Learning HMMs for nucleotide sequences from amino acid alignments Article Swipe
Carlos Norberto Fischer
,
Cláudia M. A. Carareto
,
Renato Santos
,
Ricardo Cerri
,
Eduardo C. Marques Costa
,
Leander Schietgat
,
Celine Vens
·
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1093/bioinformatics/btv054
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1093/bioinformatics/btv054
Profile hidden Markov models (profile HMMs) are known to efficiently predict whether an amino acid (AA) sequence belongs to a specific protein family. Profile HMMs can also be used to search for protein domains in genome sequences. In this case, HMMs are typically learned from AA sequences and then used to search on the six-frame translation of nucleotide (NT) sequences. However, this approach demands additional processing of the original data and search results. Here, we propose an alternative and more direct method which converts an AA alignment into an NT one, after which an NT-based HMM is trained to be applied directly on a genome. Contact : [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.
Related Topics
Concepts
Hidden Markov model
Computer science
Genome
Computational biology
Translation (biology)
Frame (networking)
Multiple sequence alignment
Sequence alignment
Sequence (biology)
Artificial intelligence
Pattern recognition (psychology)
Genetics
Biology
Peptide sequence
Gene
Telecommunications
Messenger RNA
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/bioinformatics/btv054
- https://academic.oup.com/bioinformatics/article-pdf/31/11/1836/17122185/btv054.pdf
- OA Status
- bronze
- Cited By
- 1
- References
- 10
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2122490547
All OpenAlex metadata
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https://openalex.org/W2122490547Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/bioinformatics/btv054Digital Object Identifier
- Title
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Learning HMMs for nucleotide sequences from amino acid alignmentsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2015Year of publication
- Publication date
-
2015-01-31Full publication date if available
- Authors
-
Carlos Norberto Fischer, Cláudia M. A. Carareto, Renato Santos, Ricardo Cerri, Eduardo C. Marques Costa, Leander Schietgat, Celine VensList of authors in order
- Landing page
-
https://doi.org/10.1093/bioinformatics/btv054Publisher landing page
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https://academic.oup.com/bioinformatics/article-pdf/31/11/1836/17122185/btv054.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
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https://academic.oup.com/bioinformatics/article-pdf/31/11/1836/17122185/btv054.pdfDirect OA link when available
- Concepts
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Hidden Markov model, Computer science, Genome, Computational biology, Translation (biology), Frame (networking), Multiple sequence alignment, Sequence alignment, Sequence (biology), Artificial intelligence, Pattern recognition (psychology), Genetics, Biology, Peptide sequence, Gene, Telecommunications, Messenger RNATop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2016: 1Per-year citation counts (last 5 years)
- References (count)
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10Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| best_oa_location.source.is_in_doaj | True |
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| best_oa_location.source.host_organization_name | Oxford University Press |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310311648 |
| best_oa_location.license | |
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| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
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| primary_location.is_oa | True |
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| primary_location.source.issn | 1367-4803, 1367-4811 |
| primary_location.source.type | journal |
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| primary_location.source.issn_l | 1367-4803 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Bioinformatics |
| primary_location.source.host_organization | https://openalex.org/P4310311648 |
| primary_location.source.host_organization_name | Oxford University Press |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310311648 |
| primary_location.license | |
| primary_location.pdf_url | https://academic.oup.com/bioinformatics/article-pdf/31/11/1836/17122185/btv054.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Bioinformatics |
| primary_location.landing_page_url | https://doi.org/10.1093/bioinformatics/btv054 |
| publication_date | 2015-01-31 |
| publication_year | 2015 |
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| abstract_inverted_index.: | 107 |
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| abstract_inverted_index.AA | 46, 86 |
| abstract_inverted_index.In | 38 |
| abstract_inverted_index.NT | 90 |
| abstract_inverted_index.an | 13, 77, 85, 89, 94 |
| abstract_inverted_index.at | 115 |
| abstract_inverted_index.be | 28, 100 |
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| abstract_inverted_index.on | 53, 103 |
| abstract_inverted_index.to | 9, 19, 30, 51, 99 |
| abstract_inverted_index.we | 75 |
| abstract_inverted_index.HMM | 96 |
| abstract_inverted_index.and | 48, 71, 79 |
| abstract_inverted_index.are | 7, 42, 113 |
| abstract_inverted_index.can | 26 |
| abstract_inverted_index.for | 32 |
| abstract_inverted_index.the | 54, 68 |
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| abstract_inverted_index.(NT) | 59 |
| abstract_inverted_index.HMMs | 25, 41 |
| abstract_inverted_index.acid | 15 |
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| [email protected] | 108 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.57979139 |
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| citation_normalized_percentile.is_in_top_10_percent | False |