Prodigal: Prokaryotic Gene Recognition and Translation Initiation Site Identification

Journal Article · Fri Jan 01 04:00:00 EST 2010 · BMC Bioinformatics

DOI:https://doi.org/10.1186/1471-2105-11-119· OSTI ID:974215

Hyatt, Philip Douglas ^[1]; Chen, Gwo-Liang ^[1]; Larimer, Frank W ^[1]; LoCascio, Philip F ^[1]; Hauser, Loren John ^[1]; Land, Miriam L ^[1]

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1. ORNL

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.

• Research Organization: Oak Ridge National Laboratory (ORNL)
• Sponsoring Organization: SC USDOE - Office of Science (SC)
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 974215
• Journal Information: BMC Bioinformatics, Journal Name: BMC Bioinformatics Journal Issue: 119 Vol. 11
• Country of Publication: United States
• Language: English

Related Subjects

59 BASIC BIOLOGICAL SCIENCES
99 GENERAL AND MISCELLANEOUS
ALGORITHMS
DYNAMIC PROGRAMMING
FORECASTING
GENES
PIPELINES