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Prodigal: prokaryotic gene recognition and translation initiation site identification

Doug Hyatt12*, Gwo-Liang Chen1, Philip F LoCascio1, Miriam L Land13, Frank W Larimer12 and Loren J Hauser13

Author Affiliations

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

2 Genome Science and Technology Graduate School, The University of Tennessee, Knoxville, TN 37996, USA

3 DOE Joint Genome Institute, Oak Ridge National Laboratory, Oak Ridge TN 37831, USA

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BMC Bioinformatics 2010, 11:119  doi:10.1186/1471-2105-11-119

Published: 8 March 2010

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/ webcite. Prodigal achieved good results compared to existing methods, and we believe it will be a valuable asset to automated microbial annotation pipelines.