Generalized Baum-Welch algorithm based on the similarity between sequences.
The profile hidden Markov model (PHMM) is widely used to assign the protein sequences to their respective families. A major limitation of a PHMM is the assumption that given states the observations (amino acids) are independent. To overcome this limitation, the dependency between amino acids in a mu...
| Main Authors: | , , |
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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/PMC3869655?pdf=render |
| _version_ | 1830360371059228672 |
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| author | Vahid Rezaei Hamid Pezeshk Horacio Pérez-Sa'nchez |
| author_facet | Vahid Rezaei Hamid Pezeshk Horacio Pérez-Sa'nchez |
| author_sort | Vahid Rezaei |
| collection | DOAJ |
| description | The profile hidden Markov model (PHMM) is widely used to assign the protein sequences to their respective families. A major limitation of a PHMM is the assumption that given states the observations (amino acids) are independent. To overcome this limitation, the dependency between amino acids in a multiple sequence alignment (MSA) which is the representative of a PHMM can be appended to the PHMM. Due to the fact that with a MSA, the sequences of amino acids are biologically related, the one-by-one dependency between two amino acids can be considered. In other words, based on the MSA, the dependency between an amino acid and its corresponding amino acid located above can be combined with the PHMM. For this purpose, the new emission probability matrix which considers the one-by-one dependencies between amino acids is constructed. The parameters of a PHMM are of two types; transition and emission probabilities which are usually estimated using an EM algorithm called the Baum-Welch algorithm. We have generalized the Baum-Welch algorithm using similarity emission matrix constructed by integrating the new emission probability matrix with the common emission probability matrix. Then, the performance of similarity emission is discussed by applying it to the top twenty protein families in the Pfam database. We show that using the similarity emission in the Baum-Welch algorithm significantly outperforms the common Baum-Welch algorithm in the task of assigning protein sequences to protein families. |
| first_indexed | 2024-12-20T03:15:51Z |
| format | Article |
| id | doaj.art-d05b0dd8a79d4b6fa6ba044deed75192 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-20T03:15:51Z |
| publishDate | 2013-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-d05b0dd8a79d4b6fa6ba044deed751922022-12-21T19:55:21ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-01812e8056510.1371/journal.pone.0080565Generalized Baum-Welch algorithm based on the similarity between sequences.Vahid RezaeiHamid PezeshkHoracio Pérez-Sa'nchezThe profile hidden Markov model (PHMM) is widely used to assign the protein sequences to their respective families. A major limitation of a PHMM is the assumption that given states the observations (amino acids) are independent. To overcome this limitation, the dependency between amino acids in a multiple sequence alignment (MSA) which is the representative of a PHMM can be appended to the PHMM. Due to the fact that with a MSA, the sequences of amino acids are biologically related, the one-by-one dependency between two amino acids can be considered. In other words, based on the MSA, the dependency between an amino acid and its corresponding amino acid located above can be combined with the PHMM. For this purpose, the new emission probability matrix which considers the one-by-one dependencies between amino acids is constructed. The parameters of a PHMM are of two types; transition and emission probabilities which are usually estimated using an EM algorithm called the Baum-Welch algorithm. We have generalized the Baum-Welch algorithm using similarity emission matrix constructed by integrating the new emission probability matrix with the common emission probability matrix. Then, the performance of similarity emission is discussed by applying it to the top twenty protein families in the Pfam database. We show that using the similarity emission in the Baum-Welch algorithm significantly outperforms the common Baum-Welch algorithm in the task of assigning protein sequences to protein families.http://europepmc.org/articles/PMC3869655?pdf=render |
| spellingShingle | Vahid Rezaei Hamid Pezeshk Horacio Pérez-Sa'nchez Generalized Baum-Welch algorithm based on the similarity between sequences. PLoS ONE |
| title | Generalized Baum-Welch algorithm based on the similarity between sequences. |
| title_full | Generalized Baum-Welch algorithm based on the similarity between sequences. |
| title_fullStr | Generalized Baum-Welch algorithm based on the similarity between sequences. |
| title_full_unstemmed | Generalized Baum-Welch algorithm based on the similarity between sequences. |
| title_short | Generalized Baum-Welch algorithm based on the similarity between sequences. |
| title_sort | generalized baum welch algorithm based on the similarity between sequences |
| url | http://europepmc.org/articles/PMC3869655?pdf=render |
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