Prodigal: prokaryotic gene recognition and translation initiation site identification.
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.


CONCLUSIONS
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.
Hyatt
Doug
D
Computational Biology and Bioinformatics Group, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA. hyattpd@ornl.gov
Chen
Gwo-Liang
GL
Locascio
Philip F
PF
Land
Miriam L
ML
Larimer
Frank W
FW
Hauser
Loren J
LJ
D000465:Algorithms; D030541:Databases, Genetic; D016680:Genome, Bacterial; D010442:Peptide Chain Initiation, Translational / Q000235:genetics*; D011387:Prokaryotic Cells; D012984:Software