Various parameters can be set prior to the pattern discovery. The parameters are listed below and a screenshot of the parameter settings can be seen in figure 16.21.
- Create and search with new model. This will create a new HMM model based on the selected sequences. The found model will be opened after the run and presented in a table view. It can be saved and used later if desired.
- Use existing model. It is possible to use already created models to search for the same pattern in new sequences.
- Minimum pattern length. Here, the minimum length of patterns to search for, can be specified.
- Maximum pattern length. Here, the maximum length of patterns to search for, can be specified.
- Noise (%). Specify noise-level of the model. This parameter has influence on the level of degeneracy of patterns in the sequence(s). The noise parameter can be 1,2,5 or 10 percent.
- Number of different kinds of patterns to predict. Number of iterations the algorithm goes through. After the first iteration, we force predicted pattern-positions in the first run to be member of the background: In that way, the algorithm finds new patterns in the second iteration. Patterns marked 'Pattern1' have the highest confidence. The maximal iterations to go through is 3.
- Include background distribution. For protein sequences it is possible to include information on the background distribution of amino acids from a range of organisms.