基因预测
假阳性悖论
基因
注释
鉴定(生物学)
基因组
计算机科学
基因注释
翻译(生物学)
计算生物学
DNA微阵列
真核翻译
数据挖掘
遗传学
生物
人工智能
基因表达
信使核糖核酸
植物
作者
Doreene R. Hyatt,Gwo-Liang Chen,Philip LoCascio,Miriam Land,Frank W. Larimer,Loren Hauser
标识
DOI:10.1186/1471-2105-11-119
摘要
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.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.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.
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