Ab initio gene identification in metagenomic sequences.
Ab initio gene identification in metagenomic sequences.
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DOI：
10.1093/nar/gkq275
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发表时间：
2010-07
影响因子：
14.9
通讯作者：
Borodovsky M
中科院分区：
文献类型：
--
作者：
Zhu W;Lomsadze A;Borodovsky M
我们描述了一种算法，基因鉴定的DNA序列衍生自鸟枪法测序的微生物群落。准确的从头算基因预测在短核苷酸序列的匿名来源阻碍了模型参数的不确定性。虽然可以提出几种机器学习方法来绕过这一困难，但一种有效的方法是从进化中形成的依赖关系中估计参数，即蛋白质编码区寡核苷酸频率与基因组核苷酸组成之间的依赖关系。该方法的原始版本于1999年提出，并已用于(i)重建病毒基因组中基因发现所需的密码子频率向量和（ii）初始化自训练基因发现算法的参数。随着新的原核生物基因组的大量出现，通过使用寡核苷酸频率的直接多项式和逻辑近似以及通过分离细菌和古细菌模型来增强原始方法成为可能。这些进步提高了模型重建的准确性，进而提高了基因预测的准确性。我们描述了改进的方法，并评估其准确性已知的原核生物基因组分裂成短序列。此外，我们表明，由于新方法的应用，数千个新基因可以添加到几种人类和小鼠肠道宏基因组的现有注释中。
We describe an algorithm for gene identification in DNA sequences derived from shotgun sequencing of microbial communities. Accurate ab initio gene prediction in a short nucleotide sequence of anonymous origin is hampered by uncertainty in model parameters. While several machine learning approaches could be proposed to bypass this difficulty, one effective method is to estimate parameters from dependencies, formed in evolution, between frequencies of oligonucleotides in protein-coding regions and genome nucleotide composition. Original version of the method was proposed in 1999 and has been used since for (i) reconstructing codon frequency vector needed for gene finding in viral genomes and (ii) initializing parameters of self-training gene finding algorithms. With advent of new prokaryotic genomes en masse it became possible to enhance the original approach by using direct polynomial and logistic approximations of oligonucleotide frequencies, as well as by separating models for bacteria and archaea. These advances have increased the accuracy of model reconstruction and, subsequently, gene prediction. We describe the refined method and assess its accuracy on known prokaryotic genomes split into short sequences. Also, we show that as a result of application of the new method, several thousands of new genes could be added to existing annotations of several human and mouse gut metagenomes.