Parra Genis, Bradnam Keith, Korf Ian
UC Davis Genome Center, University of California Davis, Davis, CA 95616, USA.
Bioinformatics. 2007 May 1;23(9):1061-7. doi: 10.1093/bioinformatics/btm071. Epub 2007 Mar 1.
The numbers of finished and ongoing genome projects are increasing at a rapid rate, and providing the catalog of genes for these new genomes is a key challenge. Obtaining a set of well-characterized genes is a basic requirement in the initial steps of any genome annotation process. An accurate set of genes is needed in order to learn about species-specific properties, to train gene-finding programs, and to validate automatic predictions. Unfortunately, many new genome projects lack comprehensive experimental data to derive a reliable initial set of genes.
In this study, we report a computational method, CEGMA (Core Eukaryotic Genes Mapping Approach), for building a highly reliable set of gene annotations in the absence of experimental data. We define a set of conserved protein families that occur in a wide range of eukaryotes, and present a mapping procedure that accurately identifies their exon-intron structures in a novel genomic sequence. CEGMA includes the use of profile-hidden Markov models to ensure the reliability of the gene structures. Our procedure allows one to build an initial set of reliable gene annotations in potentially any eukaryotic genome, even those in draft stages.
Software and data sets are available online at http://korflab.ucdavis.edu/Datasets.
已完成和正在进行的基因组计划数量正在迅速增加，为这些新基因组提供基因目录是一项关键挑战。获得一组特征明确的基因是任何基因组注释过程初始步骤的基本要求。需要一组准确的基因来了解物种特异性特征、训练基因发现程序以及验证自动预测结果。不幸的是，许多新的基因组计划缺乏全面的实验数据来推导可靠的初始基因集。
在本研究中，我们报告了一种计算方法CEGMA（核心真核基因定位方法），用于在缺乏实验数据的情况下构建高度可靠的基因注释集。我们定义了一组存在于多种真核生物中的保守蛋白家族，并提出了一种定位程序，可在新的基因组序列中准确识别它们的外显子-内含子结构。CEGMA包括使用轮廓隐马尔可夫模型来确保基因结构的可靠性。我们的程序允许在潜在的任何真核基因组中构建一组可靠的初始基因注释，甚至是处于草图阶段的基因组。
