%0 Journal Article
%T IDEPI: Rapid Prediction of HIV-1 Antibody Epitopes and Other Phenotypic Features from Sequence Data Using a Flexible Machine Learning Platform
%A Ben Murrell
%A Davey M. Smith
%A Dennis R. Burton
%A Douglas D. Richman
%A Konrad Scheffler
%A N. Lance Hepler
%A Pascal Poignard
%A Sergei L. Kosakovsky Pond
%A Steven Weaver
%J -
%D 2014
%R 10.1371/journal.pcbi.1003842
%X Since its identification in 1983, HIV-1 has been the focus of a research effort unprecedented in scope and difficulty, whose ultimate goals ¡ª a cure and a vaccine ¨C 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
%K Sequence motif analysis
%K HIV-1
%K Machine learning algorithms
%K Sequence alignment
%K Machine learning
%K Sequence databases
%K Multiple alignment calculation
%K Phenotypes
%U https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003842