Homo sapiens
Ontology highlight
ABSTRACT:
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| DRR039925_1.fastq.gz | Fastqsanger.gz | |||
| DRR039925_2.fastq.gz | Fastqsanger.gz | |||
| DRR039926_1.fastq.gz | Fastqsanger.gz | |||
| DRR039926_2.fastq.gz | Fastqsanger.gz | |||
| DRR039927_1.fastq.gz | Fastqsanger.gz |
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Project description:This data is part of a miRNA platform comparison study. We compared the performance characteristics of four commercial miRNA array technologies and found that all platforms performed well in separate measures of performance. The Ambion and Agilent platforms were more accurate, whereas the Illumina and Exiqon platforms were more specific. Furthermore, the data analysis approach had a large impact on the performance, predominantly by improving precision.
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Project description:This data is part of a miRNA platform comparison study. We compared the performance characteristics of four commercial miRNA array technologies and found that all platforms performed well in separate measures of performance. The Ambion and Agilent platforms were more accurate, whereas the Illumina and Exiqon platforms were more specific. Furthermore, the data analysis approach had a large impact on the performance, predominantly by improving precision. Performance of four (4) commercially available miRNA platforms was evaluated using 7 placenta samples spiked with synthetic microRNA spikes (in Latin-square design) absent in placenta. Platforms were primarily evaluated for accuracy and specificity.
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Project description:The most widely-used method for detecting and measuring genome-wide protein-DNA interactions is chromatin immunoprecipitation on tiling microarrays, commonly known as ChIP-chip. Many tiling array platforms, amplification methods, and analysis algorithms exist for ChIP-chip, but a rigorous assessment of the relative performance of these factors has not been reported. In a multi-lab simulation of a ChIP-chip experiment, we conducted the first objective analysis of tiling array platforms and analysis algorithms. We designed a complex mixture of human genomic DNA with a "spike-in" comprised of nearly 100 human sequences at various concentrations. Eight independent groups hybridized these mixtures to four different tiling array platforms. The groups were blind to the composition of the spike-in mix, the range of concentrations covered, or how many sequences it contained. Still blind to the key, each group made predictions of the spike-in locations based on their measurements. The results reveal that all commercial tiling array platforms perform well, although each platform and analysis algorithm has distinct performance characteristics. Simple sequence repeats and genome redundancy tend to result in false positives on oligonucleotide platforms. We also compare genome-wide platforms with regard to performance and cost. The spike-in DNA samples and the resulting array data presented in our study provide a stable benchmark against which future ChIP platforms, protocol improvements, and analysis methods can be evaluated. Keywords: Spike in Control For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf
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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
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Project description:We compare the performance of two library preparation protocols (poly(A) and exome capture) in in vitro degraded RNA samples VcaP cell were grown, and treated with MDV3100 (enzalutamide) or DHT (dihydrotestosterone), intact RNA was isolated and samples were prepared in technical triplicates using two library preparation protocol. Also cells were subject to in vitro degradation through incubation of the whole cell lysate in 37C for increasing amounts of time. Following incbation paired capture and poly(A) libraries were prepared.
2015-08-03 | E-GEOD-64113 | biostudies-arrayexpress
Project description:The most widely-used method for detecting and measuring genome-wide protein-DNA interactions is chromatin immunoprecipitation on tiling microarrays, commonly known as ChIP-chip. Many tiling array platforms, amplification methods, and analysis algorithms exist for ChIP-chip, but a rigorous assessment of the relative performance of these factors has not been reported. In a multi-lab simulation of a ChIP-chip experiment, we conducted the first objective analysis of tiling array platforms and analysis algorithms. We designed a complex mixture of human genomic DNA with a "spike-in" comprised of nearly 100 human sequences at various concentrations. Eight independent groups hybridized these mixtures to four different tiling array platforms. The groups were blind to the composition of the spike-in mix, the range of concentrations covered, or how many sequences it contained. Still blind to the key, each group made predictions of the spike-in locations based on their measurements. The results reveal that all commercial tiling array platforms perform well, although each platform and analysis algorithm has distinct performance characteristics. Simple sequence repeats and genome redundancy tend to result in false positives on oligonucleotide platforms. We also compare genome-wide platforms with regard to performance and cost. The spike-in DNA samples and the resulting array data presented in our study provide a stable benchmark against which future ChIP platforms, protocol improvements, and analysis methods can be evaluated. Keywords: Spike in Control For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf With the availability of sequenced genomes and whole-genome tiling microarrays, many researchers have conducted experiments using ChIP-chip and related methods to study genome-wide protein-DNA interactions1-9. These are powerful yet challenging techniques, which are comprised of many steps that can introduce variability in the final results. One potentially important factor is the relative performance of different types of tiling arrays. Currently the most popular platforms for performing ChIP-chip experiments are PCR-product based tiling arrays spotted in academic laboratories and commercial oligonucleotide-based tiling arrays from Affymetrix, NimbleGen, and Agilent. A second factor known to introduce variation is the DNA amplification protocol, which is often required because the low DNA yield from a ChIP experiment prevents direct detection on microarrays. A third factor is the algorithm used for detecting regions of enrichment from the tiling array data. A number of algorithms have been developed, but until this report there was no benchmark dataset to systematically evaluate them. In this study, we used a spike-in experiment to systematically evaluate the effects of tiling microarrays, amplification protocols, and data analysis algorithms on ChIP-chip results. There are other potentially important factors that from a practical standpoint are more difficult to systematically control and evaluate, and are not assessed here. These include the experimenter, the amount of starting material used, size of DNA fragments after shearing, DNA labeling method, and hybridization conditions. There have been several studies evaluating the performance of gene expression microarrays and analysis algorithms10-13. However, because tiling arrays must cover large genomic regions, they have different probe properties and high probe densities, and therefore present different biochemical and informatics challenges. Thus the results from the expression array spike-in experiments are not necessarily directly relevant to tiling array experiments. One recent study compared the performance of array-based (ChIP-chip) and sequence-based (ChIP-PET) technologies on a real ChIP experiment14. However, because this was an exploratory experiment, the list of absolute “true positive” targets was and is unknown. Since this experiment was performed without a key, the sensitivity and specificity of each technology had to be estimated retrospectively by qPCR validation of targets predicted from each platform. 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