scholarly journals Whole genome amplification with SurePlex results in better copy number alteration detection using sequencing data compared to the MALBAC method

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Lieselot Deleye ◽  
Dieter De Coninck ◽  
Christodoulos Christodoulou ◽  
Tom Sante ◽  
Annelies Dheedene ◽  
...  
Keyword(s):  
Copy Number ◽  
Copy Number Alteration ◽  
Whole Genome Amplification ◽  
Whole Genome ◽  
Sequencing Data ◽  
Genome Amplification

scholarly journals SNP-based chromosomal copy number ascertainment following multiple displacement whole-genome amplification

BioTechniques ◽  
2007 ◽  
Vol 42 (1) ◽  
pp. 77-83 ◽  
Author(s):  
Jason J. Corneveaux ◽  
Michael C. Kruer ◽  
Diane Hu-Lince ◽  
Keri E. Ramsey ◽  
Victoria L. Zismann ◽  
...  
Keyword(s):  
Copy Number ◽  
Whole Genome Amplification ◽  
Whole Genome ◽  
Genome Amplification ◽  
Chromosomal Copy Number ◽  
Chromosomal Copy

scholarly journals Comparison of single-cell whole-genome amplification strategies

2018 ◽  
Author(s):  
Nuria Estévez-Gómez ◽  
Tamara Prieto ◽  
Amy Guillaumet-Adkins ◽  
Holger Heyn ◽  
Sonia Prado-López ◽  
...  
Keyword(s):  
Single Cell ◽  
Whole Genome Amplification ◽  
Whole Genome ◽  
Sequencing Data ◽  
Genome Amplification ◽  
Dna Yield ◽  
Amplification Bias ◽  
Random Amplification ◽  
Low Pass ◽  
Amplicon Size

Single-cell genomics is an alluring area that holds the potential to change the way we understand cell populations. Due to the small amount of DNA within a single cell, whole-genome amplification becomes a mandatory step in many single-cell applications. Unfortunately, single-cell whole-genome amplification (scWGA) strategies suffer from several technical biases that complicate the posterior interpretation of the data. Here we compared the performance of six different scWGA methods (GenomiPhi, REPLIg, TruePrime, Ampli1, MALBAC, and PicoPLEX) after amplifying and low-pass sequencing the complete genome of 230 healthy/tumoral human cells. Overall, REPLIg outperformed competing methods regarding DNA yield, amplicon size, amplification breadth, amplification uniformity –being the only method with a random amplification bias–, and false single-nucleotide variant calls. On the other hand, non-MDA methods, and in particular Ampli1, showed less allelic imbalance and ADO, more reliable copy-number profiles and less chimeric amplicons. While no single scWGA method showed optimal performance for every aspect, they clearly have distinct advantages. Our results provide a convenient guide for selecting a scWGA method depending on the question of interest while revealing relevant weaknesses that should be considered during the analysis and interpretation of single-cell sequencing data.

Genetic Alterations Detected by High-Resolution Array Comparative Genomic Hybridization in Microdissected HRS Cells Correlate with Treatment Outcome in Classical Hodgkin Lymphoma

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 522-522
Author(s):  
Christian Steidl ◽  
Adele Telenius ◽  
Sohrab P. Shah ◽  
K-John Cheung ◽  
Lorena Barclay ◽  
...  
Keyword(s):  
Treatment Outcome ◽  
High Resolution ◽  
Disease Progression ◽  
Hodgkin Lymphoma ◽  
Copy Number ◽  
Whole Genome Amplification ◽  
Array Cgh ◽  
Whole Genome ◽  
Copy Number Alterations ◽  
Genome Amplification

