In this study, we characterize error patterns derived from Titanium 454 pyrosequencing data and estimate to what extent different error types account for sequencing errors.
We characterized sequencing error as a function of information about position in the sequence (Position and Seq.length), the presence of homopolymers (Homopolymer) and reference sequence type (Seq.type), all considered being sequence-specific information.
With these design we characterized some systematic error and bias for each NGS platform, and were able to optimize parameters for performing hybrid assembly.
We characterize measurement error due to instrument imprecision and spatial variability as multiplicative (i.e. additive on the log scale) and model it over a range of error types to assess impacts on risk ratio estimates both on a per measurement unit basis and on a per interquartile range (IQR) basis in a time-series study in Atlanta.
As we did for emissivity spectra, we compare those differences with the 2σ error we characterized in Maturilli et al. (2014).
For all test trials in which an error was made, we characterized the type.
Further research into the effects of sample size, geographic representativeness, functional form, and the utility of TLS to address these questions is required to properly characterize errors in field estimates of biomass, and propagate these errors through to maps.
In the panel studies, indoor air pollution will be measured to characterize errors when using exposure information from questionnaires and models.
To evaluate the performance of our design, we characterize sensitivity and symbol error rate (SER) in theoretical analysis, simulations and empirical experiments.
Thus, while we apply the stringent genome- or chromosome-wide threshold for QTL detection to minimize type I errors, we characterize the distribution of pleiotropic effects across the entire set of traits to minimize type II errors.
