SNPchip analysis of single cell products
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE8567 | GEO | 2007/12/20
SECONDARY ACCESSION(S): PRJNA101711
REPOSITORIES: GEO
Similar Datasets
Project description:Single-cell human genome analysis using whole-genome amplified product is hampered by allele bias during amplification. Using an oligonucleotide SNP array, we examined the nature of the allele bias and its effect on the chromosomal copy number analysis. Experiment Overall Design: TB106, lymphoblastoid cell line; CMK11-5 and CMK86, cell line; WGA, whole-genome amplified from bulk DNA; SC, whole-genome amplified product from single cell. Genotype and copy number status were compared between non-amplified products and their single cell products.
2008-06-16 | E-GEOD-8567 | biostudies-arrayexpress
Project description:RNA-Seq is a powerful tool for transcriptome profiling, but is hampered by sequence-dependent bias and inaccuracy at low copy numbers intrinsic to exponential PCR amplification. We developed a simple strategy for mitigating these complications, allowing truly digital RNA-Seq. Following reverse transcription, a large set of barcode sequences is added in excess, and nearly every cDNA molecule is uniquely labeled by random attachment of barcode sequences to both ends. After PCR, we applied paired-end deep sequencing to read the two barcodes and cDNA sequences. Rather than counting the number of reads, RNA abundance is measured based on the number of unique barcode sequences observed for a given cDNA sequence. We optimized the barcodes to be unambiguously identifiable even in the presence of multiple sequencing errors. This method allows counting with single copy resolution despite sequence-dependent bias and PCR amplification noise, and is analogous to digital PCR but amendable to quantifying a whole transcriptome. We demonstrated transcriptome profiling of E. coli with more accurate and reproducible quantification than conventional RNA-Seq.
2012-01-07 | GSE34449 | GEO
Project description:Primary objectives: The primary objective is to investigate circulating tumor DNA (ctDNA) via deep sequencing for mutation detection and by whole genome sequencing for copy number analyses before start (baseline) with regorafenib and at defined time points during administration of regorafenib for treatment efficacy in colorectal cancer patients in terms of overall survival (OS).
Primary endpoints: circulating tumor DNA (ctDNA) via deep sequencing for mutation detection and by whole genome sequencing for copy number analyses before start (baseline) with regorafenib and at defined time points during administration of regorafenib for treatment efficacy in colorectal cancer patients in terms of overall survival (OS).
| 2533916 | ecrin-mdr-crc
Project description:Background: Insufficient quantities of human genomic DNA are a limiting factor for many clinical applications. Whole genome amplification (WGA) is an approach designed to overcome small amount of DNA for genome-wide genetic tests as it allows amplification of the entire genome from picogram or nanogram quantities of DNA. Various strategies of WGA have been developed; however, none of them can guarantee the absence of amplification bias. High-quality genome-representative amplified DNA is crucial for WGA use in basic research and clinical genetics. Thus, systematic evaluation of WGA effect on downstream methods is necessary. Results: In this paper, 4 multiple displacement amplification (MDA) -based and 2 PCR-based WGA kits were compared in their effect on segmental copy-number changes as well as copy-number neutral loss of heterozygosity detection by high-density oligonucleotide DNA arrays. We described outcomes and limits for each individual WGA; however, the main goal of this study was chiefly to show a general compatibility and features specific for particular WGA strategy. The main outcomes are as follows: 1) MDA-based WGAs showed higher tendency to generate false positive imbalances in contrast to PCR-based WGAs with higher risk of false negativity; 2) the specific risk of false positivity and/or negativity increased with decreasing copy-number segments size; 3) single-cell WGAs showed significantly worse effect on results in comparison to WGAs with nanogram level of DNA as input; 4) PCR-based WGAs were not compatible with copy-number neutral loss of heterozygosity analysis based on single nucleotide polymorphisms in restriction digestion sites and also showed higher risk of copy-number neutral loss of heterozygosity false negativity if combined with analysis based on simple hybridization. Conclusions: This study gives a comprehensive insight into the WGA effect on DNA array analysis. The results of this study help to choose WGA according to individual user requirements and options. Moreover, we show a strategy to verify and validate segmental copy-number changes detection by DNA array protocol including any WGA for any purpose to attain the highest efficiency without an unnecessary WGA bias.
