Hi, I construct a profile HMM with randomized uniform distributions. I then train the model and, regardless of the training set I use, it ends up looking like this: - a 0.999 probability to go from one delete state to the next - roughly a uniform emission distribution for emission states (insert and match) - about a 50% prob. to go from an insert state to a delete state and a 25% prob. to go to a match or insert state
The result of aligning a sequence to a trained model is as follows: ------------------- + original sequence In other words, any sequence is rejected by the model as being produced by it (the sequence will go though all the delete states in the model and emit the whole sequence in the final insert state). The raw sequences I use should align in a sensible way and are based on an existing seed alignment. Has anyone encountered a similar problem? Is there something obvious that I may be doing wrong? Many thanks, Henry _______________________________________________ Biojava-l mailing list - [EMAIL PROTECTED] http://biojava.org/mailman/listinfo/biojava-l
