Improving high-resolution copy number variation analysis from next generation sequencing using unique molecular identifiers

Abstract

Background Recently, copy number variations (CNV) impacting genes involved in oncogenic pathways have attracted an increasing attention to manage disease susceptibility. CNV is one of the most important somatic aberrations in the genome of tumor cells. Oncogene activation and tumor suppressor gene inactivation are often attributed to copy number gain/amplification or deletion, respectively, in many cancer types and stages. Recent advances in next generation sequencing protocols allow for the addition of unique molecular identifiers (UMI) to each read. Each targeted DNA fragment is labeled with a unique random nucleotide sequence added to sequencing primers. UMI are especially useful for CNV detection by making each DNA molecule in a population of reads distinct. Results Here, we present molecular Copy Number Alteration (mCNA), a new methodology allowing the detection of copy number changes using UMI. The algorithm is composed of four main steps: the construction of UMI count matrices, the use of control samples to construct a pseudo-reference, the computation of log-ratios, the segmentation and finally the statistical inference of abnormal segmented breaks. We demonstrate the success of mCNA on a dataset of patients suffering from Diffuse Large B-cell Lymphoma and we highlight that mCNA results have a strong correlation with comparative genomic hybridization. Conclusion We provide mCNA, a new approach for CNV detection, freely available at https://gitlab.com/pierrejulien.viailly/mcna/ under MIT license. mCNA can significantly improve detection accuracy of CNV changes by using UMI.

Noninvasive Detection of Fetal Genetic Variations through Polymorphic Sites Sequencing of Maternal Plasma DNA

“…As different alleles of each amplicon had nearly identical sequences with similar amplification properties, relative allelic counts of each target were expected to reserve in each amplicon product. For real plasma cfDNA samples, which was inherently noisy, it may be necessary to optimize amplification conditions to improve detection accuracy, such as using unique molecular identifiers 26 or cSMART techniques 11 . For abnormality detection, nearly all amplification products were used for our method and therefore it should be cost-effective, while the WGS-based approaches used only a fraction of reads mapped to the target chromosomal regions.…”

cfDNA Sequencing: Technological Approaches and Bioinformatic Issues

“…cfDNA fragments are often shorter than DNA extracted from tissue and make it impossible to use conventional approaches for the detection of CNV such as read-depth algorithms. Recent approaches, like mCNA [ 86 ], use the UMI counts instead of read counts to improve high-resolution copy number variation of genes.…”

“…In PCR-based library construction, amplification introduces biases in further reads count because the amplification factor is dependent on many parameters such as library size, GC content, region length or competition between primers overlapping the same locus. Thus, the use of UMI via the mCNA tool allows the direct count of targeted DNA molecules before any amplification and the detection of CNV in a robust and sensitive way [ 86 ]. Cancer Personalized Profiling by Deep Sequencing (CAPP-Seq) …”

ifCNV: a novel isolation-forest-based package to detect copy number variations from various NGS datasets

“…Although these techniques are still routinely used, the rapid implementation of high-throughput nextgeneration sequencing (NGS) methods, especially targeted DNA panels, in clinical laboratories has led to the emergence of a fairly large number of pipelines and algorithms able to detect CNVs from NGS data. [16][17][18][19][20][21][22][23][24][25][26][27][28] Most of these studies use the read-depth approach, relying on the hypothesis that the number of reads aligned to a genomic region is proportional to the copy number of the region. In multiple sample methods, CNVs are detected by comparing the read counts of the sample of interest to the read counts of a reference sample.…”