EasyGene – a prokaryotic gene finder that ranks ORFs by statistical significance

BACKGROUND: Contrary to other areas of sequence analysis, a measure of statistical significance of a putative gene has not been devised to help in discriminating real genes from the masses of random Open Reading Frames (ORFs) in prokaryotic genomes. Therefore, many genomes have too many short ORFs annotated as genes. RESULTS: In this paper, we present a new automated gene-finding method, EasyGene, which estimates the statistical significance of a predicted gene. The gene finder is based on a hidden Markov model (HMM) that is automatically estimated for a new genome. Using extensions of similarities in Swiss-Prot, a high quality training set of genes is automatically extracted from the genome and used to estimate the HMM. Putative genes are then scored with the HMM, and based on score and length of an ORF, the statistical significance is calculated. The measure of statistical significance for an ORF is the expected number of ORFs in one megabase of random sequence at the same significance level or better, where the random sequence has the same statistics as the genome in the sense of a third order Markov chain. CONCLUSIONS: The result is a flexible gene finder whose overall performance matches or exceeds other methods. The entire pipeline of computer processing from the raw input of a genome or set of contigs to a list of putative genes with significance is automated, making it easy to apply EasyGene to newly sequenced organisms. EasyGene with pre-trained models can be accessed at

MED: a new non-supervised gene prediction algorithm for bacterial and archaeal genomes

BACKGROUND: Despite a remarkable success in the computational prediction of genes in Bacteria and Archaea, a lack of comprehensive understanding of prokaryotic gene structures prevents from further elucidation of differences among genomes. It continues to be interesting to develop new ab initio algorithms which not only accurately predict genes, but also facilitate comparative studies of prokaryotic genomes. RESULTS: This paper describes a new prokaryotic genefinding algorithm based on a comprehensive statistical model of protein coding Open Reading Frames (ORFs) and Translation Initiation Sites (TISs). The former is based on a linguistic "Entropy Density Profile" (EDP) model of coding DNA sequence and the latter comprises several relevant features related to the translation initiation. They are combined to form a so-called Multivariate Entropy Distance (MED) algorithm, MED 2.0, that incorporates several strategies in the iterative program. The iterations enable us to develop a non-supervised learning process and to obtain a set of genome-specific parameters for the gene structure, before making the prediction of genes. CONCLUSION: Results of extensive tests show that MED 2.0 achieves a competitive high performance in the gene prediction for both 5' and 3' end matches, compared to the current best prokaryotic gene finders. The advantage of the MED 2.0 is particularly evident for GC-rich genomes and archaeal genomes. Furthermore, the genome-specific parameters given by MED 2.0 match with the current understanding of prokaryotic genomes and may serve as tools for comparative genomic studies. In particular, MED 2.0 is shown to reveal divergent translation initiation mechanisms in archaeal genomes while making a more accurate prediction of TISs compared to the existing gene finders and the current GenBank annotation

GISMO—gene identification using a support vector machine for ORF classification

We present the novel prokaryotic gene finder GISMO, which combines searches for protein family domains with composition-based classification based on a support vector machine. GISMO is highly accurate; exhibiting high sensitivity and specificity in gene identification. We found that it performs well for complete prokaryotic chromosomes, irrespective of their GC content, and also for plasmids as short as 10 kb, short genes and for genes with atypical sequence composition. Using GISMO, we found several thousand new predictions for the published genomes that are supported by extrinsic evidence, which strongly suggest that these are very likely biologically active genes. The source code for GISMO is freely available under the GPL license

Prodigal: prokaryotic gene recognition and translation initiation site identification

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>We built a fast, lightweight, open source gene prediction program called Prodigal <url>http://compbio.ornl.gov/prodigal/</url>. Prodigal achieved good results compared to existing methods, and we believe it will be a valuable asset to automated microbial annotation pipelines.</p

A single-copy IS5-like transposon in the genome of a bdelloid rotifer

Author Posting. © The Authors, 2009. This is the author's version of the work. It is posted here by permission of Oxford University for personal use, not for redistribution. The definitive version was published in Molecular Biology and Evolution 26 (2009): 1921-1929, doi:10.1093/molbev/msp104.In the course of sequencing telomeric chromosomal regions of the bdelloid rotifer Adineta vaga, we encountered an unusual DNA transposon. Unlike other bdelloid and, more generally, eukaryotic transposable elements (TEs), it exhibits similarity to prokaryotic insertion sequences (IS). Phylogenetic analysis indicates that this transposon, named IS5_Av, is related to the ISL2 group of the IS5 family of bacterial IS elements. Despite the apparent intactness of the single open reading frame coding for a DDE transposase and the perfect identity of its 213-bp terminal inverted repeats (TIRs), the element is present in only one copy per diploid genome. It does not exhibit any detectable levels of transcription, so that its transposase gene appears to be silent in the bdelloid host. While horizontal transfers of TEs between kingdoms are not known to happen in nature, it appears likely that IS5_Av underwent integration into the A. vaga genome relatively recently, but was not successful in adapting to the new host and failed to increase in copy number. Alternatively, it might be the only known member of a novel eukaryotic DNA TE superfamily which is so rare that its other members, if any, have not yet been identified in eukaryotic genomes sequenced to date.This research was supported by the U.S. National Science Foundation grant MCB- 0821956 to I.A

