2021-01-312021-01-312014-01-011553-734X1553-7358http://dx.doi.org/10.34944/dspace/531525254639 (pubmed)http://hdl.handle.net/20.500.12613/5333© 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.e1003842-e1003842enCC BYhttp://creativecommons.org/licenses/by/4.0/AIDS Dementia ComplexAlgorithmsAntibodies, NeutralizingComputational BiologyDrug Resistance, ViralEpitopesHIV AntibodiesHIV InfectionsHIV-1Human Immunodeficiency Virus ProteinsHumansMachine LearningPhenotypeSequence Analysis, ProteinSoftwareArticle2021-01-31