Abstract INTRODUCTION: Clinical decision making in Hodgkin lymphoma (HL) is primarily based on clinical variables in part because the scarcity of the malignant Hodgkin Reed Sternberg cells (HRS cells) hampers their molecular characterization. However, more recently investigation using laser capture microdissection has allowed a more detailed analysis of these cells. The objective of this study was to detect genomic alterations in HRS cells and correlate these changes with treatment outcome. PATIENTS AND METHODS: We studied 53 patients with classical HL who were primarily treated at the BC Cancer Agency in Vancouver between 1984 and 2006. All received at least 4 cycles of polychemotherapy and stage-dependent radiotherapy if indicated. The cohort included 43 pretreatment samples and 10 biopsies taken at relapse. Treatment failure was defined as disease progression or relapse at any time (n=23), treatment success as absence of progression (n=30). Whole genome amplification (GenomePlex, Sigma) of pools from 500–1000 individually picked, microdissected HRS cells (Molecular Machines & Industries Cellcut with Nikon Eclipse TE2000-S microscope) was performed. 200 ng of amplified DNA was hybridized to 32k submegabase resolution BAC tiling arrays (SMRT) against sex-matched control-DNA. Scoring of array CGH data was performed by computational analysis using CNA-HMMer v0.1 (available at http://www.cs.ubc.ca/~sshah/acgh/) based on a Hidden Markov Model (HMM). Clustering of the 53 cases was performed using the K-medoids algorithm. Areas of amplification bias and known copy number polymorphisms were excluded. RESULTS: On average whole genome amplification generated 500-fold amplification of genomic DNA. The most frequent copy number alterations (>20% of cases) included gains of 2p14–24.3, 9p12–24.3, 12p11.21–13.33, 16p11.2–13.3, 17p11.2–13.3, 17q11.1–25.1, 19p12–13.3, 19q12–13.43, 20q11.21–13.32, 21q22.11–22.2 and losses of 1p36.31–36.33, 6q11.1–27, 7q22.1–36.3, 8p23.1–23.3, 11q22.3–25, 13q33.3–34 and Xq11.2–28. We also identified several small changes (<5 Mbs) such as loss of 1p36.32, 5q31.1 or 6q23.3 and amplification of 1q32.1, 8q24.21, 17q21.31 or 20q13.2. When comparing the different outcome groups we more frequently identified gains of chromosomal regions 12p13.31–13.33 and 16p12.1–13.3 in treatment failures (39% vs. 13%, Fisher exact p=0.05 and 43% vs. 10%, p=0.009), and losses of chromosomal regions 16q12.1–12.2 and 17p13.1–13.2 (17% vs. 0%, p=0.061 and 27% vs. 4%, p=0.061) were more frequently observed in treatment successes. We did not find significant differences of these changes between pretreatment (n=13) and relapse biopsies (n=10) of patients failing treatment. Using unsupervised analysis we identified a sample cluster of eight cases characterized by simultaneous occurrence of gains of 2p, 16p, 17p, 19q and losses of 6q. Notably, treatment failed in six of these cases. DISCUSSION: The combination of laser microdissection with subsequent WGA and high resolution array CGH provides a robust and sensitive platform for detecting chromosomal imbalances in microdissected HRS cells. We identified at high-resolution new and recurrent changes defining chromosomal regions that potentially harbor oncogenes and tumor suppressor genes crucial to the pathogenesis of HL. Furthermore, we found copy number alterations that are significantly associated with disease progression which, therefore, could serve as predictive factors for treatment outcome.

scholarly journals PD2-2-7: Two wrongs make a right: the use of whole genome amplification for pair-wise genome-wide copy number analysis of limited patient material

2007 ◽  
Vol 2 (8) ◽  
pp. S442-S443
Author(s):  
Trevor J. Pugh ◽  
Allen D. Delaney ◽  
Stephane Flibotte ◽  
Noushin Farnoud ◽  
Irene Li ◽  
...  
Keyword(s):  
Copy Number ◽  
Whole Genome Amplification ◽  
Whole Genome ◽  
Copy Number Analysis ◽  
Genome Amplification ◽  
Genome Wide

scholarly journals A streamlined workflow for single-cells genome-wide copy-number profiling by low-pass sequencing of LM-PCR whole-genome amplification products

PLoS ONE ◽  
2018 ◽  
Vol 13 (3) ◽  
pp. e0193689 ◽  
Author(s):  
Alberto Ferrarini ◽  
Claudio Forcato ◽  
Genny Buson ◽  
Paola Tononi ◽  
Valentina del Monaco ◽  
...  
Keyword(s):  
Copy Number ◽  
Whole Genome Amplification ◽  
Single Cells ◽  
Whole Genome ◽  
Genome Amplification ◽  
Genome Wide ◽  
Low Pass

scholarly journals Improved DOP-PCR (iDOP-PCR): a robust and simple WGA method for efficient amplification of low copy number genomic DNA

2017 ◽  
Author(s):  
Konstantin A. Blagodatskikh ◽  
Vladimir M. Kramarov ◽  
Ekaterina V. Barsova ◽  
Alexey V. Garkovenko ◽  
Dmitriy S. Shcherbo ◽  
...  
Keyword(s):  
Copy Number ◽  
Genomic Dna ◽  
Whole Genome Amplification ◽  
Dropout Rate ◽  
Whole Genome ◽  
Genome Amplification ◽  
Dna Libraries ◽  
Degenerate Oligonucleotide ◽  
Low Copy Number ◽  
Cell Genome

AbstractWhole-genome amplification (WGA) techniques are used for non-specific amplification of low-copy number DNA, and especially for single-cell genome and transcriptome amplification. There are a number of WGA methods that have been developed over the years. One example is degenerate oligonucleotide-primed PCR (DOP-PCR), which is a very simple, fast and inexpensive WGA technique. Although DOP-PCR has been regarded as one of the pioneering methods for WGA, it only provides low genome coverage and a high allele dropout rate when compared to more modern techniques. Here we describe an improved DOP-PCR (iDOP-PCR). We have modified the classic DOP-PCR by using a new thermostable DNA polymerase (SD polymerase) with a strong strand-displacement activity and by adjustments in primers design. We compared iDOP-PCR, classic DOP-PCR and the well-established PicoPlex technique for whole genome amplification of both high- and low-copy number human genomic DNA. The amplified DNA libraries were evaluated by analysis of short tandem repeat genotypes and NGS data. In summary, iDOP-PCR provided a better quality of the amplified DNA libraries compared to the other WGA methods tested, especially when low amounts of genomic DNA were used as an input material.

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