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Prodigal: prokaryotic gene recognition and translation initiation site identification
Open accessAbstract
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.
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Irvine, J., Martinson, J. N. V., Arora, J., Hasham, S. I., Patel, J. R., Qi, E. T., Cress, B. F., & Rubin, B. E. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf1198 · Cited by 4
Wallau, G. D. L., Barbier, E., Machado, L. C., da Silva, A. F., Dias, Y. J. M., Dezordi, F. Z., Tomazatos, A., Horváth, B., Lins, R. D., Bernard, E., et al. (2025) · Virus Evolution · doi:10.1093/ve/veaf094 · Cited by 2
Kerek, Á., Husz, L. H., Szarka, E., Tornyos, G. Á., & Jerzsele, Á. (2026) · Antibiotics · doi:10.3390/antibiotics15060544
Arnoux, J. (2026) · Thèse de doctorat, Université Paris-Saclay / PPanGGOLiN · doi:10.70675/eee354d9zf63dz48ebz9444zd1809f8fb74d
Staged microbial consortium valorizes cattle manure wastewater and promotes maize growth
Wang, X., Cao, J., Ni, L., Zhao, H., Zhou, L., & Wang, Y. (2026) · Journal of Applied Microbiology · doi:10.1093/jambio/lxag151
Metadata
- Authors
- Hyatt, D., Chen, G.-L., LoCascio, P. F., Land, M. L., Larimer, F. W., & Hauser, L. J.
- Venue
- BMC Bioinformatics
- Published
- 2010-03-08
- Volume
- 11
- Issue
- 1
- Pages
- 119-119
- ISSN
- 1471-2105 (ISSN-L)
- OpenAlex
- W2143485490
- Cited by
- 12,587
- References
- 2
- FWCI
- 63.80
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.