Systematic integration of experimental data and models in systems biology |
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Authors: | Peter Li Joseph O Dada Daniel Jameson Irena Spasic Neil Swainston Kathleen Carroll Warwick Dunn Farid Khan Naglis Malys Hanan L Messiha Evangelos Simeonidis Dieter Weichart Catherine Winder Jill Wishart David S Broomhead Carole A Goble Simon J Gaskell Douglas B Kell Hans V Westerhoff Pedro Mendes Norman W Paton |
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Institution: | 1. Oak Ridge National Laboratory, Computational Biology and Bioinformatics Group, Oak Ridge, TN, 37831, USA 2. Genome Science and Technology Graduate School, The University of Tennessee, Knoxville, TN, 37996, USA 3. Oak Ridge National Laboratory, DOE Joint Genome Institute, Oak Ridge, TN, 37831, USA
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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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