Prodigal: prokaryotic gene recognition and translation initiation site identification

TLDR

Automated gene prediction in microbes has improved over the past decade, yet accuracy remains suboptimal. The study seeks to increase correct gene and translation initiation site identifications while reducing false positives. Prodigal, a fast, lightweight, open‑source algorithm built from JGI curation experience, was designed to enhance gene structure prediction, TIS recognition, and reduce false positives, and its performance was benchmarked against existing methods. Prodigal outperformed existing gene‑finding tools, achieving higher accuracy in gene and TIS prediction with fewer false positives, and is poised to improve automated microbial annotation pipelines.

Abstract

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. 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. 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.