790ab023-43e1-4ae4-838b-173fced45862 - samples
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ABSTRACT:
PROVIDER: EGAD00001010120 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:Discovery of common Asian copy number variants using a novel integrated high-resolution array CGH and massively parallel DNA sequencing. We attempted to discover common Asian copy number variants (CNVs) from the DNA of 30 Asian women (10 Korean, 10 CHB (HapMap), 10 JPT (HapMap)) using a custom-designed 24M-oligonucleotide Agilent platform (1.1M X 24 slides). The reference sample for aCGH was NA10851 (HapMap CEPH). In addition to the 30 women, 3 more individuals were analyzed as controls (AK1 (Kim, J.I. et al., 2009 Nature), NA12878 and NA19240).
2010-01-13 | GSE19651 | GEO
Project description:We designed spike-in controls for cfMeDIP-seq experiments. These spike-in controls mitigate batch effects and allow for absolute quantification of cell-free DNA.
| EGAS00001005069 | EGA
Project description:This dataset is part of a study aimed at developing algorithms for the quantification of stable isotope content in microorganisms in microbial communities after labeling them with stable isotope-labeled substrates. For this dataset Escherichia coli cultures were labeled with different percentages (1, 5 and 10%) of fully labeled 13C glucose (13C1-6) and spiked-in into a mock microbial community consisting of 32 species of bacteria, archaea, eukaryote and bacteriophages (UNEVEN Community described in Kleiner et al. 2017 Nat Communications 8(1):1558). The community also contained unlabeled E. coli cells and labeled/unlabeled E. coli cells in the spike-in sample were at a 1:1 ratio. Cultures of E. coli were grown in M9 minimal medium in which a percentage of the glucose was replaced with 13C1-6 glucose for >10 generations to achieve close to complete labeling of cells. The following percentages of 13C1-6 glucose were added 1, 5 and 10%. Triplicate cultures were grown for each percentage. Please note that the unlabeled glucose that was used of course had a natural content of 13C of around 1.1%, thus the 0% added label samples have an actual 13C content of 1.1% and all added label is on top of this value. We included a tab delimited table with this submission providing details on all raw files.
2022-12-30 | PXD024174 | Pride
Project description:Background.The cell-free methylated DNA immunoprecipitation-sequencing (cfMeDIP-seq) method, is adapted to work with low input DNA and with circulating cell-free DNA (cfDNA). This method allowsfor epigenetic profiling from liquid biopsy samples, providing potential information about tissue of origin. Similar to classical immunoprecipitation based enrichment protocols, interpretation requires a referenceor control to draw inference against a composite experimental baseline and against designed standards allowing for cross-experiment comparisons. Methods.To meet the need for a reference control in cfMeDIP-seqexperiments, we designed spike-in controlsand integrated the use of unique molecular index (UMI) to adjust for polymerase chain reaction (PCR)bias, and immunoprecipitation bias caused by the fragment length, G+C content, and CpG density ofthe DNA fragments. This enables for absolute quantification of methylated DNA in picomoles, while retaining epigenomic information that allows for sensitive, tissue-specific detection as well as comparableresults between different experiments. We designed 54 DNA fragments with combinations of methylationstatus (methylated and unmethylated), fragment length in base pair (bp) (80 bp,160 bp,320 bp), G+C content (35%,50%,65%), and fraction of CpGs within a fragment (1/80 bp,1/40 bp,1/20 bp). We checked spike-in control DNA sequence to ensure they had no cross alignment to the human genome and minimized formation of secondary structures to avoid issues with amplification. We carried outcfMeDIP-seq on either solely spike-in DNA fragments, spike-in DNA added to sheared HCT116 genomic DNA or spike-inDNA added tocfDNAfrom acute myeloid leukemia (AML) samples to assess technical and biological biases, determine optimal amount of spike-in DNA required for an experiment and to assess batch effects,respectively. Results. We show thatcfMeDIP-seqenriches for highly methylated regions, with less than 0.01%non-specific binding and preference to high G+C content and CpG fraction DNA fragments. The use of 0.01 ngof spike-in control DNA results in sufficient sequencing reads to adjust for variance due to fragment length,G+C content and CpG fraction without negatively impacting the number of sequencing reads generatedfor each sample. With known amount of each spike-in control, we generated a generalized linear modelthat can absolutely quantify molar amount from read counts while adjusting for fragment length, G+C content, and CpG fraction. Using our spike-in controls, we show that we can greatly mitigate batch effects,reducing batch associated variance in the data to ≤5%of the total variance. Conclusions.The incorporation of spike-in controls allows for easier interpretation of data generated from cfMeDIP-seq and MeDIP-seq experiments when compared to relative read count. Through the use of a generalized linear model tailored to each experiment, molar amount for each genomic region can becalculated, greatly mitigating both biological and technical biases in the data. We have created an Rpackage, spiky, to convert read counts to DNA picomoles while adjusting for fragment length, G+C contentand CpG fraction.
2021-02-16 | GSE166259 | GEO
Project description:Genome wide DNA methylation profiling of paired parental and therapy resistant cancer cell lines. Parental cell lines are mostly established cell lines. Resistant cell lines were obtained through long term exposure of the parental cells to gradually increasing doses of cancer therapies. The Illumina Infinium 450k and EPIC Human DNA methylation Beadchips were used to obtain DNA methylation profiles across approximately 450,000 or 850,000 CpGs from the cells. Samples include 7 parental and 10 derived resistant cell lines.
2020-02-20 | GSE128684 | GEO
Project description:ENCODE Spike-in ChIP-chip Experiments Keywords: ChIP-chip comparison
2006-08-08 | GSE5053 | GEO
Project description:MGS/IGA Targeted Resequencing of 10 HapMap Samples
| PRJNA33799 | ENA
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. This SuperSeries is composed of the following subset Series: GSE9732: Spike-in Experiment for ChIP-chip Simulation GSE9842: Systematic evaluation of variability in simulated ChIP-chip experiments GSE9848: Systematic evaluation of variability in simulated ChIP-chip experiments Kevin_Encode GSE9849: Systematic evaluation of variability in simulated ChIP-chip experiments Myles_Encode GSE10004: ENCODE Spike-In, Yale Group GSE10076: ENCODE spikein, amplified DNA samples, NimbleGen arrays GSE10090: ENCODE spikein, nonamplified DNA samples, NimbleGen arrays GSE10112: Systematic evaluation of variability in simulated ChIP-chip experiments Keywords: SuperSeries For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf Refer to individual Series
2010-09-30 | E-GEOD-10114 | biostudies-arrayexpress