An Evolutionary Model-Based Algorithm for Accurate Phylogenetic Breakpoint Mapping and Subtype Prediction in HIV-1
Genetically diverse pathogens (such as Human Immunodeficiency virus type 1, HIV-1) are frequently stratified into phylogenetically or immunologically defined subtypes for classification purposes. Computational identification of such subtypes is helpful in surveillance, epidemiological analysis and d...
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| Published in | PLoS computational biology Vol. 5; no. 11; p. e1000581 |
|---|---|
| Main Authors | , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
01.11.2009
Public Library of Science (PLoS) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1553-7358 1553-734X 1553-7358 |
| DOI | 10.1371/journal.pcbi.1000581 |
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| Abstract | Genetically diverse pathogens (such as Human Immunodeficiency virus type 1, HIV-1) are frequently stratified into phylogenetically or immunologically defined subtypes for classification purposes. Computational identification of such subtypes is helpful in surveillance, epidemiological analysis and detection of novel variants, e.g., circulating recombinant forms in HIV-1. A number of conceptually and technically different techniques have been proposed for determining the subtype of a query sequence, but there is not a universally optimal approach. We present a model-based phylogenetic method for automatically subtyping an HIV-1 (or other viral or bacterial) sequence, mapping the location of breakpoints and assigning parental sequences in recombinant strains as well as computing confidence levels for the inferred quantities. Our Subtype Classification Using Evolutionary ALgorithms (SCUEAL) procedure is shown to perform very well in a variety of simulation scenarios, runs in parallel when multiple sequences are being screened, and matches or exceeds the performance of existing approaches on typical empirical cases. We applied SCUEAL to all available polymerase (pol) sequences from two large databases, the Stanford Drug Resistance database and the UK HIV Drug Resistance Database. Comparing with subtypes which had previously been assigned revealed that a minor but substantial (approximately 5%) fraction of pure subtype sequences may in fact be within- or inter-subtype recombinants. A free implementation of SCUEAL is provided as a module for the HyPhy package and the Datamonkey web server. Our method is especially useful when an accurate automatic classification of an unknown strain is desired, and is positioned to complement and extend faster but less accurate methods. Given the increasingly frequent use of HIV subtype information in studies focusing on the effect of subtype on treatment, clinical outcome, pathogenicity and vaccine design, the importance of accurate, robust and extensible subtyping procedures is clear. |
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| AbstractList | Genetically diverse pathogens (such as Human Immunodeficiency virus type 1, HIV-1) are frequently stratified into phylogenetically or immunologically defined subtypes for classification purposes. Computational identification of such subtypes is helpful in surveillance, epidemiological analysis and detection of novel variants, e.g., circulating recombinant forms in HIV-1. A number of conceptually and technically different techniques have been proposed for determining the subtype of a query sequence, but there is not a universally optimal approach. We present a model-based phylogenetic method for automatically subtyping an HIV-1 (or other viral or bacterial) sequence, mapping the location of breakpoints and assigning parental sequences in recombinant strains as well as computing confidence levels for the inferred quantities. Our Subtype Classification Using Evolutionary ALgorithms (SCUEAL) procedure is shown to perform very well in a variety of simulation scenarios, runs in parallel when multiple sequences are being screened, and matches or exceeds the performance of existing approaches on typical empirical cases. We applied SCUEAL to all available polymerase (pol) sequences from two large databases, the Stanford Drug Resistance database and the UK HIV Drug Resistance Database. Comparing with subtypes which had previously been assigned revealed that a minor but substantial (≈5%) fraction of pure subtype sequences may in fact be within- or inter-subtype recombinants. A free implementation of SCUEAL is provided as a module for the HyPhy package and the Datamonkey web server. Our method is especially useful when an accurate automatic classification of an unknown strain is desired, and is positioned to complement and extend faster but less accurate methods. Given the increasingly frequent use of HIV subtype information in studies focusing on the effect of subtype on treatment, clinical outcome, pathogenicity and vaccine design, the importance of accurate, robust and extensible subtyping procedures is clear. There are nine