA-seq) enables characterization of copy number profiles at the cellular level. This technology circumvents the averaging effects associated with bulk-tissue sequencing and increases resolution while decreasing ambiguity in tracking the evolutionary history of cancer. ScDNA-seq data is, however, highly sparse and noisy due to the biases and artifacts that are introduced during the library preparation and sequencing procedure. Here, we propose SCOPE, a normalization and copy number estimation method for scDNA-seq data of cancer cells. The main features of SCOPE include: (i) a Poisson latent factor model for normalization, which borrows information across cells and regions to estimate bias, using negative control cells identified by cell-specific Gini coefficients; (ii) modeling of GC content bias using an expectation-maximization algorithm embedded in the normalization step, which accounts for the aberrant copy number changes that deviate from the null distributions; and (iii) a cross-sample segmentation procedure to identify breakpoints that are shared across cells from the same subclone. We evaluate SCOPE on a diverse set of scDNA-seq data in cancer genomics, using array-based calls of purified bulk samples as gold standards and whole-exome sequencing and single-cell RNA sequencing as orthogonal validations; we find that, compared to existing methods, SCOPE offers more accurate copy number estimates. Further, we demonstrate SCOPE on three recently released scDNA-seq datasets by 10X Genomics: we show that it can reliably recover 1% cancer cell spike-ins from a background of normal cells and that it successfully reconstructs cancer subclonal structure from ∼10,000 breast cancer cells.

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

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.