Complete genome sequence of the industrial bacterium Bacillus licheniformis and comparisons with closely related Bacillus species

BACKGROUND: Bacillus licheniformis is a Gram-positive, spore-forming soil bacterium that is used in the biotechnology industry to manufacture enzymes, antibiotics, biochemicals and consumer products. This species is closely related to the well studied model organism Bacillus subtilis, and produces an assortment of extracellular enzymes that may contribute to nutrient cycling in nature. RESULTS: We determined the complete nucleotide sequence of the B. licheniformis ATCC 14580 genome which comprises a circular chromosome of 4,222,336 base-pairs (bp) containing 4,208 predicted protein-coding genes with an average size of 873 bp, seven rRNA operons, and 72 tRNA genes. The B. licheniformis chromosome contains large regions that are colinear with the genomes of B. subtilis and Bacillus halodurans, and approximately 80% of the predicted B. licheniformis coding sequences have B. subtilis orthologs. CONCLUSIONS: Despite the unmistakable organizational similarities between the B. licheniformis and B. subtilis genomes, there are notable differences in the numbers and locations of prophages, transposable elements and a number of extracellular enzymes and secondary metabolic pathway operons that distinguish these species. Differences include a region of more than 80 kilobases (kb) that comprises a cluster of polyketide synthase genes and a second operon of 38 kb encoding plipastatin synthase enzymes that are absent in the B. licheniformis genome. The availability of a completed genome sequence for B. licheniformis should facilitate the design and construction of improved industrial strains and allow for comparative genomics and evolutionary studies within this group of Bacillaceae

Computational evaluation of TIS annotation for prokaryotic genomes

<p>Abstract</p> <p>Background</p> <p>Accurate annotation of translation initiation sites (TISs) is essential for understanding the translation initiation mechanism. However, the reliability of TIS annotation in widely used databases such as RefSeq is uncertain due to the lack of experimental benchmarks.</p> <p>Results</p> <p>Based on a homogeneity assumption that gene translation-related signals are uniformly distributed across a genome, we have established a computational method for a large-scale quantitative assessment of the reliability of TIS annotations for any prokaryotic genome. The method consists of modeling a positional weight matrix (PWM) of aligned sequences around predicted TISs in terms of a linear combination of three elementary PWMs, one for true TIS and the two others for false TISs. The three elementary PWMs are obtained using a reference set with highly reliable TIS predictions. A generalized least square estimator determines the weighting of the true TIS in the observed PWM, from which the accuracy of the prediction is derived. The validity of the method and the extent of the limitation of the assumptions are explicitly addressed by testing on experimentally verified TISs with variable accuracy of the reference sets. The method is applied to estimate the accuracy of TIS annotations that are provided on public databases such as RefSeq and ProTISA and by programs such as EasyGene, GeneMarkS, Glimmer 3 and TiCo. It is shown that RefSeq's TIS prediction is significantly less accurate than two recent predictors, Tico and ProTISA. With convincing proofs, we show two general preferential biases in the RefSeq annotation, <it>i.e</it>. over-annotating the longest open reading frame (LORF) and under-annotating ATG start codon. Finally, we have established a new TIS database, SupTISA, based on the best prediction of all the predictors; SupTISA has achieved an average accuracy of 92% over all 532 complete genomes.</p> <p>Conclusion</p> <p>Large-scale computational evaluation of TIS annotation has been achieved. A new TIS database much better than RefSeq has been constructed, and it provides a valuable resource for further TIS studies.</p

Characterization of probiotic Escherichia coli isolates with a novel pan-genome microarray

pan-genome microarra

Microbial comparative pan-genomics using binomial mixture models

<p>Abstract</p> <p>Background</p> <p>The size of the core- and pan-genome of bacterial species is a topic of increasing interest due to the growing number of sequenced prokaryote genomes, many from the same species. Attempts to estimate these quantities have been made, using regression methods or mixture models. We extend the latter approach by using statistical ideas developed for capture-recapture problems in ecology and epidemiology.</p> <p>Results</p> <p>We estimate core- and pan-genome sizes for 16 different bacterial species. The results reveal a complex dependency structure for most species, manifested as heterogeneous detection probabilities. Estimated pan-genome sizes range from small (around 2600 gene families) in <it>Buchnera aphidicola </it>to large (around 43000 gene families) in <it>Escherichia coli</it>. Results for <it>Echerichia coli </it>show that as more data become available, a larger diversity is estimated, indicating an extensive pool of rarely occurring genes in the population.</p> <p>Conclusion</p> <p>Analyzing pan-genomics data with binomial mixture models is a way to handle dependencies between genomes, which we find is always present. A bottleneck in the estimation procedure is the annotation of rarely occurring genes.</p