Sjölander Kimmen
Berkeley Phylogenomics Group, Department of Bioengineering, University of California, 473 Evans Hall 1762, Berkeley, CA 94720-1762, USA.
Bioinformatics. 2004 Jan 22;20(2):170-9. doi: 10.1093/bioinformatics/bth021.
Protein families evolve a multiplicity of functions through gene duplication, speciation and other processes. As a number of studies have shown, standard methods of protein function prediction produce systematic errors on these data. Phylogenomic analysis--combining phylogenetic tree construction, integration of experimental data and differentiation of orthologs and paralogs--has been proposed to address these errors and improve the accuracy of functional classification. The explicit integration of structure prediction and analysis in this framework, which we call structural phylogenomics, provides additional insights into protein superfamily evolution.
Results of protein functional classification using phylogenomic analysis show fewer expected false positives overall than when pairwise methods of functional classification are employed. We present an overview of the motivations and fundamental principles of phylogenomic analysis, new methods developed for the key tasks, benchmark datasets for these tasks (when available) and suggest procedures to increase accuracy. We also discuss some of the methods used in the Celera Genomics high-throughput phylogenomic classification of the human genome.
Software tools from the Berkeley Phylogenomics Group are available at http://phylogenomics.berkeley.edu
蛋白质家族通过基因复制、物种形成及其他过程演化出多种功能。正如许多研究表明的那样，蛋白质功能预测的标准方法在这些数据上会产生系统性错误。系统发育基因组学分析——结合系统发育树构建、实验数据整合以及直系同源物和旁系同源物的区分——已被提出以解决这些错误并提高功能分类的准确性。在这个框架中明确整合结构预测和分析，我们称之为结构系统发育基因组学，它为蛋白质超家族的进化提供了更多见解。
使用系统发育基因组学分析进行蛋白质功能分类的结果显示，总体上预期的假阳性比采用成对功能分类方法时要少。我们概述了系统发育基因组学分析的动机和基本原理、为关键任务开发的新方法、这些任务的基准数据集（如果有的话），并提出了提高准确性的程序。我们还讨论了塞雷拉基因组公司在人类基因组高通量系统发育基因组学分类中使用的一些方法。
伯克利系统发育基因组学小组的软件工具可在http://phylogenomics.berkeley.edu获取
