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In this study, we describe how the optimal number of replicate measures (technical replicates) for each biological sample (biological replicate) can be determined.
The dataset of the biological replicate can be found in the Additional file 2: Figure S1.
This observation suggests that the effects of biological or analytical variation from replicate to replicate can be reduced if comparisons are made between paired samples.
These data can then be quickly surveyed using the data browser and an Edit Mode track, and binding regions considered spurious (for example, those also observed in control experiments) or unreliable (for example, those observed in only one experimental replicate) can be flagged and then filtered using one of MochiView's data refinement utilities.
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The specific statistical methods applied and descriptions of replicates can be found in the figure legends.
Going back months later to re-do these experiments with better controls or more replicates can be very frustrating!
Consequently, the commonly used quality control method of applying correlation analysis on technical replicates can be adopted for assessing array performance based on different biological samples using tERCs.
The existence of negative correlation among the replicates can be seen in Figure 2 (more downward spikes than upward).
Multiple analytical replicates can be created for one biological replicate.
The replicates can be those profiled either in the same plates or in different plates.
The coefficient of variation in amplicon yield among replicates can be as much as 45.1% [ 4].
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