IDEPI: Rapid Prediction of HIV-1 Antibody Epitopes and Other Phenotypic Features from Sequence Data Using a Flexible Machine Learning Platform
dc.creator | Hepler, NL | |
dc.creator | Scheffler, K | |
dc.creator | Weaver, S | |
dc.creator | Murrell, B | |
dc.creator | Richman, DD | |
dc.creator | Burton, DR | |
dc.creator | Poignard, P | |
dc.creator | Smith, DM | |
dc.creator | Kosakovsky Pond, SL | |
dc.date.accessioned | 2021-01-31T17:49:45Z | |
dc.date.available | 2021-01-31T17:49:45Z | |
dc.date.issued | 2014-01-01 | |
dc.identifier.issn | 1553-734X | |
dc.identifier.issn | 1553-7358 | |
dc.identifier.doi | http://dx.doi.org/10.34944/dspace/5315 | |
dc.identifier.other | 25254639 (pubmed) | |
dc.identifier.uri | http://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.extent | e1003842-e1003842 | |
dc.language.iso | en | |
dc.relation.haspart | PLoS Computational Biology | |
dc.relation.isreferencedby | Public Library of Science (PLoS) | |
dc.rights | CC BY | |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
dc.subject | AIDS Dementia Complex | |
dc.subject | Algorithms | |
dc.subject | Antibodies, Neutralizing | |
dc.subject | Computational Biology | |
dc.subject | Drug Resistance, Viral | |
dc.subject | Epitopes | |
dc.subject | HIV Antibodies | |
dc.subject | HIV Infections | |
dc.subject | HIV-1 | |
dc.subject | Human Immunodeficiency Virus Proteins | |
dc.subject | Humans | |
dc.subject | Machine Learning | |
dc.subject | Phenotype | |
dc.subject | Sequence Analysis, Protein | |
dc.subject | Software | |
dc.title | IDEPI: Rapid Prediction of HIV-1 Antibody Epitopes and Other Phenotypic Features from Sequence Data Using a Flexible Machine Learning Platform | |
dc.type | Article | |
dc.type.genre | Journal Article | |
dc.relation.doi | 10.1371/journal.pcbi.1003842 | |
dc.ada.note | For Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu | |
dc.creator.orcid | Pond, Sergei L. Kosakovsky|0000-0003-4817-4029 | |
dc.date.updated | 2021-01-31T17:49:42Z | |
refterms.dateFOA | 2021-01-31T17:49:46Z |