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A model-based information sharing protocol for profile Hidden Markov Models used for HIV-1 recombination detection.

Bulla I, Schultz AK, Chesneau C, Mark T, Serea F - BMC Bioinformatics (2014)

Bottom Line: In order to implement the proposed protocol, we make use of an existing HMM architecture and its associated inference engine.Thereby, we demonstrate that the performance of pHMMs can be significantly improved by the proposed technique.Moreover, we show that our algorithm performs significantly better than Simplot and Bootscanning.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institut für Mathematik und Informatik, Universität Greifswald, Walther-Rathenau-Straße 47, 17487 Greifswald, Germany. ingobulla@gmail.com.

ABSTRACT

Background: In many applications, a family of nucleotide or protein sequences classified into several subfamilies has to be modeled. Profile Hidden Markov Models (pHMMs) are widely used for this task, modeling each subfamily separately by one pHMM. However, a major drawback of this approach is the difficulty of dealing with subfamilies composed of very few sequences. One of the most crucial bioinformatical tasks affected by the problem of small-size subfamilies is the subtyping of human immunodeficiency virus type 1 (HIV-1) sequences, i.e., HIV-1 subtypes for which only a small number of sequences is known.

Results: To deal with small samples for particular subfamilies of HIV-1, we introduce a novel model-based information sharing protocol. It estimates the emission probabilities of the pHMM modeling a particular subfamily not only based on the nucleotide frequencies of the respective subfamily but also incorporating the nucleotide frequencies of all available subfamilies. To this end, the underlying probabilistic model mimics the pattern of commonality and variation between the subtypes with regards to the biological characteristics of HI viruses. In order to implement the proposed protocol, we make use of an existing HMM architecture and its associated inference engine.

Conclusions: We apply the modified algorithm to classify HIV-1 sequence data in the form of partial HIV-1 sequences and semi-artificial recombinants. Thereby, we demonstrate that the performance of pHMMs can be significantly improved by the proposed technique. Moreover, we show that our algorithm performs significantly better than Simplot and Bootscanning.

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The fraction of correctly classified sequences forTpure. This fraction is shown for the application of jpHMM ml, jpHMM scal, jpHMM semi, and jpHMM prob to Tpure, stratified by subtypes.
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Figure 8: The fraction of correctly classified sequences forTpure. This fraction is shown for the application of jpHMM ml, jpHMM scal, jpHMM semi, and jpHMM prob to Tpure, stratified by subtypes.

Mentions: We compare the four jpHMM versions jpHMM semi, jpHMM scal, jpHMM ml, and jpHMM prob on the test sequence set Tpure and all of them but jpHMM scal on Trec. The results of this evaluation are illustrated in Figures 8, 9 and 10. jpHMM scal is not tested on Trec since the purpose of introducing jpHMM scal was to demonstrate that it is not possible to reach the performance of jpHMM ml or jpHMM prob by employing an arbitrary heuristic approach to estimate the emission probabilities. For this purpose, testing only on Tpure is sufficient.


A model-based information sharing protocol for profile Hidden Markov Models used for HIV-1 recombination detection.

Bulla I, Schultz AK, Chesneau C, Mark T, Serea F - BMC Bioinformatics (2014)

The fraction of correctly classified sequences forTpure. This fraction is shown for the application of jpHMM ml, jpHMM scal, jpHMM semi, and jpHMM prob to Tpure, stratified by subtypes.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4230192&req=5

Figure 8: The fraction of correctly classified sequences forTpure. This fraction is shown for the application of jpHMM ml, jpHMM scal, jpHMM semi, and jpHMM prob to Tpure, stratified by subtypes.
Mentions: We compare the four jpHMM versions jpHMM semi, jpHMM scal, jpHMM ml, and jpHMM prob on the test sequence set Tpure and all of them but jpHMM scal on Trec. The results of this evaluation are illustrated in Figures 8, 9 and 10. jpHMM scal is not tested on Trec since the purpose of introducing jpHMM scal was to demonstrate that it is not possible to reach the performance of jpHMM ml or jpHMM prob by employing an arbitrary heuristic approach to estimate the emission probabilities. For this purpose, testing only on Tpure is sufficient.

Bottom Line: In order to implement the proposed protocol, we make use of an existing HMM architecture and its associated inference engine.Thereby, we demonstrate that the performance of pHMMs can be significantly improved by the proposed technique.Moreover, we show that our algorithm performs significantly better than Simplot and Bootscanning.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institut für Mathematik und Informatik, Universität Greifswald, Walther-Rathenau-Straße 47, 17487 Greifswald, Germany. ingobulla@gmail.com.

ABSTRACT

Background: In many applications, a family of nucleotide or protein sequences classified into several subfamilies has to be modeled. Profile Hidden Markov Models (pHMMs) are widely used for this task, modeling each subfamily separately by one pHMM. However, a major drawback of this approach is the difficulty of dealing with subfamilies composed of very few sequences. One of the most crucial bioinformatical tasks affected by the problem of small-size subfamilies is the subtyping of human immunodeficiency virus type 1 (HIV-1) sequences, i.e., HIV-1 subtypes for which only a small number of sequences is known.

Results: To deal with small samples for particular subfamilies of HIV-1, we introduce a novel model-based information sharing protocol. It estimates the emission probabilities of the pHMM modeling a particular subfamily not only based on the nucleotide frequencies of the respective subfamily but also incorporating the nucleotide frequencies of all available subfamilies. To this end, the underlying probabilistic model mimics the pattern of commonality and variation between the subtypes with regards to the biological characteristics of HI viruses. In order to implement the proposed protocol, we make use of an existing HMM architecture and its associated inference engine.

Conclusions: We apply the modified algorithm to classify HIV-1 sequence data in the form of partial HIV-1 sequences and semi-artificial recombinants. Thereby, we demonstrate that the performance of pHMMs can be significantly improved by the proposed technique. Moreover, we show that our algorithm performs significantly better than Simplot and Bootscanning.

Show MeSH
Related in: MedlinePlus