Strong repeatability in the pattern of heterogeneity in coverage across a sequence would evidence that the variation is mainly due to inherent characteristics of particular sites or regions of that sequence, rather than representing stochastic variation in library preparation and/or amplification.
We describe a method to evaluate the reproducibility and stochastics in exome library preparation, and delineate the advantages of aggregating the data derived from technical replicates.
This procedure ignores the potential stochastic variation in the library preparation or sequencing reaction.
This raises the question whether this difference is biological (genes are inherently more variable under longer photoperiods) or technical (differences in library or material preparation, as well as stochastic differences, could have resulted in lower correlations in the NDI dataset).
With all the above preparation, consider a d-dimensional stochastic functional differential equations: d x ( t ) = f ( x t, t ) d t + g ( x t, t ) d B ( t ), t 0 ≤ t ≤ T, (2.1).
43 The strategy of local pairwise interchange (LOPI) does not guarantee global optimality, but it is very efficient, 44 and being enhanced by stochastic techniques, brings good results (manuscript in preparation).
Effects of stochastic motion along the reaction coordinate and fluctuational preparation of the potential barrier are discussed.
Diffusion-controlled processes in porous structures are analyzed using planar stochastic fractal objects, which are related to known preparation procedures of porous materials.
Various types of systematic and stochastic fluctuations contribute to noise during biological sample preparation, hybridization, expression measurement and image processing (Schuchhardt et al., 2000).
Now, under the above preparations, we can represent the partial stochastic differential equation (4.1) in the abstract form (1.1).
