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      Prodigal: prokaryotic gene recognition and translation initiation site identification

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          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/. 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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          Most cited references14

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          Artemis: sequence visualization and annotation

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            GeneMark.hmm: new solutions for gene finding.

            The number of completely sequenced bacterial genomes has been growing fast. There are computer methods available for finding genes but yet there is a need for more accurate algorithms. The GeneMark. hmm algorithm presented here was designed to improve the gene prediction quality in terms of finding exact gene boundaries. The idea was to embed the GeneMark models into naturally derived hidden Markov model framework with gene boundaries modeled as transitions between hidden states. We also used the specially derived ribosome binding site pattern to refine predictions of translation initiation codons. The algorithm was evaluated on several test sets including 10 complete bacterial genomes. It was shown that the new algorithm is significantly more accurate than GeneMark in exact gene prediction. Interestingly, the high gene finding accuracy was observed even in the case when Markov models of order zero, one and two were used. We present the analysis of false positive and false negative predictions with the caution that these categories are not precisely defined if the public database annotation is used as a control.
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              On the Theory of Dynamic Programming.

              R Bellman (1952)
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                Author and article information

                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central
                1471-2105
                2010
                8 March 2010
                : 11
                : 119
                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
                Article
                1471-2105-11-119
                10.1186/1471-2105-11-119
                2848648
                20211023
                bab5f51f-9a8b-44e0-bf44-854e2ea9f4a5
                Copyright ©2010 Hyatt et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 20 July 2009
                : 8 March 2010
                Categories
                Software

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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