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dc.creatorNickle, DC
dc.creatorHeath, L
dc.creatorJensen, MA
dc.creatorGilbert, PB
dc.creatorMullins, JI
dc.creatorKosakovsky Pond, SL
dc.date.accessioned2021-02-01T22:09:18Z
dc.date.available2021-02-01T22:09:18Z
dc.date.issued2007-12-01
dc.identifier.issn1932-6203
dc.identifier.issn1932-6203
dc.identifier.doihttp://dx.doi.org/10.34944/dspace/5602
dc.identifier.other17551583 (pubmed)
dc.identifier.urihttp://hdl.handle.net/20.500.12613/5620
dc.description.abstractComparative sequence analyses, including such fundamental bioinformatics techniques as similarity searching, sequence alignment and phylogenetic inference, have become a mainstay for researchers studying type 1 Human Immunodeficiency Virus (HIV-1) genome structure and evolution. Implicit in comparative analyses is an underlying model of evolution, and the chosen model can significantly affect the results. In general, evolutionary models describe the probabilities of replacing one amino acid character with another over a period of time. Most widely used evolutionary models for protein sequences have been derived from curated alignments of hundreds of proteins, usually based on mammalian genomes. It is unclear to what extent these empirical models are generalizable to a very different organism, such as HIV-1-the most extensively sequenced organism in existence. We developed a maximum likelihood model fitting procedure to a collection of HIV-1 alignments sampled from different viral genes, and inferred two empirical substitution models, suitable for describing between-and within-host evolution. Our procedure pools the information from multiple sequence alignments, and provided software implementation can be run efficiently in parallel on a computer cluster. We describe how the inferred substitution models can be used to generate scoring matrices suitable for alignment and similarity searches. Our models had a consistently superior fit relative to the best existing models and to parameter-rich data-driven models when benchmarked on independent HIV-1 alignments, demonstrating evolutionary biases in amino-acid substitution that are unique to HIV, and that are not captured by the existing models. The scoring matrices derived from the models showed a marked difference from common amino-acid scoring matrices. The use of an appropriate evolutionary model recovered a known viral transmission history, whereas a poorly chosen model introduced phylogenetic error. We argue that our model derivation procedure is immediately applicable to other organisms with extensive sequence data available, such as Hepatitis C and Influenza A viruses. © 2007 Nickle et al.
dc.format.extente503-e503
dc.language.isoen
dc.relation.haspartPLoS ONE
dc.relation.isreferencedbyPublic Library of Science (PLoS)
dc.rightsCC BY
dc.subjectAmino Acid Substitution
dc.subjectEvolution, Molecular
dc.subjectHuman Immunodeficiency Virus Proteins
dc.subjectHumans
dc.subjectModels, Statistical
dc.subjectPhylogeny
dc.subjectSequence Analysis, Protein
dc.titleHIV-Specific Probabilistic Models of Protein Evolution
dc.typeArticle
dc.type.genreJournal Article
dc.relation.doi10.1371/journal.pone.0000503
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.creator.orcidPond, Sergei L. Kosakovsky|0000-0003-4817-4029
dc.date.updated2021-02-01T22:09:15Z
refterms.dateFOA2021-02-01T22:09:18Z


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