BACKGROUND: Functional annotation of bacterial genomes is an obligatory and crucially important step of information processing from the genome sequences into cellular mechanisms. However, there is a lack of computational methods to evaluate the quality of functional assignments. RESULTS: We developed a genome-scale model that assigns Bayesian probability to each gene utilizing a known property of functional similarity between neighboring genes in bacteria. CONCLUSIONS: Our model clearly distinguished true annotation from random annotation with Bayesian annotation probability >0.95. Our model will provide a useful guide to quantitatively evaluate functional annotation methods and to detect gene sets with reliable annotations.

MOTIVATION: Gene prediction in metagenomic sequences remains a difficult problem. Current sequencing technologies do not achieve sufficient coverage to assemble the individual genomes in a typical sample; consequently, sequencing runs produce a large number of short sequences whose exact origin is unknown. Since these sequences are usually smaller than the average length of a gene, algorithms must make predictions based on very little data. RESULTS: We present MetaProdigal, a metagenomic version of the gene prediction program Prodigal, that can identify genes in short, anonymous coding sequences with a high degree of accuracy. The novel value of the method consists of enhanced translation initiation site identification, ability to identify sequences that use alternate genetic codes and confidence values for each gene call. We compare the results of MetaProdigal with other methods and conclude with a discussion of future improvements. AVAILABILITY: The Prodigal software is freely available under the General Public License from http://code.google.com/p/prodigal/.

Background Advances in sequencing technologies are outpacing the rate at which genomes can be thoroughly finished and analyzed. Over the next year, genome sequencing will increase many-fold, but high quality and high-throughput annotation methods have yet to be developed to handle the need. As more microbial genomes are sequenced, whole-genome annotation methods identify many putative genes which need further verification. By analyzing a broad range of annotated genomes we can identify patterns and statistics useful in determining the annotation quality and spurious gene outliers. Our work is attempting to identify quality control measures based on a full inter-genomic comparison instead of individual sequence-level or database-specific statistics. Using these methods to compare and filter, it is possible to narrow the scope of manual gene curation and allow greater scrutiny on putative genes before publication, making higher quality genome annotation possible. Our results plainly show the quality of well-studied genomes, the weaknesses of draft genome builds, and illustrate the need for further high-throughput quality control measures.

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.