As hypothesized, the noise models learned from homozygous deletion data made very accurate predictions.
Figure 2 shows two regulation events that can hardly be detected by deletion data alone.
The sequences of the peptide region of the remaining three phages showed deletion (data not shown).
Deletion data are further sub-divided into homozygous deletion and heterozygous deletion.
Comparing to previous methods, our approach to using deletion data in inferring regulatory events is relatively simple.
Deletion data were available for 105 samples from 65 patients for analysis of copy neutral and copy loss LOH.
In such a scenario, traditional time course data could supplement the deletion data in detecting the missing edges.
Sophisticated computational methods have been developed in previous studies to use deletion data to infer regulatory networks.
For deletion data, we learned a noise model to distinguish real signals from random fluctuations using an iterative method.
We always obtained the expected products from both the wild type and the DSC Nnd-1 DNA using PCR primers located outside the deletion (data not shown).
While deletion data is good for detecting simple, direct regulatory events, they may not be sufficient for decoding those that are more complicated.
