Dataset Information

STR profiling and Copy Number Variation analysis on single, preserved cells using current Whole Genome Amplification methods.

ABSTRACT: The growing interest in liquid biopsies for cancer research and cell-based non-invasive prenatal testing (NIPT) invigorates the need for improved single cell analysis. In these applications, target cells are extremely rare and fragile in peripheral circulation, which makes the genetic analysis very challenging. To overcome these challenges, cell stabilization and unbiased whole genome amplification are required. This study investigates the performance of four WGA methods on single or a limited number of cells after 24?hour of Streck Cell-Free DNA BCT preservation. The suitability of the DNA, amplified with Ampli1, DOPlify, PicoPLEX and REPLI-g, was assessed for both short tandem repeat (STR) profiling and copy number variant (CNV) analysis after shallow whole genome massively parallel sequencing (MPS). Results demonstrate that Ampli1, DOPlify and PicoPLEX perform well for both applications, with some differences between the methods. Samples amplified with REPLI-g did not result in suitable STR or CNV profiles, indicating that this WGA method is not able to generate high quality DNA after Streck Cell-Free DNA BCT stabilization of the cells.

SUBMITTER: Vander Plaetsen AS

PROVIDER: S-EPMC5719346 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

STR profiling and Copy Number Variation analysis on single, preserved cells using current Whole Genome Amplification methods.

Vander Plaetsen Ann-Sophie AS Deleye Lieselot L Cornelis Senne S Tilleman Laurentijn L Van Nieuwerburgh Filip F Deforce Dieter D

Scientific reports 20171207 1

The growing interest in liquid biopsies for cancer research and cell-based non-invasive prenatal testing (NIPT) invigorates the need for improved single cell analysis. In these applications, target cells are extremely rare and fragile in peripheral circulation, which makes the genetic analysis very challenging. To overcome these challenges, cell stabilization and unbiased whole genome amplification are required. This study investigates the performance of four WGA methods on single or a limited n ...[more]

PMID: 29215049

Similar Datasets

Project description:Copy number variations (CNVs) are gain and loss of DNA sequence of a genome. High throughput platforms such as microarrays and next generation sequencing technologies (NGS) have been applied for genome wide copy number losses. Although progress has been made in both approaches, the accuracy and consistency of CNV calling from the two platforms remain in dispute. In this study, we perform a deep analysis on copy number losses on 254 human DNA samples, which have both SNP microarray data and NGS data publicly available from Hapmap Project and 1000 Genomes Project respectively. We show that the copy number losses reported from Hapmap Project and 1000 Genome Project only have < 30% overlap, while these reports are required to have cross-platform (e.g. PCR, microarray and high-throughput sequencing) experimental supporting by their corresponding projects, even though state-of-art calling methods were employed. On the other hand, copy number losses are found directly from HapMap microarray data by an accurate algorithm, i.e. CNVhac, almost all of which have lower read mapping depth in NGS data; furthermore, 88% of which can be supported by the sequences with breakpoint in NGS data. Our results suggest the ability of microarray calling CNVs and the possible introduction of false negatives from the unessential requirement of the additional cross-platform supporting. The inconsistency of CNV reports from Hapmap Project and 1000 Genomes Project might result from the inadequate information containing in microarray data, the inconsistent detection criteria, or the filtration effect of cross-platform supporting. The statistical test on CNVs called from CNVhac show that the microarray data can offer reliable CNV reports, and majority of CNV candidates can be confirmed by raw sequences. Therefore, the CNV candidates given by a good caller could be highly reliable without cross-platform supporting, so additional experimental information should be applied in need instead of necessarily.

Dataset Information

STR profiling and Copy Number Variation analysis on single, preserved cells using current Whole Genome Amplification methods.

Publications

STR profiling and Copy Number Variation analysis on single, preserved cells using current Whole Genome Amplification methods.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets