Dataset Information

Using small samples to estimate neutral component size and robustness in the genotype-phenotype map of RNA secondary structure.

ABSTRACT: In genotype-phenotype (GP) maps, the genotypes that map to the same phenotype are usually not randomly distributed across the space of genotypes, but instead are predominantly connected through one-point mutations, forming network components that are commonly referred to as neutral components (NCs). Because of their impact on evolutionary processes, the characteristics of these NCs, like their size or robustness, have been studied extensively. Here, we introduce a framework that allows the estimation of NC size and robustness in the GP map of RNA secondary structure. The advantage of this framework is that it only requires small samples of genotypes and their local environment, which also allows experimental realizations. We verify our framework by applying it to the exhaustively analysable GP map of RNA sequence length L = 15, and benchmark it against an existing method by applying it to longer, naturally occurring functional non-coding RNA sequences. Although it is specific to the RNA secondary structure GP map in the first place, our framework can probably be transferred and adapted to other sequence-to-structure GP maps.

SUBMITTER: Weiß M

PROVIDER: S-EPMC7276537 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Publications

Using small samples to estimate neutral component size and robustness in the genotype-phenotype map of RNA secondary structure.

Weiß Marcel M Ahnert Sebastian E SE

Journal of the Royal Society, Interface 20200520 166

In genotype-phenotype (GP) maps, the genotypes that map to the same phenotype are usually not randomly distributed across the space of genotypes, but instead are predominantly connected through one-point mutations, forming network components that are commonly referred to as neutral components (NCs). Because of their impact on evolutionary processes, the characteristics of these NCs, like their size or robustness, have been studied extensively. Here, we introduce a framework that allows the estim ...[more]

PMID: 32429824

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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

Using small samples to estimate neutral component size and robustness in the genotype-phenotype map of RNA secondary structure.

Publications

Using small samples to estimate neutral component size and robustness in the genotype-phenotype map of RNA secondary structure.

Similar Datasets

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