You suspect that there is a signal peptide in PepY and you will use an HMM to pr

Question

You suspect that there is a signal peptide in PepY and you will use an HMM to predict its position. The model and parameters are given in the graph below. Note in the figure ‘S’ stands for “signal peptide” state and ‘N’ (marked NS in the diagram) for “Non-signal peptide” state.

2. You suspect that there is a signal peptide in PepY and you will use an HMM to predict its position. The model and parameters are given in the graph below. Note in the figure "S" stands for "signal peptide" state and 'N' (marked NS in the diagram) for "Non-signal peptide" state. C 0.25 C 0.1 Emission of PepY (s) KKRKvRR K 0.05 K 0.4 R 0. R 0.25 State Path of Pep Y (TT) SSSSNNN v 0,75 V 0.1 1.0 02. Ns 0.2 end) a) You are given a sequence s and a path TT (above), what is P(s, TT)? 0.8 b) Name the algorithm used for each of the following questions: (i) Given a sequence, what is the most likely path (m)? (ii) Given a sequence, how likely did it come from this model?

Explanation / Answer

Explanation:-

1.

A signal peptide is a short peptide chain that directs the post translational transport of a protein.
V will be more likely to be in the “NS” state.

2.(b)

(i) A probabilistic sequence model is a set of instructions for generating sequences (over some specified alphabet of permissible characters), which can use probabilistic operations, such as:

• print a letter that is A with probability 0.4, C with probability 0.3, G with probability 0.2, or T with probability 0.1

• go to statement 1 with probability 0.4, to statement 5 with probability 0.3, to statement 17 with probability 0.2, or to statement 9 with probability 0.1 .

(ii)On the left side is pictured the model that repeatedly prints a letter according to the fixed emission probabilities. The model with different letters and/or probabilities is called an i.i.d. model which means independent identically distributed model. With the model pictured on the right, each state (the conventional name for nodes or vertices in the context) emits a fixed letter, and the probabilistic nature comes from the transition probabilities, which controls the frequency with which the next state is chosen at each step. The model emitsthe runs of A and of B, with the former generally longer than the first one. A model of this sort i.e independent identically distributed model., where each state emits a fixed letter and where no two states have the same letter, is called a Markov chain.If there is only one set of the instructions (i.e., path through the flowchart) that generates the sequence, then the score is simply the product of all emission and/or transition probabilities along that path. For instance, the scores of AAC for the above i.i.d. model will be (0.4)2 (0.3). It is not difficult to see that for any i.i.d. model, the sum of the scores for all the sequences of the fixed length is 1. The Markov model, e.g., above on the right, also provides the probability distribution for the sequences of a fixed length, so long as we decides how the first state is being picked.

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