different subtypes of the main group of HIV-1, each originating as a distinct subepidemic of HIV-1. The distribution of subtypes is often unique to a given geographic region of the world and constitutes a useful epidemiological and surveillance resource. The effects of viral subtype on disease progression, treatment outcome and vaccine design are being actively researched, and the importance of accurate subtyping procedures is clear. In HIV-1, subtype assignment is complicated by frequent recombination among co-circulating strains, creating new genetic mosaics or recombinant forms: 43 have been characterized to date, and many more likely exist. We present an automated phylogenetic method (SCUEAL) to accurately characterize both simple and complex HIV-1 mosaics. Using computer simulations and biological data we demonstrate that SCUEAL performs very well under various conditions, especially when some of the existing classification procedures fail. Furthermore, we show that a small, but noticeable proportion of subtype characterization stored in public databases may be incomplete or incorrect. The computational technique introduced here should provide a much more accurate characterization of HIV-1 strains, especially novel recombinants, and lead to new insights into molecular history, epidemiology and geographical distribution of the virus. Genetically diverse pathogens (such as Human Immunodeficiency virus type 1, HIV-1) are frequently stratified into phylogenetically or immunologically defined subtypes for classification purposes. Computational identification of such subtypes is helpful in surveillance, epidemiological analysis and detection of novel variants, e.g., circulating recombinant forms in HIV-1. A number of conceptually and technically different techniques have been proposed for determining the subtype of a query sequence, but there is not a universally optimal approach. We present a model-based phylogenetic method for automatically subtyping an HIV-1 (or other viral or bacterial) sequence, mapping the location of breakpoints and assigning parental sequences in recombinant strains as well as computing confidence levels for the inferred quantities. Our Subtype Classification Using Evolutionary ALgorithms (SCUEAL) procedure is shown to perform very well in a variety of simulation scenarios, runs in parallel when multiple sequences are being screened, and matches or exceeds the performance of existing approaches on typical empirical cases. We applied SCUEAL to all available polymerase (pol) sequences from two large databases, the Stanford Drug Resistance database and the UK HIV Drug Resistance Database. Comparing with subtypes which had previously been assigned revealed that a minor but substantial (approximately 5%) fraction of pure subtype sequences may in fact be within- or inter-subtype recombinants. A free implementation of SCUEAL is provided as a module for the HyPhy package and the Datamonkey web server. Our method is especially useful when an accurate automatic classification of an unknown strain is desired, and is positioned to complement and extend faster but less accurate methods. Given the increasingly frequent use of HIV subtype information in studies focusing on the effect of subtype on treatment, clinical outcome, pathogenicity and vaccine design, the importance of accurate, robust and extensible subtyping procedures is clear. Genetically diverse pathogens (such as Human Immunodeficiency virus type 1, HIV-1) are frequently stratified into phylogenetically or immunologically defined subtypes for classification purposes. Computational identification of such subtypes is helpful in surveillance, epidemiological analysis and detection of novel variants, e.g., circulating recombinant forms in HIV-1. A number of conceptually and technically different techniques have been proposed for determining the subtype of a query sequence, but there is not a universally optimal approach. We present a model-based phylogenetic method for automatically subtyping an HIV-1 (or other viral or bacterial) sequence, mapping the location of breakpoints and assigning parental sequences in recombinant strains as well as computing confidence levels for the inferred quantities. Our Subtype Classification Using Evolutionary ALgorithms (SCUEAL) procedure is shown to perform very well in a variety of simulation scenarios, runs in parallel when multiple sequences are being screened, and matches or exceeds the performance of existing approaches on typical empirical cases. We applied SCUEAL to all available polymerase (pol) sequences from two large databases, the Stanford Drug Resistance database and the UK HIV Drug Resistance Database. Comparing with subtypes which had previously been assigned revealed that a minor but substantial (a5%) fraction of pure subtype sequences may in fact be within- or inter-subtype recombinants. A free implementation of SCUEAL is provided as a module for the HyPhy package and the Datamonkey web server. Our method is especially useful