Dataset Information

Obstructions to Sampling Qualitative Properties.

ABSTRACT:

Background

Sampling methods have proven to be a very efficient and intuitive method to understand properties of complicated spaces that cannot easily be computed using deterministic methods. Therefore, sampling methods became a popular tool in the applied sciences.

Results

Here, we show that sampling methods are not an appropriate tool to analyze qualitative properties of complicated spaces unless RP = NP. We illustrate these results on the example of the thermodynamically feasible flux space of genome-scale metabolic networks and show that with artificial centering hit and run (ACHR) not all reactions that can have variable flux rates are sampled with variables flux rates. In particular a uniform sample of the flux space would not sample the flux variabilities completely.

Conclusion

We conclude that unless theoretical convergence results exist, qualitative results obtained from sampling methods should be considered with caution and if possible double checked using a deterministic method.

SUBMITTER: Reimers AC

PROVIDER: S-EPMC4546154 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Obstructions to Sampling Qualitative Properties.

Reimers Arne C AC

PloS one 20150819 8

<h4>Background</h4>Sampling methods have proven to be a very efficient and intuitive method to understand properties of complicated spaces that cannot easily be computed using deterministic methods. Therefore, sampling methods became a popular tool in the applied sciences.<h4>Results</h4>Here, we show that sampling methods are not an appropriate tool to analyze qualitative properties of complicated spaces unless RP = NP. We illustrate these results on the example of the thermodynamically feasibl ...[more]

PMID: 26287384

Similar Datasets

Project description:Fetal gastrointestinal tract obstruction (GITO) is the most frequently encountered gastrointestinal defect in the prenatal period. This study aimed to investigate the genetic disorders and pregnancy outcomes of fetal GITO. We reviewed data from 70 pregnancies that were referred for invasive prenatal testing because of fetal GITO. According to the level of obstruction, they were classified into esophageal atresia/stenosis, duodenal atresia/stenosis, jejunal or ileal atresia/stenosis, or anal atresia. Traditional karyotyping was performed on all the 70 pregnancies, and chromosomal microarray analysis (CMA) was performed on 32 of them in parallel. Traditional karyotyping revealed twelve (17.1%) chromosomal abnormalities, including 11 cases of trisomy 21 (Down syndrome), and one case of a supernumerary marker chromosome related to Cat eye syndrome. According to the absence or presence of other ultrasound anomalies, they were categorized into isolated GITO (n = 36) and non-isolated GITO (n = 34). The rate of chromosomal abnormalities in the non-isolated GITO pregnancies was significantly higher than that in the isolated GITO pregnancies (29.4 vs. 5.5%, p < 0.05); the survival rate in the isolated group was significantly higher than that in the non-isolated group (67.6 vs. 34.4%, p < 0.05). Among the 32 cases where CMA was performed, an additional one (3.1%) copy number variant with clinical significance was noted in a fetus with normal karyotype. The microduplication on 7q12 was considered to be the genetic etiology of duodenal stenosis, although it was inherited from a phenotypically normal mother. Our study supports the strong association between Down syndrome and fetal GITO, especially duodenal stenosis. Our findings suggested that the risk of chromosomal abnormalities was increased when GITO was accompanied by other ultrasound anomalies; thus, chromosomal abnormalities and fetal anatomy should be carefully evaluated for pregnancy management of fetal GITO.

Project description:BackgroundClinical implementation of Next-Generation Sequencing (NGS) is challenged by poor control for stochastic sampling, library preparation biases and qualitative sequencing error. To address these challenges we developed and tested two hypotheses.MethodsHypothesis 1: Analytical variation in quantification is predicted by stochastic sampling effects at input of a) amplifiable nucleic acid target molecules into the library preparation, b) amplicons from library into sequencer, or c) both. We derived equations using Monte Carlo simulation to predict assay coefficient of variation (CV) based on these three working models and tested them against NGS data from specimens with well characterized molecule inputs and sequence counts prepared using competitive multiplex-PCR amplicon-based NGS library preparation method comprising synthetic internal standards (IS). Hypothesis 2: Frequencies of technically-derived qualitative sequencing errors (i.e., base substitution, insertion and deletion) observed at each base position in each target native template (NT) are concordant with those observed in respective competitive synthetic IS present in the same reaction. We measured error frequencies at each base position within amplicons from each of 30 target NT, then tested whether they correspond to those within the 30 respective IS.ResultsFor hypothesis 1, the Monte Carlo model derived from both sampling events best predicted CV and explained 74% of observed assay variance. For hypothesis 2, observed frequency and type of sequence variation at each base position within each IS was concordant with that observed in respective NTs (R2 = 0.93).ConclusionIn targeted NGS, synthetic competitive IS control for stochastic sampling at input of both target into library preparation and of target library product into sequencer, and control for qualitative errors generated during library preparation and sequencing. These controls enable accurate clinical diagnostic reporting of confidence limits and limit of detection for copy number measurement, and of frequency for each actionable mutation.

Dataset Information

Obstructions to Sampling Qualitative Properties.

Background

Results

Conclusion

Publications

Obstructions to Sampling Qualitative Properties.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets