A Probabilistic Model For Sequence Analysis

TL;DR

This paper introduces a probabilistic framework for DNA sequence analysis, modeling nucleotide arrangements with various probability schemes and graphical representations to assess sequence similarity.

Contribution

It presents a novel probabilistic approach with graphical tools for DNA sequence analysis, incorporating successive and unsuccessful probability models.

Findings

Graphical representations of probability schemes

Method to compare new sequences with existing ones

Framework for probabilistic DNA sequence analysis

Abstract

This paper presents a probabilistic approach for DNA sequence analysis. A DNA sequence consists of an arrangement of the four nucleotides A, C, T and G and different representation schemes are presented according to a probability measure associated with them. There are different ways that probability can be associated with the DNA sequence: one way is when the probability of an occurrence of a letter does not depend on the previous one (termed as unsuccessive probability) and in another scheme the probability of occurrence of a letter depends on its previous letter (termed as successive probability). Further, based on these probability measures graphical representations of the schemes are also presented. Using the diagram probability measure one can easily calculate an associated probability measure which can serve as a parameter to check how close is a new sequence to already existing ones.