Fitting hidden Markov models of protein domains to a target species: application to <it>Plasmodium falciparum</it>
<p>Abstract</p> <p>Background</p> <p>Hidden Markov Models (HMMs) are a powerful tool for protein domain identification. The Pfam database notably provides a large collection of HMMs which are widely used for the annotation of proteins in new sequenced organisms. In Pfam...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2012-05-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/13/67 |
| _version_ | 1819137312460636160 |
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| author | Terrapon Nicolas Gascuel Olivier Maréchal Éric Bréhélin Laurent |
| author_facet | Terrapon Nicolas Gascuel Olivier Maréchal Éric Bréhélin Laurent |
| author_sort | Terrapon Nicolas |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Hidden Markov Models (HMMs) are a powerful tool for protein domain identification. The Pfam database notably provides a large collection of HMMs which are widely used for the annotation of proteins in new sequenced organisms. In Pfam, each domain family is represented by a curated multiple sequence alignment from which a profile HMM is built. In spite of their high specificity, HMMs may lack sensitivity when searching for domains in divergent organisms. This is particularly the case for species with a biased amino-acid composition, such as <it>P. falciparum</it>, the main causal agent of human malaria. In this context, fitting HMMs to the specificities of the target proteome can help identify additional domains.</p> <p>Results</p> <p>Using <it>P. falciparum</it> as an example, we compare approaches that have been proposed for this problem, and present two alternative methods. Because previous attempts strongly rely on known domain occurrences in the target species or its close relatives, they mainly improve the detection of domains which belong to already identified families. Our methods learn global correction rules that adjust amino-acid distributions associated with the match states of HMMs. These rules are applied to all match states of the whole HMM library, thus enabling the detection of domains from previously absent families. Additionally, we propose a procedure to estimate the proportion of false positives among the newly discovered domains. Starting with the Pfam standard library, we build several new libraries with the different HMM-fitting approaches. These libraries are first used to detect new domain occurrences with low E-values. Second, by applying the Co-Occurrence Domain Discovery (CODD) procedure we have recently proposed, the libraries are further used to identify likely occurrences among potential domains with higher E-values.</p> <p>Conclusion</p> <p>We show that the new approaches allow identification of several domain families previously absent in the <it>P. falciparum</it> proteome and the Apicomplexa phylum, and identify many domains that are not detected by previous approaches. In terms of the number of new discovered domains, the new approaches outperform the previous ones when no close species are available or when they are used to identify likely occurrences among potential domains with high E-values. All predictions on <it>P. falciparum</it> have been integrated into a dedicated website which pools all known/new annotations of protein domains and functions for this organism. A software implementing the two proposed approaches is available at the same address: <url>http://www.lirmm.fr/∼terrapon/HMMfit/</url></p> |
| first_indexed | 2024-12-22T10:48:52Z |
| format | Article |
| id | doaj.art-2e5ed8fe06914947bfd567ae5c792483 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-22T10:48:52Z |
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| spelling | doaj.art-2e5ed8fe06914947bfd567ae5c7924832022-12-21T18:28:50ZengBMCBMC Bioinformatics1471-21052012-05-011316710.1186/1471-2105-13-67Fitting hidden Markov models of protein domains to a target species: application to <it>Plasmodium falciparum</it>Terrapon NicolasGascuel OlivierMaréchal ÉricBréhélin Laurent<p>Abstract</p> <p>Background</p> <p>Hidden Markov Models (HMMs) are a powerful tool for protein domain identification. The Pfam database notably provides a large collection of HMMs which are widely used for the annotation of proteins in new sequenced organisms. In Pfam, each domain family is represented by a curated multiple sequence alignment from which a profile HMM is built. In spite of their high specificity, HMMs may lack sensitivity when searching for domains in divergent organisms. This is particularly the case for species with a biased amino-acid composition, such as <it>P. falciparum</it>, the main causal agent of human malaria. In this context, fitting HMMs to the specificities of the target proteome can help identify additional domains.</p> <p>Results</p> <p>Using <it>P. falciparum</it> as an example, we compare approaches that have been proposed for this problem, and present two alternative methods. Because previous attempts strongly rely on known domain occurrences in the target species or its close relatives, they mainly improve the detection of domains which belong to already identified families. Our methods learn global correction rules that adjust amino-acid distributions associated with the match states of HMMs. These rules are applied to all match states of the whole HMM library, thus enabling the detection of domains from previously absent families. Additionally, we propose a procedure to estimate the proportion of false positives among the newly discovered domains. Starting with the Pfam standard library, we build several new libraries with the different HMM-fitting approaches. These libraries are first used to detect new domain occurrences with low E-values. Second, by applying the Co-Occurrence Domain Discovery (CODD) procedure we have recently proposed, the libraries are further used to identify likely occurrences among potential domains with higher E-values.</p> <p>Conclusion</p> <p>We show that the new approaches allow identification of several domain families previously absent in the <it>P. falciparum</it> proteome and the Apicomplexa phylum, and identify many domains that are not detected by previous approaches. In terms of the number of new discovered domains, the new approaches outperform the previous ones when no close species are available or when they are used to identify likely occurrences among potential domains with high E-values. All predictions on <it>P. falciparum</it> have been integrated into a dedicated website which pools all known/new annotations of protein domains and functions for this organism. A software implementing the two proposed approaches is available at the same address: <url>http://www.lirmm.fr/∼terrapon/HMMfit/</url></p>http://www.biomedcentral.com/1471-2105/13/67 |
| spellingShingle | Terrapon Nicolas Gascuel Olivier Maréchal Éric Bréhélin Laurent Fitting hidden Markov models of protein domains to a target species: application to <it>Plasmodium falciparum</it> BMC Bioinformatics |
| title | Fitting hidden Markov models of protein domains to a target species: application to <it>Plasmodium falciparum</it> |
| title_full | Fitting hidden Markov models of protein domains to a target species: application to <it>Plasmodium falciparum</it> |
| title_fullStr | Fitting hidden Markov models of protein domains to a target species: application to <it>Plasmodium falciparum</it> |
| title_full_unstemmed | Fitting hidden Markov models of protein domains to a target species: application to <it>Plasmodium falciparum</it> |
| title_short | Fitting hidden Markov models of protein domains to a target species: application to <it>Plasmodium falciparum</it> |
| title_sort | fitting hidden markov models of protein domains to a target species application to it plasmodium falciparum it |
| url | http://www.biomedcentral.com/1471-2105/13/67 |
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