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普罗迪格:原核基因识别和翻译起始位点鉴定。

Prodigal: prokaryotic gene recognition and translation initiation site identification.

机构信息

Computational Biology and Bioinformatics Group, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.

出版信息

BMC Bioinformatics. 2010 Mar 8;11:119. doi: 10.1186/1471-2105-11-119.

Abstract

BACKGROUND

The quality of automated gene prediction in microbial organisms has improved steadily over the past decade, but there is still room for improvement. Increasing the number of correct identifications, both of genes and of the translation initiation sites for each gene, and reducing the overall number of false positives, are all desirable goals.

RESULTS

With our years of experience in manually curating genomes for the Joint Genome Institute, we developed a new gene prediction algorithm called Prodigal (PROkaryotic DYnamic programming Gene-finding ALgorithm). With Prodigal, we focused specifically on the three goals of improved gene structure prediction, improved translation initiation site recognition, and reduced false positives. We compared the results of Prodigal to existing gene-finding methods to demonstrate that it met each of these objectives.

CONCLUSION

We built a fast, lightweight, open source gene prediction program called Prodigal http://compbio.ornl.gov/prodigal/. Prodigal achieved good results compared to existing methods, and we believe it will be a valuable asset to automated microbial annotation pipelines.

摘要

背景

在过去的十年中,微生物自动基因预测的质量稳步提高,但仍有改进的空间。增加正确识别的基因数量和每个基因的翻译起始位点的数量,同时减少总的假阳性数量,都是理想的目标。

结果

凭借我们在联合基因组研究所(Joint Genome Institute)手动编辑基因组方面的多年经验,我们开发了一种名为 Prodigal(原核生物动态编程基因发现算法)的新基因预测算法。在 Prodigal 中,我们特别关注改进基因结构预测、改进翻译起始位点识别和减少假阳性这三个目标。我们将 Prodigal 的结果与现有的基因发现方法进行了比较,证明它满足了所有这些目标。

结论

我们构建了一个快速、轻量级、开源的基因预测程序,称为 Prodigal(http://compbio.ornl.gov/prodigal/)。Prodigal 与现有方法相比取得了良好的效果,我们相信它将成为自动化微生物注释管道的宝贵资产。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/326d/2848648/27c3260eac2a/1471-2105-11-119-1.jpg

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