Hepler N Lance, Scheffler Konrad, Weaver Steven, Murrell Ben, Richman Douglas D, Burton Dennis R, Poignard Pascal, Smith Davey M, Kosakovsky Pond Sergei L
Interdisciplinary Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, California, United States of America.
Department of Medicine, University of California San Diego, La Jolla, California, United States of America.
PLoS Comput Biol. 2014 Sep 25;10(9):e1003842. doi: 10.1371/journal.pcbi.1003842. eCollection 2014 Sep.
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.
自1983年被发现以来，人类免疫缺陷病毒1型（HIV-1）一直是一项范围和难度前所未有的研究工作的重点，其最终目标——治愈方法和疫苗——仍然难以实现。实现这些目标的一个根本挑战是该病毒巨大的基因变异性，在流行毒株中，某些基因在多达40%的核苷酸位置上存在差异。因此，许多病毒表型的遗传基础，最显著的是对特定抗体中和作用的敏感性，很难通过计算来确定。利用开源通用机器学习算法和库，我们开发了一个软件包IDEPI（识别表位），用于从具有已知表型的序列中学习基因型到表型的预测模型。IDEPI可以应用所学模型对未知表型的序列进行分类，还可以识别导致特定表型的特定序列特征。我们证明，在四个经过充分研究的问题上，IDEPI的性能与之前发表的方法相当或更好：寻找广泛中和抗体（bNab）的表位、确定病毒的共受体嗜性、识别病毒的特定区室遗传特征以及推断与耐药性相关的突变。IDEPI的跨平台Python源代码（根据GPL 3.0许可发布）、文档、问题跟踪以及预配置虚拟机可在https://github.com/veg/idepi上找到。