when an accurate automatic classification of an unknown strain is desired, and is positioned to complement and extend faster but less accurate methods. Given the increasingly frequent use of HIV subtype information in studies focusing on the effect of subtype on treatment, clinical outcome, pathogenicity and vaccine design, the importance of accurate, robust and extensible subtyping procedures is clear. Author Summary There are nine different subtypes of the main group of HIV-1, each originating as a distinct subepidemic of HIV-1. The distribution of subtypes is often unique to a given geographic region of the world and constitutes a useful epidemiological and surveillance resource. The effects of viral subtype on disease progression, treatment outcome and vaccine design are being actively researched, and the importance of accurate subtyping procedures is clear. In HIV-1, subtype assignment is complicated by frequent recombination among co-circulating strains, creating new genetic mosaics or recombinant forms: 43 have been characterized to date, and many more likely exist. We present an automated phylogenetic method (SCUEAL) to accurately characterize both simple and complex HIV-1 mosaics. Using computer simulations and biological data we demonstrate that SCUEAL performs very well under various conditions, especially when some of the existing classification procedures fail. Furthermore, we show that a small, but noticeable proportion of subtype characterization stored in public databases may be incomplete or incorrect. The computational technique introduced here should provide a much more accurate characterization of HIV-1 strains, especially novel recombinants, and lead to new insights into molecular history, epidemiology and geographical distribution of the virus. Genetically diverse pathogens (such as Human Immunodeficiency virus type 1, HIV-1) are frequently stratified into phylogenetically or immunologically defined subtypes for classification purposes. Computational identification of such subtypes is helpful in surveillance, epidemiological analysis and detection of novel variants, e.g., circulating recombinant forms in HIV-1. A number of conceptually and technically different techniques have been proposed for determining the subtype of a query sequence, but there is not a universally optimal approach. We present a model-based phylogenetic method for automatically subtyping an HIV-1 (or other viral or bacterial) sequence, mapping the location of breakpoints and assigning parental sequences in recombinant strains as well as computing confidence levels for the inferred quantities. Our Subtype Classification Using Evolutionary ALgorithms (SCUEAL) procedure is shown to perform very well in a variety of simulation scenarios, runs in parallel when multiple sequences are being screened, and matches or exceeds the performance of existing approaches on typical empirical cases. We applied SCUEAL to all available polymerase (pol) sequences from two large databases, the Stanford Drug Resistance database and the UK HIV Drug Resistance Database. Comparing with subtypes which had previously been assigned revealed that a minor but substantial ([approximate]5%) fraction of pure subtype sequences may in fact be within- or inter-subtype recombinants. A free implementation of SCUEAL is provided as a module for the HyPhy package and the Datamonkey web server. Our method is especially useful when an accurate automatic classification of an unknown strain is desired, and is positioned to complement and extend faster but less accurate methods. Given the increasingly frequent use of HIV subtype information in studies focusing on the effect of subtype on treatment, clinical outcome, pathogenicity and vaccine design, the importance of accurate, robust and extensible subtyping procedures is clear. Genetically diverse pathogens (such as Human Immunodeficiency virus type 1, HIV-1) are frequently stratified into phylogenetically or immunologically defined subtypes for classification purposes. Computational identification of such subtypes is helpful in surveillance, epidemiological analysis and detection of novel variants, e.g., circulating recombinant forms in HIV-1. A number of conceptually and technically different techniques have been proposed for determining the subtype of a query sequence, but there is not a universally optimal approach. We present a model-based phylogenetic method for automatically subtyping an HIV-1 (or other viral or bacterial) sequence, mapping the location of breakpoints and assigning parental sequences in recombinant strains as well as computing confidence levels for the inferred quantities. Our Subtype Classification Using Evolutionary Algorithms (SCUEAL) procedure is shown to perform very well in a variety of simulation scenarios, runs in parallel when multiple sequences are being screened, and matches or exceeds the performance of existing approaches on typical empirical cases. We applied SCUEAL to all available polymerase (pol) sequences