Computing the likelihood of sequence segmentation under Markov modelling

TL;DR

This paper introduces an efficient algorithm to compute the likelihood of sequence segmentation under Markov models, enabling improved prediction of sequence homogeneity and applications like CpG island detection.

Contribution

It presents a novel algorithm for likelihood computation that does not rely on Hidden Markov Models, facilitating sequence segmentation analysis.

Findings

Efficient likelihood computation for sequence segmentation.

Application to CpG island detection.

Method outperforms traditional HMM-based approaches.

Abstract

I tackle the problem of partitioning a sequence into homogeneous segments, where homogeneity is defined by a set of Markov models. The problem is to study the likelihood that a sequence is divided into a given number of segments. Here, the moments of this likelihood are computed through an efficient algorithm. Unlike methods involving Hidden Markov Models, this algorithm does not require probability transitions between the models. Among many possible usages of the likelihood, I present a maximum \textit{a posteriori} probability criterion to predict the number of homogeneous segments into which a sequence can be divided, and an application of this method to find CpG islands.