A wholly-defined Agilent microarray spike-in dataset
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ABSTRACT:
ORGANISM(S): Drosophila melanogaster
PROVIDER: GSE24866 | GEO | 2010/10/23
SECONDARY ACCESSION(S): PRJNA132121
REPOSITORIES: GEO
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Project description:We have generated a wholly defined spike-in dataset for Agilent microarrays consisting of 12 arrays with more than 2000 differentially expressed, and approximately 3600 background, cRNAs. The composition of this âAg Spikeâdataset is identical to that of our previous Platinum Spike dataset (GSE21344) and therefore allows direct cross-platform comparison. Comparison between the Ag Spike and Platinum Spike studies shows high agreement between results obtained using the Affymetrix and Agilent platforms. We used the same 28 PCR pools that had been used before to generate the Platinum-Spike dataset (GSE21344). These pools contain PCR products from 5725 Drosophila Gene Collection release 1.0 (DGCr1) cDNA clones. We mixed PCR products from all pools together with specified relative abundance to generate two samples representing the A and B conditions. Both samples were in vitro transcribed and then labeled with Cy3 and Cy5 fluorescent dye. Labeled A samples were hybridized with B samples labeled with reverse dye to three Agilent Drosophila Gene Expression microarrays. The process from in vitro transcription to hybridization was repeated twice. At the end we generated 12 arrays that were randomly placed in three independent slides to remove potential systematic error due to array position.
2010-10-23 | E-GEOD-24866 | biostudies-arrayexpress
Project description:We present a new wholly defined Affymetrix spike-in dataset consisting of 18 microarrays. Over 5700 RNAs are spiked in at relative concentrations ranging from 1- to 4-fold, and the arrays from each condition are balanced with respect to both total RNA amount and degree of positive- versus negative-fold change. We use this new â??Platinum Spikeâ?? dataset to evaluate microarray analysis routes and contrast the results to those achieved using our earlier Golden Spike dataset. PCR products from 5725 Drosophila Gene Collection release 1.0 (DGCr1) cDNA clones were collected into 28 distinct pools, and three independent in vitro transcription and labeling reactions were performed for each pool. Labeled cRNAs from each individual pool were then added at specified amounts to samples A and B to achieve the desired fold change differences between samples. 24 cRNAs generated from DGCr2 cDNA clones were added to each sample in known concentrations before hybridization.
2010-04-28 | E-GEOD-21344 | biostudies-arrayexpress
Project description:We present a new wholly defined Affymetrix spike-in dataset consisting of 18 microarrays. Over 5700 RNAs are spiked in at relative concentrations ranging from 1- to 4-fold, and the arrays from each condition are balanced with respect to both total RNA amount and degree of positive- versus negative-fold change. We use this new “Platinum Spike” dataset to evaluate microarray analysis routes and contrast the results to those achieved using our earlier Golden Spike dataset.
2010-04-15 | GSE21344 | GEO
Project description:A wholly-defined Agilent microarray spike-in dataset
| PRJNA132121 | ENA
Project description:ENCODE Spike-in ChIP-chip Experiments Keywords: ChIP-chip comparison
2006-08-08 | GSE5053 | 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 and analysis 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. All commercial tiling array platforms performed well, although each platform and analysis algorithm had distinct performance and cost characteristics. Simple sequence repeats and genome redundancy tend to result in false positives on oligonucleotide platforms. The spike-in DNA samples and the resulting array data presented here provide a stable benchmark against which future ChIP platforms, protocol improvements, and analysis methods can be evaluated. Keywords: chip-ChIP simulation For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf For each replicate array, we labeled 1µg of spike-in sample and 1µg of control sample. The Bioprime Plus Array CGH labeling Module (Invitrogen Catolog number 18095-014) was used. The spike-in was labeled with the red channel dye (Alexa Fluor-647) and the control was labeled with the green channel dye (Alexa Fluor-555) in two of the replicates. A dye swap was performed for the third replicate. These samples were then competitively hybridized to three replicate 244k arrays from Agilent, (AMADID 25150451). We hybridized the samples to the arrays using the Agilent Array CGH protocol, with some modifications. Briefly, labeled DNA was combined with Cot-1 DNA, blocking reagent, and hybridization buffer. The hybridizations were carried out at 60°C. The arrays were scanned using an Agilent dual laser scanner, and the images were processed using Agilent Feature Extraction Software.
2010-05-22 | E-GEOD-9732 | biostudies-arrayexpress
Project description:Preferred analysis methods for Affymetrix GeneChips. II. An expanded, balanced, wholly-defined spike-in dataset
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Project description:Spike-in normalization is a powerful approach to assess global changes in data obtained from genomic mapping of DNA-associated proteins by methods such as ChIP-sequencing (ChIP-seq) or CUT&RUN. While multiple spike-in methods provide detailed documentation, the implementation of these approaches often omit critical quality control steps and veer from the established procedures. Here, we show that proper application of spike-in normalization can increase quantification accuracy across a spectrum of conditions. Next, we outline how misuse of spike-in approaches can create erroneous biological interpretations and provide guidelines to minimize pitfalls when applying this approach to normalize data from protein-DNA interaction results.
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Project description:In this study, we assess technical differences between commonly used single-cell RNA-Sequencing (scRNA-Seq) methods. We perform scRNA-seq on a homogenous population of mouse embryonic stem cells along with two kinds of control spike-in molecules to assess sensitivity and accuracy of these specific methods. In this dataset, we assess the RNA-degradation and decay rates by subjecting both spike-in molecules to range of repeated freezing and thawing (freeze-thaw) cycles. We manually add spike-in molecules across a 96-well plate (containing cells and reagents), perform Smart-Seq2 method manually and generate single-cell libraries using Nextera XT kit
2017-03-14 | E-MTAB-5481 | biostudies-arrayexpress
Project description:Total RNA from each sample was quantified using the NanoDrop ND-1000 and RNA integrity was assessed by Bioanalyzer 2100 or Mops electrophoresis. The sample preparation and microarray hybridization were performed based on the Arraystar’s standard protocols. Briefly, the total RNAs were immunoprecipitated with anti-N6-methyladenosine (m6A) antibody. The modified RNAs were eluted from the immunoprecipitated magnetic beads as the “IP”. The unmodified RNAs were recovered from the supernatant as “Sup”. The “IP” and “Sup” RNAs were labeled with Cy5 and Cy3 respectively as cRNAs in separate reactions using Arraystar RNA Labeling protocol. The cRNAs were combined together and hybridized onto Arraystar Mouse mRNA&lncRNA Epitranscriptomic Microarray (8x60K, Arraystar). After washing the slides, the arrays were scanned in two-color channels by an Agilent Scanner G2505C.Agilent Feature Extraction software (version 11.0.1.1) was used to analyze acquired array images. Raw intensities of IP (immunoprecipitated, Cy5-labelled) and Sup (supernatant, Cy3-labelled) were normalized with average of log2-scaled Spike-in RNA intensities. After Spike-in normalization, the probe signals having Present (P) or Marginal (M) QC flags in a certain proportion were retained for further “m6A quantity” analyses. “m6A quantity” was calculated for the m6A methylation amount based on the IP (Cy5-labelled) normalized intensities. Differentially m6A-methylated RNAs between two comparison groups were identified by filtering with the fold change and statistical significance (p-value) thresholds. Hierarchical Clustering was performed to show the distinguishable m6A-methylation pattern among samples.
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