Mus musculus
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA93549 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Transcript copy number estimation using a mouse whole-genome oligonucleotide microarray (22k Linearity)
| PRJNA93547 | ENA
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:Methods of comprehensive microarray based analyses of single cell DNA are rapidly emerging. Whole genome amplification (WGA) remains a critical component for these methods to be successful. A number of commercially available WGA kits have been independently utilized in previous single cell microarray studies. However, direct comparison of their performance on single cells has not been conducted. The present study demonstrates that among previously published methods, a single cell GenomePlex WGA protocol provides the best combination of speed and accuracy for SNP microarray based copy number analysis when compared to a REPLI-g or GenomiPhi based protocol. Alternatively, for applications that do not have constraints on turn-around time and that are directed at accurate genotyping rather than copy number assignments, a REPLI-g based protocol may provide the best solution.
2010-12-20 | GSE24690 | 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: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:Representational Oligonucleotide Microarray Analysis (ROMA) array for Copy Number Variation Detection.
| PRJNA141915 | ENA
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:The most widely used method for detecting genome-wide protein-DNA interactions is chromatin immunoprecipitation on tiling microarrays, commonly known as ChIP-chip. Here, we conducted the first objective analysis of tiling array platforms, amplification procedures, and signal detection algorithms in a simulated ChIP-chip experiment. Mixtures of human genomic DNA and "spike-ins" comprised of nearly 100 human sequences at various concentrations were hybridized to four tiling array platforms by eight independent groups. Blind to the number of spike-ins, their locations, and the range of concentrations, each group made predictions of the spike-in locations. We found that microarray platform choice is not the primary determinant of overall performance. In fact, variation in performance between labs, protocols and algorithms within the same array platform was greater than the variation in performance between array platforms. However, each array platform had unique performance characteristics that varied with tiling resolution and the number of replicates, which have implications for cost versus detection power. Long oligonucleotide arrays were slightly more sensitive at detecting very low enrichment. On all platforms, simple sequence repeats and genome redundancy tended to result in false positives. LM-PCR and WGA, the most popular sample amplification techniques, reproduced relative enrichment levels with high fidelity. Performance among signal detection algorithms was heavily dependent on array platform. The spike-in DNA samples and the data presented here provide a stable benchmark against which future ChIP platforms, protocol improvements, and analysis methods can be evaluated. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf This SuperSeries is composed of the SubSeries listed below.
2008-02-01 | GSE10114 | GEO
Project description:The most widely used method for detecting genome-wide protein-DNA interactions is chromatin immunoprecipitation on tiling microarrays, commonly known as ChIP-chip. Here, we conducted the first objective analysis of tiling array platforms, amplification procedures, and signal detection algorithms in a simulated ChIP-chip experiment. Mixtures of human genomic DNA and "spike-ins" comprised of nearly 100 human sequences at various concentrations were hybridized to four tiling array platforms by eight independent groups. Blind to the number of spike-ins, their locations, and the range of concentrations, each group made predictions of the spike-in locations. We found that microarray platform choice is not the primary determinant of overall performance. In fact, variation in performance between labs, protocols and algorithms within the same array platform was greater than the variation in performance between array platforms. However, each array platform had unique performance characteristics that varied with tiling resolution and the number of replicates, which have implications for cost versus detection power. Long oligonucleotide arrays were slightly more sensitive at detecting very low enrichment. On all platforms, simple sequence repeats and genome redundancy tended to result in false positives. LM-PCR and WGA, the most popular sample amplification techniques, reproduced relative enrichment levels with high fidelity. Performance among signal detection algorithms was heavily dependent on array platform. The spike-in DNA samples and the data presented here provide a stable benchmark against which future ChIP platforms, protocol improvements, and analysis methods can be evaluated. Keywords: ChIP-chip For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf
2008-12-31 | GSE10112 | GEO
Project description:The most widely used method for detecting genome-wide protein-DNA interactions is chromatin immunoprecipitation on tiling microarrays, commonly known as ChIP-chip. Here, we conducted the first objective analysis of tiling array platforms, amplification procedures, and signal detection algorithms in a simulated ChIP-chip experiment. Mixtures of human genomic DNA and "spike-ins" comprised of nearly 100 human sequences at various concentrations were hybridized to four tiling array platforms by eight independent groups. Blind to the number of spike-ins, their locations, and the range of concentrations, each group made predictions of the spike-in locations. We found that microarray platform choice is not the primary determinant of overall performance. In fact, variation in performance between labs, protocols and algorithms within the same array platform was greater than the variation in performance between array platforms. However, each array platform had unique performance characteristics that varied with tiling resolution and the number of replicates, which have implications for cost versus detection power. Long oligonucleotide arrays were slightly more sensitive at detecting very low enrichment. On all platforms, simple sequence repeats and genome redundancy tended to result in false positives. LM-PCR and WGA, the most popular sample amplification techniques, reproduced relative enrichment levels with high fidelity. Performance among signal detection algorithms was heavily dependent on array platform. The spike-in DNA samples and the data presented here provide a stable benchmark against which future ChIP platforms, protocol improvements, and analysis methods can be evaluated. Keywords: ChIP-chip For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf
2007-12-13 | GSE9849 | GEO