from two large databases, the Stanford Drug Resistance database and the UK HIV Drug Resistance Database. Comparing with subtypes which had previously been assigned revealed that a minor but substantial (≅5%) fraction of pure subtype sequences may in fact be within- or inter-subtype recombinants. A free implementation of SCUEAL is provided as a module for the HyPhy package and the Datamonkey web server. Our method is especially useful when an accurate automatic classification of an unknown strain is desired, and is positioned to complement and extend faster but less accurate methods. Given the increasingly frequent use of HIV subtype information in studies focusing on the effect of subtype on treatment, clinical outcome, pathogenicity and vaccine design, the importance of accurate, robust and extensible subtyping procedures is clear. Genetically diverse pathogens (such as Human Immunodeficiency virus type 1, HIV-1) are frequently stratified into phylogenetically or immunologically defined subtypes for classification purposes. Computational identification of such subtypes is helpful in surveillance, epidemiological analysis and detection of novel variants, e.g., circulating recombinant forms in HIV-1. A number of conceptually and technically different techniques have been proposed for determining the subtype of a query sequence, but there is not a universally optimal approach. We present a model-based phylogenetic method for automatically subtyping an HIV-1 (or other viral or bacterial) sequence, mapping the location of breakpoints and assigning parental sequences in recombinant strains as well as computing confidence levels for the inferred quantities. Our Subtype Classification Using Evolutionary ALgorithms (SCUEAL) procedure is shown to perform very well in a variety of simulation scenarios, runs in parallel when multiple sequences are being screened, and matches or exceeds the performance of existing approaches on typical empirical cases. We applied SCUEAL to all available polymerase (pol) sequences from two large databases, the Stanford Drug Resistance database and the UK HIV Drug Resistance Database. Comparing with subtypes which had previously been assigned revealed that a minor but substantial (approximately 5%) fraction of pure subtype sequences may in fact be within- or inter-subtype recombinants. A free implementation of SCUEAL is provided as a module for the HyPhy package and the Datamonkey web server. Our method is especially useful when an accurate automatic classification of an unknown strain is desired, and is positioned to complement and extend faster but less accurate methods. Given the increasingly frequent use of HIV subtype information in studies focusing on the effect of subtype on treatment, clinical outcome, pathogenicity and vaccine design, the importance of accurate, robust and extensible subtyping procedures is clear.Genetically diverse pathogens (such as Human Immunodeficiency virus type 1, HIV-1) are frequently stratified into phylogenetically or immunologically defined subtypes for classification purposes. Computational identification of such subtypes is helpful in surveillance, epidemiological analysis and detection of novel variants, e.g., circulating recombinant forms in HIV-1. A number of conceptually and technically different techniques have been proposed for determining the subtype of a query sequence, but there is not a universally optimal approach. We present a model-based phylogenetic method for automatically subtyping an HIV-1 (or other viral or bacterial) sequence, mapping the location of breakpoints and assigning parental sequences in recombinant strains as well as computing confidence levels for the inferred quantities. Our Subtype Classification Using Evolutionary ALgorithms (SCUEAL) procedure is shown to perform very well in a variety of simulation scenarios, runs in parallel when multiple sequences are being screened, and matches or exceeds the performance of existing approaches on typical empirical cases. We applied SCUEAL to all available polymerase (pol) sequences from two large databases, the Stanford Drug Resistance database and the UK HIV Drug Resistance Database. Comparing with subtypes which had previously been assigned revealed that a minor but substantial (approximately 5%) fraction of pure subtype sequences may in fact be within- or inter-subtype recombinants. A free implementation of SCUEAL is provided as a module for the HyPhy package and the Datamonkey web server. Our method is especially useful when an accurate automatic classification of an unknown strain is desired, and is positioned to complement and extend faster but less accurate methods. Given the increasingly frequent use of HIV subtype information in studies focusing on the effect of subtype on treatment, clinical outcome, pathogenicity and vaccine design, the importance of accurate, robust and extensible subtyping procedures is clear. |
| Audience | Academic |
