Systematic evaluation of reference protein normalization in proteomic experiments.
Ontology highlight
ABSTRACT:
SUBMITTER: Zauber H
PROVIDER: S-EPMC3583035 | biostudies-literature | 2013
REPOSITORIES: biostudies-literature
Publications
Systematic evaluation of reference protein normalization in proteomic experiments.
Zauber Henrik H Schüler Vivian V Schulze Waltraud W
Frontiers in plant science 20130227
Quantitative comparative analyses of protein abundances using peptide ion intensities and their modifications have become a widely used technique in studying various biological questions. In the past years, several methods for quantitative proteomics were established using stable-isotope labeling and label-free approaches. We systematically evaluated the application of reference protein normalization (RPN) for proteomic experiments using a high mass accuracy LC-MS/MS platform. In RPN all sample ...[more]
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2010-05-14 | E-GEOD-10112 | 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. 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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. 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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
2008-02-01 | GSE9842 | 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
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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, 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 3 spike-in replicates and 3 genomic input control replicates
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