Schiex Thomas, Gouzy Jérôme, Moisan Annick, de Oliveira Yannick
Unité de Biométrie et Intelligence Artificielle, INRA, CNRS-INRA, 31326, Castanet Tolosan Cedex, France.
Nucleic Acids Res. 2003 Jul 1;31(13):3738-41. doi: 10.1093/nar/gkg610.
We describe FrameD, a program that predicts coding regions in prokaryotic and matured eukaryotic sequences. Initially targeted at gene prediction in bacterial GC rich genomes, the gene model used in FrameD also allows to predict genes in the presence of frameshifts and partially undetermined sequences which makes it also very suitable for gene prediction and frameshift correction in unfinished sequences such as EST and EST cluster sequences. Like recent eukaryotic gene prediction programs, FrameD also includes the ability to take into account protein similarity information both in its prediction and its graphical output. Its performances are evaluated on different bacterial genomes. The web site (http://genopole.toulouse.inra.fr/bioinfo/FrameD/FD) allows direct prediction, sequence correction and translation and the ability to learn new models for new organisms.
我们描述了FrameD,这是一个用于预测原核生物和成熟真核生物序列中编码区域的程序。FrameD最初旨在预测富含GC的细菌基因组中的基因,其使用的基因模型还能够在存在移码和部分未确定序列的情况下预测基因,这使得它也非常适合在未完成的序列(如EST和EST簇序列)中进行基因预测和移码校正。与最近的真核生物基因预测程序一样,FrameD在其预测和图形输出中也具备考虑蛋白质相似性信息的能力。我们在不同的细菌基因组上评估了它的性能。该网站(http://genopole.toulouse.inra.fr/bioinfo/FrameD/FD)允许直接进行预测、序列校正和翻译,以及为新生物体学习新模型的功能。