2015-09-29 | GSE73513 | GEO
Project description:RNA-Seq is a powerful tool for transcriptome profiling, but is hampered by sequence-dependent bias and inaccuracy at low copy numbers intrinsic to exponential PCR amplification. We developed a simple strategy for mitigating these complications, allowing truly digital RNA-Seq. Following reverse transcription, a large set of barcode sequences is added in excess, and nearly every cDNA molecule is uniquely labeled by random attachment of barcode sequences to both ends. After PCR, we applied paired-end deep sequencing to read the two barcodes and cDNA sequences. Rather than counting the number of reads, RNA abundance is measured based on the number of unique barcode sequences observed for a given cDNA sequence. We optimized the barcodes to be unambiguously identifiable even in the presence of multiple sequencing errors. This method allows counting with single copy resolution despite sequence-dependent bias and PCR amplification noise, and is analogous to digital PCR but amendable to quantifying a whole transcriptome. We demonstrated transcriptome profiling of E. coli with more accurate and reproducible quantification than conventional RNA-Seq. We analyzed two replicates of the same bulk E. coli transcriptome sample. In each sample, we included internal standards to demonstrate that the digital RNA-Seq system may accurately count fragments correctly.
2012-01-07 | E-GEOD-34449 | biostudies-arrayexpress
Project description:Methods for haplotyping and DNA copy number typing of single cells are paramount for studying genomic heterogeneity and enabling genetic diagnosis. Before analyzing the DNA of a single cell by microarray or next-generation sequencing, a whole-genome amplification (WGA) process is required that substantially distorts the frequency and composition of the cell’s alleles. As a consequence, haplotyping methods suffer from error-prone discrete SNP-genotypes (AA, AB, BB), and DNA copy number profiling remains difficult as true DNA copy number aberrations have to be discriminated from WGA-artifacts. Here, we developed a single-cell genome analysis method that reconstructs genome-wide haplotype architectures as well as the copy-number and segregational origin of those haplotypes by deciphering WGA-distorted SNP B-allele fractions, using a process we coin haplarithmisis. We demonstrate clinical precision of the method on single cells biopsied from human embryos to diagnose disease alleles genome wide, we advance and facilitate the detection of numerical and structural chromosomal anomalies in single cells, and can distinguish meiotic from mitotic segregation errors in a single assay.
2015-05-15 | GSE60907 | GEO
Project description:Methods for haplotyping and DNA copy number typing of single cells are paramount for studying genomic heterogeneity and enabling genetic diagnosis. Before analyzing the DNA of a single cell by microarray or next-generation sequencing, a whole-genome amplification (WGA) process is required that substantially distorts the frequency and composition of the cell’s alleles. As a consequence, haplotyping methods suffer from error-prone discrete SNP-genotypes (AA, AB, BB), and DNA copy number profiling remains difficult as true DNA copy number aberrations have to be discriminated from WGA-artifacts. Here, we developed a single-cell genome analysis method that reconstructs genome-wide haplotype architectures as well as the copy-number and segregational origin of those haplotypes by deciphering WGA-distorted SNP B-allele fractions, using a process we coin haplarithmisis. We demonstrate clinical precision of the method on single cells biopsied from human embryos to diagnose disease alleles genome wide, we advance and facilitate the detection of numerical and structural chromosomal anomalies in single cells, and can distinguish meiotic from mitotic segregation errors in a single assay.
2015-05-15 | GSE60909 | GEO
Project description:Illumina Infinium whole genome genotyping (WGG) arrays are increasingly being applied in cancer genomics to study gene copy number alterations and allele-specific aberrations such as loss-of-heterozygosity (LOH). Methods developed for normalization of WGG arrays have mostly focused on diploid, normal samples. However, for cancer samples genomic aberrations may confound normalization and data interpretation. Therefore, we examined the effects of the conventionally used normalization method for Illumina Infinium arrays when applied to cancer samples. We demonstrate an asymmetry in the detection of the two alleles for each SNP, which deleteriously influences both allelic proportions and copy number estimates. The asymmetry is caused by a remaining bias between the two dyes used in the Infinium II assay after using the normalization method in Illumina’s proprietary software (BeadStudio). We propose a quantile normalization strategy for correction of this dye bias. We tested the normalization strategy using 535 individual hybridizations from 10 data sets from the analysis of cancer genomes and normal blood samples generated on Illumina Infinium II 300k version 1 and 2, 370k and 550k BeadChips. We show that the proposed normalization strategy successfully removes asymmetry in estimates of both allelic proportions and copy numbers. Additionally, the normalization strategy reduces the technical variation for copy number estimates while retaining the response to copy number alterations. The proposed normalization strategy represents a valuable low-level analysis tool that improves the quality of data obtained from Illumina Infinium arrays, in particular when used for LOH and copy number variation studies.
2008-10-04 | GSE11977 | GEO
Project description:Genome-wide copy number and allele-specific copy number analysis of choroid plexus tumors (II)
| PRJNA260859 | ENA
Project description:Genome-wide copy number and allele-specific copy number analysis of choroid plexus tumors (I)
| PRJNA259772 | ENA