Applications of Generalized Pair Hidden Markov Models to Alignment and Gene Finding Problems

Pachter, Lior; Alexandersson, Marina; Cawley, S.

doi:10.1089/10665270252935520

Cited by 79 publications

(61 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To compute block posterior probabilities, we transform the SFF model to a generalized PHMM [20], in which all repeat states are replaced by a single generalized state R. In generalized HMMs, emission of a state in one step can be an arbitrary string, rather than a single character. In our case, the new state R generates a pair of sequences from the same distribution as defined by one pass through the repeat portion of the original SFF model.…”

Section: Block Decodingmentioning

confidence: 99%

Probabilistic Approaches to Alignment with Tandem Repeats

Nánási

Vinař

Brejová

2013

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Background: Short tandem repeats are ubiquitous in genomic sequences and due to their complex evolutionary history pose a challenge for sequence alignment tools. Results: To better account for the presence of tandem repeats in pairwise sequence alignments, we propose a simple tractable pair hidden Markov model that explicitly models their presence. Using the framework of gain functions, we design several optimization criteria for decoding this model and describe resulting decoding algorithms, ranging from the traditional Viterbi and posterior decoding to block-based decoding algorithms tailored to our model. We compare the accuracy of individual decoding algorithms on simulated and real data and find that our approach is superior to the classical three-state pair HMM. Conclusions:Our study illustrates versatility of pair hidden Markov models coupled with appropriate decoding criteria as a modeling tool for capturing complex sequence features.

show abstract

Section: Block Decodingmentioning

confidence: 99%

Probabilistic Approaches to Alignment with Tandem Repeats

Nánási

Vinař

Brejová

2013

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…Thus, the output depends on the current state and on previous outputs. These tools are widely used in the field of sound processing [11], gene finding and alignment in DNA sequences [12]. They were introduced by Andre Markov in [13] and developed in [14].…”

Section: Fig 3 Concurrent Hidden Markov Models (Left) Hmm Model (Rimentioning

confidence: 99%

HMM-Based Abnormal Behaviour Detection Using Heterogeneous Sensor Network

Aliakbarpour

Khoshhal

Quintas

et al. 2011

Technological Innovation for Sustainability

View full text Add to dashboard Cite

Abstract. This paper proposes a HMM-based approach for detecting abnormal situations in some simulated ATM (Automated Teller Machine) scenarios, by using a network of heterogeneous sensors. The applied sensor network comprises of cameras and microphone arrays. The idea is to use such a sensor network in order to detect the normality or abnormality of the scenes in terms of whether a robbery is happening or not. The normal or abnormal event detection is performed in two stages. . The attained experimental results validate the applicability and effectiveness of the using heterogeneous sensor network to detect abnormal events in the security applications.

show abstract

“…The family of HMMs has also shown to be effective for analysing biological sequence data (Yoon, 2009;Durbin et al, 1998). The applications include: sequence alignment (Pachter et al, 2002), gene and protein structure predictions (Munch and Krogh, 2006;Won et al, 2007), modelling DNA sequencing errors (Lottaz et al, 2003), and for analysing RNA structure (Yoon and Vaidyanathan, 2008;Harmanci et al, 2007). A HMM is composed by the following elements (Rabiner and H., 1986): a finite set of states, a finite alphabet, and probabilities of state transition and symbol emission.…”

Section: General Introductionmentioning

confidence: 99%

Hidden Markov models for gene sequence classification

Mesa¹,

Basterrech²,

Guerberoff³

et al. 2015

Pattern Anal Applic

View full text Add to dashboard Cite

The article presents an application of Hidden Markov Models (HMMs) for pattern recognition on genome sequences. We apply HMM for identifying genes encoding the Variant Surface Glycoprotein (VSG) in the genomes of Trypanosoma brucei (T. brucei) and other African trypanosomes. These are parasitic protozoa causative agents of sleeping sickness and several diseases in domestic and wild animals. These parasites have a peculiar strategy to evade the host's immune system that consists in periodically changing their predominant cellular surface protein (VSG). The motivation for using patterns recognition methods to identify these genes, instead of traditional homology based ones, is that the levels of sequence identity (amino acid and DNA sequence) amongst these genes is often below of what is considered reliable in these methods. Among pattern recognition approaches, HMM are particularly suitable to tackle this problem because they can handle more naturally the determination of gene edges. We evaluate the performance of the model using different number of states in the Markov model, as well as several performance metrics. The model is applied using public genomic data. Our empirical results show that the VSG genes on T. brucei can be safely identified (high sensitivity and low rate of false positives) using HMM.

show abstract

Applications of Generalized Pair Hidden Markov Models to Alignment and Gene Finding Problems

Cited by 79 publications

References 21 publications

Probabilistic Approaches to Alignment with Tandem Repeats

Probabilistic Approaches to Alignment with Tandem Repeats

HMM-Based Abnormal Behaviour Detection Using Heterogeneous Sensor Network

Hidden Markov models for gene sequence classification

Contact Info

Product

Resources

About