| Author | Leigh Brown, Andrew J. Kosakovsky Pond, Sergei L. Stawiski, Eric Chappey, Colombe Frost, Simon D.W. Fearnhill, Esther Posada, David Hughes, Gareth Poon, Art F.Y. Gravenor, Mike B. |
| AuthorAffiliation | Imperial College London, United Kingdom 4 Department of Pathology, University of California San Diego, La Jolla, California, United States of America 7 School of Medicine, University of Swansea, Swansea, United Kingdom 8 Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, United Kingdom 1 Department of Medicine, University of California San Diego, La Jolla, California, United States of America 5 Health Protection Agency East of England Regional Epidemiology Unit, Cambridge, United Kingdom 9 Department of Veterinary Medicine, University of Cambridge, Cambridge, United Kingdom 3 Monogram Biosciences, South San Francisco, California, United States of America 6 Medical Research Council Clinical Trials Unit, London, United Kingdom 2 Department of Biochemistry, Genetics and Immunology, University of Vigo, Vigo, Spain |
| AuthorAffiliation_xml | – name: 2 Department of Biochemistry, Genetics and Immunology, University of Vigo, Vigo, Spain – name: 9 Department of Veterinary Medicine, University of Cambridge, Cambridge, United Kingdom – name: 1 Department of Medicine, University of California San Diego, La Jolla, California, United States of America – name: 8 Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, United Kingdom – name: 6 Medical Research Council Clinical Trials Unit, London, United Kingdom – name: Imperial College London, United Kingdom – name: 4 Department of Pathology, University of California San Diego, La Jolla, California, United States of America – name: 3 Monogram Biosciences, South San Francisco, California, United States of America – name: 5 Health Protection Agency East of England Regional Epidemiology Unit, Cambridge, United Kingdom – name: 7 School of Medicine, University of Swansea, Swansea, United Kingdom |
| Author_xml | – sequence: 1 givenname: Sergei L. surname: Kosakovsky Pond fullname: Kosakovsky Pond, Sergei L. – sequence: 2 givenname: David surname: Posada fullname: Posada, David – sequence: 3 givenname: Eric surname: Stawiski fullname: Stawiski, Eric – sequence: 4 givenname: Colombe surname: Chappey fullname: Chappey, Colombe – sequence: 5 givenname: Art F.Y. surname: Poon fullname: Poon, Art F.Y. – sequence: 6 givenname: Gareth surname: Hughes fullname: Hughes, Gareth – sequence: 7 givenname: Esther surname: Fearnhill fullname: Fearnhill, Esther – sequence: 8 givenname: Mike B. surname: Gravenor fullname: Gravenor, Mike B. – sequence: 9 givenname: Andrew J. surname: Leigh Brown fullname: Leigh Brown, Andrew J. – sequence: 10 givenname: Simon D.W. surname: Frost fullname: Frost, Simon D.W. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/19956739$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | COPYRIGHT 2009 Public Library of Science Kosakovsky Pond et al. 2009 2009 Kosakovsky Pond et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Kosakovsky Pond SL, Posada D, Stawiski E, Chappey C, Poon AF, et al. (2009) An Evolutionary Model-Based Algorithm for Accurate Phylogenetic Breakpoint Mapping and Subtype Prediction in HIV-1. PLoS Comput Biol 5(11): e1000581. doi:10.1371/journal.pcbi.1000581 |
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| Keywords | Biological Evolution HIV-1 Genome, Viral Algorithms Computer Simulation Chromosome Mapping Models, Genetic Software Phylogeny |
| Language | English |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Conceived and designed the experiments: SLKP DP AJLB SDWF. Performed the experiments: SLKP. Analyzed the data: SLKP DP ES GH. Contributed reagents/materials/analysis tools: SLKP DP ES CC AFYP GH EF MBG SDWF. Wrote the paper: SLKP DP AFYP SDWF. |
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| Snippet | Genetically diverse pathogens (such as Human Immunodeficiency virus type 1, HIV-1) are frequently stratified into phylogenetically or immunologically defined... Genetically diverse pathogens (such as Human Immunodeficiency virus type 1, HIV-1) are frequently stratified into phylogenetically or immunologically defined... |
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| SubjectTerms | Acquired immune deficiency syndrome AIDS Algorithms Anti-HIV agents Antiviral agents Automation Biological Evolution Chromosome Mapping Classification Computational Biology/Comparative Sequence Analysis Computational Biology/Evolutionary Modeling Computer Science/Applications Computer Simulation Drug resistance Epidemiology Genome, Viral - genetics HIV HIV (Viruses) HIV-1 - classification HIV-1 - genetics Human immunodeficiency virus Human immunodeficiency virus 1 Mathematics/Statistics Models, Genetic Phylogenetics Phylogeny Software Studies Vaccines Virology/Immunodeficiency Viruses |
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| Title | An Evolutionary Model-Based Algorithm for Accurate Phylogenetic Breakpoint Mapping and Subtype Prediction in HIV-1 |
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