Hidden Markov models for evolution and comparative genomics analysis.
The problem of reconstruction of ancestral states given a phylogeny and data from extant species arises in a wide range of biological studies. The continuous-time Markov model for the discrete states evolution is generally used for the reconstruction of ancestral states. We modify this model to acco...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2013-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3676395?pdf=render |
| _version_ | 1818694416919953408 |
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| author | Nadezda A Bykova Alexander V Favorov Andrey A Mironov |
| author_facet | Nadezda A Bykova Alexander V Favorov Andrey A Mironov |
| author_sort | Nadezda A Bykova |
| collection | DOAJ |
| description | The problem of reconstruction of ancestral states given a phylogeny and data from extant species arises in a wide range of biological studies. The continuous-time Markov model for the discrete states evolution is generally used for the reconstruction of ancestral states. We modify this model to account for a case when the states of the extant species are uncertain. This situation appears, for example, if the states for extant species are predicted by some program and thus are known only with some level of reliability; it is common for bioinformatics field. The main idea is formulation of the problem as a hidden Markov model on a tree (tree HMM, tHMM), where the basic continuous-time Markov model is expanded with the introduction of emission probabilities of observed data (e.g. prediction scores) for each underlying discrete state. Our tHMM decoding algorithm allows us to predict states at the ancestral nodes as well as to refine states at the leaves on the basis of quantitative comparative genomics. The test on the simulated data shows that the tHMM approach applied to the continuous variable reflecting the probabilities of the states (i.e. prediction score) appears to be more accurate then the reconstruction from the discrete states assignment defined by the best score threshold. We provide examples of applying our model to the evolutionary analysis of N-terminal signal peptides and transcription factor binding sites in bacteria. The program is freely available at http://bioinf.fbb.msu.ru/~nadya/tHMM and via web-service at http://bioinf.fbb.msu.ru/treehmmweb. |
| first_indexed | 2024-12-17T13:29:14Z |
| format | Article |
| id | doaj.art-c00cf3f04c4848ebb1c51494e3e1cfa7 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-17T13:29:14Z |
| publishDate | 2013-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-c00cf3f04c4848ebb1c51494e3e1cfa72022-12-21T21:46:39ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-0186e6501210.1371/journal.pone.0065012Hidden Markov models for evolution and comparative genomics analysis.Nadezda A BykovaAlexander V FavorovAndrey A MironovThe problem of reconstruction of ancestral states given a phylogeny and data from extant species arises in a wide range of biological studies. The continuous-time Markov model for the discrete states evolution is generally used for the reconstruction of ancestral states. We modify this model to account for a case when the states of the extant species are uncertain. This situation appears, for example, if the states for extant species are predicted by some program and thus are known only with some level of reliability; it is common for bioinformatics field. The main idea is formulation of the problem as a hidden Markov model on a tree (tree HMM, tHMM), where the basic continuous-time Markov model is expanded with the introduction of emission probabilities of observed data (e.g. prediction scores) for each underlying discrete state. Our tHMM decoding algorithm allows us to predict states at the ancestral nodes as well as to refine states at the leaves on the basis of quantitative comparative genomics. The test on the simulated data shows that the tHMM approach applied to the continuous variable reflecting the probabilities of the states (i.e. prediction score) appears to be more accurate then the reconstruction from the discrete states assignment defined by the best score threshold. We provide examples of applying our model to the evolutionary analysis of N-terminal signal peptides and transcription factor binding sites in bacteria. The program is freely available at http://bioinf.fbb.msu.ru/~nadya/tHMM and via web-service at http://bioinf.fbb.msu.ru/treehmmweb.http://europepmc.org/articles/PMC3676395?pdf=render |
| spellingShingle | Nadezda A Bykova Alexander V Favorov Andrey A Mironov Hidden Markov models for evolution and comparative genomics analysis. PLoS ONE |
| title | Hidden Markov models for evolution and comparative genomics analysis. |
| title_full | Hidden Markov models for evolution and comparative genomics analysis. |
| title_fullStr | Hidden Markov models for evolution and comparative genomics analysis. |
| title_full_unstemmed | Hidden Markov models for evolution and comparative genomics analysis. |
| title_short | Hidden Markov models for evolution and comparative genomics analysis. |
| title_sort | hidden markov models for evolution and comparative genomics analysis |
| url | http://europepmc.org/articles/PMC3676395?pdf=render |
| work_keys_str_mv | AT nadezdaabykova hiddenmarkovmodelsforevolutionandcomparativegenomicsanalysis AT alexandervfavorov hiddenmarkovmodelsforevolutionandcomparativegenomicsanalysis AT andreyamironov hiddenmarkovmodelsforevolutionandcomparativegenomicsanalysis |