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dc.creatorHepler, NL
dc.creatorScheffler, K
dc.creatorWeaver, S
dc.creatorMurrell, B
dc.creatorRichman, DD
dc.creatorBurton, DR
dc.creatorPoignard, P
dc.creatorSmith, DM
dc.creatorKosakovsky Pond, SL
dc.date.accessioned2021-01-31T17:49:45Z
dc.date.available2021-01-31T17:49:45Z
dc.date.issued2014-01-01
dc.identifier.issn1553-734X
dc.identifier.issn1553-7358
dc.identifier.doihttp://dx.doi.org/10.34944/dspace/5315
dc.identifier.other25254639 (pubmed)
dc.identifier.urihttp://hdl.handle.net/20.500.12613/5333
dc.description.abstract© 2014 Hepler et al. Since its identification in 1983, HIV-1 has been the focus of a research effort unprecedented in scope and difficulty, whose ultimate goals — a cure and a vaccine – remain elusive. One of the fundamental challenges in accomplishing these goals is the tremendous genetic variability of the virus, with some genes differing at as many as 40% of nucleotide positions among circulating strains. Because of this, the genetic bases of many viral phenotypes, most notably the susceptibility to neutralization by a particular antibody, are difficult to identify computationally. Drawing upon open-source general-purpose machine learning algorithms and libraries, we have developed a software package IDEPI (IDentify EPItopes) for learning genotype-to-phenotype predictive models from sequences with known phenotypes. IDEPI can apply learned models to classify sequences of unknown phenotypes, and also identify specific sequence features which contribute to a particular phenotype. We demonstrate that IDEPI achieves performance similar to or better than that of previously published approaches on four well-studied problems: finding the epitopes of broadly neutralizing antibodies (bNab), determining coreceptor tropism of the virus, identifying compartment-specific genetic signatures of the virus, and deducing drug-resistance associated mutations. The cross-platform Python source code (released under the GPL 3.0 license), documentation, issue tracking, and a pre-configured virtual machine for IDEPI can be found at https://github.com/veg/idepi.
dc.format.extente1003842-e1003842
dc.language.isoen
dc.relation.haspartPLoS Computational Biology
dc.relation.isreferencedbyPublic Library of Science (PLoS)
dc.rightsCC BY
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectAIDS Dementia Complex
dc.subjectAlgorithms
dc.subjectAntibodies, Neutralizing
dc.subjectComputational Biology
dc.subjectDrug Resistance, Viral
dc.subjectEpitopes
dc.subjectHIV Antibodies
dc.subjectHIV Infections
dc.subjectHIV-1
dc.subjectHuman Immunodeficiency Virus Proteins
dc.subjectHumans
dc.subjectMachine Learning
dc.subjectPhenotype
dc.subjectSequence Analysis, Protein
dc.subjectSoftware
dc.titleIDEPI: Rapid Prediction of HIV-1 Antibody Epitopes and Other Phenotypic Features from Sequence Data Using a Flexible Machine Learning Platform
dc.typeArticle
dc.type.genreJournal Article
dc.relation.doi10.1371/journal.pcbi.1003842
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.updated2021-01-31T17:49:42Z
refterms.dateFOA2021-01-31T17:49:46Z


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