Prodigal is a new gene prediction algorithm for prokaryotic genomes that improves gene structure prediction, translation initiation site recognition, and reduces false positives. Developed by researchers at the Oak Ridge National Laboratory, Prodigal is a fast, open-source program that has been tested against existing gene-finding methods and shown to perform well. It uses a dynamic programming approach to identify genes and translation initiation sites, and incorporates information about codon bias, ribosomal binding sites, and other genomic features to improve accuracy. The algorithm was developed using a set of curated genomes and has been validated on over 100 genomes from GenBank. Prodigal constructs a training set of genes by analyzing the GC frame plot in open reading frames (ORFs) and uses this information to calculate coding scores. It then performs dynamic programming to identify a maximal "tiling path" of genes to train on, and uses this information to improve gene prediction. Prodigal also includes a system for identifying translation initiation sites, using information about ribosomal binding sites and start codon usage. It has been tested against other gene-finding methods, including GeneMarkHMM, Glimmer, EasyGene, and MED, and has shown comparable or better performance in predicting genes and translation initiation sites. The program is available as an open-source tool and can be used for microbial genome annotation. It has been incorporated into several bioinformatics databases and is used by many institutions for genome annotation. Prodigal is particularly effective in predicting genes in high GC content genomes, where existing methods often struggle. It also performs well in identifying translation initiation sites, even in cases where the SD motif is not used. The program is designed to be efficient and easy to use, with a single executable that can be run without any organism-specific parameters. It is also available as a web server at http://compbio.ornl.gov/prodigal/.Prodigal is a new gene prediction algorithm for prokaryotic genomes that improves gene structure prediction, translation initiation site recognition, and reduces false positives. Developed by researchers at the Oak Ridge National Laboratory, Prodigal is a fast, open-source program that has been tested against existing gene-finding methods and shown to perform well. It uses a dynamic programming approach to identify genes and translation initiation sites, and incorporates information about codon bias, ribosomal binding sites, and other genomic features to improve accuracy. The algorithm was developed using a set of curated genomes and has been validated on over 100 genomes from GenBank. Prodigal constructs a training set of genes by analyzing the GC frame plot in open reading frames (ORFs) and uses this information to calculate coding scores. It then performs dynamic programming to identify a maximal "tiling path" of genes to train on, and uses this information to improve gene prediction. Prodigal also includes a system for identifying translation initiation sites, using information about ribosomal binding sites and start codon usage. It has been tested against other gene-finding methods, including GeneMarkHMM, Glimmer, EasyGene, and MED, and has shown comparable or better performance in predicting genes and translation initiation sites. The program is available as an open-source tool and can be used for microbial genome annotation. It has been incorporated into several bioinformatics databases and is used by many institutions for genome annotation. Prodigal is particularly effective in predicting genes in high GC content genomes, where existing methods often struggle. It also performs well in identifying translation initiation sites, even in cases where the SD motif is not used. The program is designed to be efficient and easy to use, with a single executable that can be run without any organism-specific parameters. It is also available as a web server at http://compbio.ornl.gov/prodigal/.