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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 influence of the degree of conservation on the performance of jpHMM prob. The performance of jpHMM prob (measured in stot) on semi-artificial recombinants against the pairwise percentage identity of the sequences in the genome region from which the respective semi-artificial recombinant was taken. The LOESS curve together with its standard deviation (dashed) and the double of the standard deviation (dotted) is given as red line.
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Figure 11: The influence of the degree of conservation on the performance of jpHMM prob. The performance of jpHMM prob (measured in stot) on semi-artificial recombinants against the pairwise percentage identity of the sequences in the genome region from which the respective semi-artificial recombinant was taken. The LOESS curve together with its standard deviation (dashed) and the double of the standard deviation (dotted) is given as red line.

Mentions: In order to measure the influence of the variability of the genome part from which a sequence stems on the performance of jpHMM prob, we evaluate the performance of jpHMM prob on semi-artificial recombinants. Since the results from pure sequences are less conclusive, we restrict our evaluation to recombinants. As Trec is too small to achieve significant results in this regards, we employ an extended version of Trec. Instead of 10 sequences per subtype pair we use 50. We measure the degree of conservation by the pairwise percentage identity of all sequences from our input MSA in the genome region from which the respective semi-artificial recombinant was taken. The results are shown in Figure 11.


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 influence of the degree of conservation on the performance of jpHMM prob. The performance of jpHMM prob (measured in stot) on semi-artificial recombinants against the pairwise percentage identity of the sequences in the genome region from which the respective semi-artificial recombinant was taken. The LOESS curve together with its standard deviation (dashed) and the double of the standard deviation (dotted) is given as red line.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 11: The influence of the degree of conservation on the performance of jpHMM prob. The performance of jpHMM prob (measured in stot) on semi-artificial recombinants against the pairwise percentage identity of the sequences in the genome region from which the respective semi-artificial recombinant was taken. The LOESS curve together with its standard deviation (dashed) and the double of the standard deviation (dotted) is given as red line.
Mentions: In order to measure the influence of the variability of the genome part from which a sequence stems on the performance of jpHMM prob, we evaluate the performance of jpHMM prob on semi-artificial recombinants. Since the results from pure sequences are less conclusive, we restrict our evaluation to recombinants. As Trec is too small to achieve significant results in this regards, we employ an extended version of Trec. Instead of 10 sequences per subtype pair we use 50. We measure the degree of conservation by the pairwise percentage identity of all sequences from our input MSA in the genome region from which the respective semi-artificial recombinant was taken. The results are shown in Figure 11.

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