Gene Prediction using HMM [Hidden Markov Model]

Predicting a gene in a DNA sequence is essential in order to understand various biological phenomena related to the genes.

This Project have implementation of the Hidden Markov Model for Gene Prediction, it helps us to find the meaningful piece of information in a new sequence of DNA. We align the DNA then model it to obtain the Recurrent patterns, furthermore we visualize it to build a generative model to describe it. To understand, learn and distinguish characteristics of each state and recognize them, we identify and label the different regions of the DNA sequence. We update the previous knowledge about biological sequence using probabilistic sequence modelling. Applying the Graph Theory to the nucleotide relation in the sequence, we are able to infer the adjacency matrix to be applied in Markov Model and Hidden Markov Model. Now using the Markov Model, we get the base vector values and after that we obtain the emission matrix and transmission matrix. Furthermore, computing the obtained value, we are able to train the algorithm and predict the gene sequence from the hidden states.