https://github.com/pprattis/Hidden-Markov-Model-for-sequence-state-decoding

A Python script that implements an HMM with two states a and b. When the model is in state a it is more likely to emit purines A and G. When it is in state b it is more likely to emit pyramidines C and T. Decode the most likely sequence of states for the GGCT sequence using logarithmic scoring instead of normal probability scoring.
https://github.com/pprattis/Hidden-Markov-Model-for-sequence-state-decoding

bioinformatics computer-science decoding hmm hmm-viterbi-algorithm nucleotides program python sequence states student

Last synced: 9 days ago
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A Python script that implements an HMM with two states a and b. When the model is in state a it is more likely to emit purines A and G. When it is in state b it is more likely to emit pyramidines C and T. Decode the most likely sequence of states for the GGCT sequence using logarithmic scoring instead of normal probability scoring.

• Host: GitHub
• URL: https://github.com/pprattis/Hidden-Markov-Model-for-sequence-state-decoding
• Owner: pprattis
• License: mit
• Created: 2019-11-27T11:50:50.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2019-11-27T12:05:41.000Z (almost 6 years ago)
• Last Synced: 2025-03-29T20:44:48.223Z (6 months ago)
• Topics: bioinformatics, computer-science, decoding, hmm, hmm-viterbi-algorithm, nucleotides, program, python, sequence, states, student
• Language: Python
• Size: 28.3 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# A Python Program / Project

**This is a Python project from my early days as a Computer Science student**

_This programm was created for the sixth semester class Bioinformatics

and is one of the final projects for the class_

> #### Description of project

>

>>A Python script that implements an HMM with two states a and b. When the model is in state a it is more likely to emit purines A and G. When it is in state b it is more likely to emit pyramidines C and T. Decode the most likely sequence of states for the GGCT sequence using logarithmic scoring instead of normal probability scoring.

> #### Implementtion of project

>

>In more detail, Table A corresponds to:

> 1. probability 0.9 to be found from state a to state a and state b to state b

> 2. probability 0.1 to be found from state a to b and from state b to a.


>

>Table π corresponds to:


>Initial probability 0.5 to be first in state a, and 0.5 to be first in state b.


>Finally, our model is described by the last figure that matches the shape of the book, adding the 0.5, 0.5 probabilities we have described above.


>Let's make the model more illustrative. This holds that when we are in state a, the probability that A will emit is 0.4, G 0.4, T 0.1 and C 0.1. Also, state b is given the probability of emitting A 0.2, G 0.2, T 0.3 and C 0.3.


>Giving better path for Q = GGTC sequence A -> A -> A -> A.


>And Viterbi score = ln (0.2) + ln (0.072) + ln (0.00648) + ln (0.0005832) = -1.609 - 2.631 - 5.039 - 7.446 ~ = - 16.725

> #### About this project

>

> - The comments to make the code understandable, are within the .py archive

> - This project was written in IDLE, Python’s Integrated Development and Learning Environment.

> - This program runs for Python version 2.7

> - This repository was created to show the variety of the work I did and experience I gained as a student

>