Variable-order sequence modeling improves bacterial strain discrimination for Ion Torrent DNA reads
Abstract Background Genome sequencing provides a powerful tool for pathogen detection and can help resolve outbreaks that pose public safety and health risks. Mapping of DNA reads to genomes plays a fundamental role in this approach, where accurate alignment and classification of sequencing data is...
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| Format: | Article |
| Language: | English |
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BMC
2017-06-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12859-017-1710-0 |
| _version_ | 1818509441548419072 |
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| author | Thomas M. Poulsen Martin Frith |
| author_facet | Thomas M. Poulsen Martin Frith |
| author_sort | Thomas M. Poulsen |
| collection | DOAJ |
| description | Abstract Background Genome sequencing provides a powerful tool for pathogen detection and can help resolve outbreaks that pose public safety and health risks. Mapping of DNA reads to genomes plays a fundamental role in this approach, where accurate alignment and classification of sequencing data is crucial. Standard mapping methods crudely treat bases as independent from their neighbors. Accuracy might be improved by using higher order paired hidden Markov models (HMMs), which model neighbor effects, but introduce design and implementation issues that have typically made them impractical for read mapping applications. We present a variable-order paired HMM that we term VarHMM, which addresses central issues involved with higher order modeling for sequence alignment. Results Compared with existing alignment methods, VarHMM is able to model higher order distributions and quantify alignment probabilities with greater detail and accuracy. In a series of comparison tests, in which Ion Torrent sequenced DNA was mapped to similar bacterial strains, VarHMM consistently provided better strain discrimination than any of the other alignment methods that we compared with. Conclusions Our results demonstrate the advantages of higher ordered probability distribution modeling and also suggest that further development of such models would benefit read mapping in a range of other applications as well. |
| first_indexed | 2024-12-10T22:45:30Z |
| format | Article |
| id | doaj.art-dc52b5a6284d42afbfb4f4145839f6b9 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-10T22:45:30Z |
| publishDate | 2017-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-dc52b5a6284d42afbfb4f4145839f6b92022-12-22T01:30:36ZengBMCBMC Bioinformatics1471-21052017-06-011811910.1186/s12859-017-1710-0Variable-order sequence modeling improves bacterial strain discrimination for Ion Torrent DNA readsThomas M. Poulsen0Martin Frith1Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST)Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST)Abstract Background Genome sequencing provides a powerful tool for pathogen detection and can help resolve outbreaks that pose public safety and health risks. Mapping of DNA reads to genomes plays a fundamental role in this approach, where accurate alignment and classification of sequencing data is crucial. Standard mapping methods crudely treat bases as independent from their neighbors. Accuracy might be improved by using higher order paired hidden Markov models (HMMs), which model neighbor effects, but introduce design and implementation issues that have typically made them impractical for read mapping applications. We present a variable-order paired HMM that we term VarHMM, which addresses central issues involved with higher order modeling for sequence alignment. Results Compared with existing alignment methods, VarHMM is able to model higher order distributions and quantify alignment probabilities with greater detail and accuracy. In a series of comparison tests, in which Ion Torrent sequenced DNA was mapped to similar bacterial strains, VarHMM consistently provided better strain discrimination than any of the other alignment methods that we compared with. Conclusions Our results demonstrate the advantages of higher ordered probability distribution modeling and also suggest that further development of such models would benefit read mapping in a range of other applications as well.http://link.springer.com/article/10.1186/s12859-017-1710-0Sequence alignmentHigher orderHMMIon TorrentPathogen detection |
| spellingShingle | Thomas M. Poulsen Martin Frith Variable-order sequence modeling improves bacterial strain discrimination for Ion Torrent DNA reads BMC Bioinformatics Sequence alignment Higher order HMM Ion Torrent Pathogen detection |
| title | Variable-order sequence modeling improves bacterial strain discrimination for Ion Torrent DNA reads |
| title_full | Variable-order sequence modeling improves bacterial strain discrimination for Ion Torrent DNA reads |
| title_fullStr | Variable-order sequence modeling improves bacterial strain discrimination for Ion Torrent DNA reads |
| title_full_unstemmed | Variable-order sequence modeling improves bacterial strain discrimination for Ion Torrent DNA reads |
| title_short | Variable-order sequence modeling improves bacterial strain discrimination for Ion Torrent DNA reads |
| title_sort | variable order sequence modeling improves bacterial strain discrimination for ion torrent dna reads |
| topic | Sequence alignment Higher order HMM Ion Torrent Pathogen detection |
| url | http://link.springer.com/article/10.1186/s12859-017-1710-0 |
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