Dataset Information

Relative quantitation of virus population size in mixed genotype infections using sequencing chromatograms.

ABSTRACT: In order to quantitatively distinguish between highly similar RNA sequences, specific primers or probes must be designed. Unfortunately, consistent and reliable results are not always obtained with conventional techniques. This study uses reverse transcription-PCR coupled with direct terminator sequencing to economically and efficiently distinguish between sequence types in pooled samples while providing accurate relative quantification. As an example, the method is applied to measure template concentration of two Barley yellow dwarf virus (BYDV; family Luteoviridae) species in doubly infected wheat plants. A PERL script (polySNP) was developed that uses PHRED to automatically extract relative peak areas and heights from sequencing chromatograms at polymorphic sites. Peak measurements from experimental samples were compared to a standard curve generated by mixing in vitro transcribed RNA from BYDV-PAV and PAS templates in several ratios (ranging from 1:9 to 9:1 PAV:PAS) prior to RT-PCR amplification and sequencing. The relative amount of RNA template added to a sample was regressed onto the proportion of the chromatogram peak height or area corresponding to one virus species. The function of the best fit line was used to calculate template frequency in the experimental samples.

SUBMITTER: Hall GS

PROVIDER: S-EPMC2246048 | biostudies-literature | 2007 Dec

REPOSITORIES: biostudies-literature

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Relative quantitation of virus population size in mixed genotype infections using sequencing chromatograms.

Hall Gerod S GS Little Damon P DP

Journal of virological methods 20070719 1-2

In order to quantitatively distinguish between highly similar RNA sequences, specific primers or probes must be designed. Unfortunately, consistent and reliable results are not always obtained with conventional techniques. This study uses reverse transcription-PCR coupled with direct terminator sequencing to economically and efficiently distinguish between sequence types in pooled samples while providing accurate relative quantification. As an example, the method is applied to measure template c ...[more]

PMID: 17640742

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Project description:ObjectiveElectronic medical records (EMRs) data is increasingly incorporated into genome-phenome association studies. Investigators hope to share data, but there are concerns it may be "re-identified" through the exploitation of various features, such as combinations of standardized clinical codes. Formal anonymization algorithms (e.g., k-anonymization) can prevent such violations, but prior studies suggest that the size of the population available for anonymization may influence the utility of the resulting data. We systematically investigate this issue using a large-scale biorepository and EMR system through which we evaluate the ability of researchers to learn from anonymized data for genome-phenome association studies under various conditions.MethodsWe use a k-anonymization strategy to simulate a data protection process (on data sets containing clinical codes) for resources of similar size to those found at nine academic medical institutions within the United States. Following the protection process, we replicate an existing genome-phenome association study and compare the discoveries using the protected data and the original data through the correlation (r(2)) of the p-values of association significance.ResultsOur investigation shows that anonymizing an entire dataset with respect to the population from which it is derived yields significantly more utility than small study-specific datasets anonymized unto themselves. When evaluated using the correlation of genome-phenome association strengths on anonymized data versus original data, all nine simulated sites, results from largest-scale anonymizations (population ∼100,000) retained better utility to those on smaller sizes (population ∼6000-75,000). We observed a general trend of increasing r(2) for larger data set sizes: r(2)=0.9481 for small-sized datasets, r(2)=0.9493 for moderately-sized datasets, r(2)=0.9934 for large-sized datasets.ConclusionsThis research implies that regardless of the overall size of an institution's data, there may be significant benefits to anonymization of the entire EMR, even if the institution is planning on releasing only data about a specific cohort of patients.

Dataset Information

Relative quantitation of virus population size in mixed genotype infections using sequencing chromatograms.

Publications

Relative quantitation of virus population size in mixed genotype infections using sequencing chromatograms.

Similar Datasets

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