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

Larsen, T. S.; Krogh, Anders

doi:10.1186/1471-2105-4-21

Cited by 137 publications

(64 citation statements)

References 33 publications

(38 reference statements)

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“…For prokaryotes, algorithms that focused almost entirely on the prediction stage of the gene identification problem (i.e., how to find genes given a training sequence) evolved into automatic self-training systems capable of adjusting to genome-specific properties in the process of estimating algorithm parameters from anonymous sequence (Audic and Claverie 1998;Hayes and Borodovsky 1998;Salzberg et al 1998;Besemer et al 2001;Larsen and Krogh 2003). A recent dramatic increase in the number of eukaryotic targets of genome sequencing has necessitated the development of unsupervised ab initio gene prediction algorithms for eukaryotes.…”

Section: Discussionmentioning

confidence: 99%

“…We demonstrated that unsupervised model training, well known in prokaryotic gene finding (Audic and Claverie 1998;Hayes and Borodovsky 1998;Salzberg et al 1998;Besemer et al 2001;Larsen and Krogh 2003;Delcher et al 2007), is also feasible for eukaryotes. Particularly, for genomes of Arabidopsis thaliana, Drosophia melanogaster, and Caenorhabditis elegans, the accuracy of the gene finder with unsupervised parameter estimation matched the accuracy of a conventional supervised gene finder.…”

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confidence: 96%

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Gene prediction in novel fungal genomes using an ab initio algorithm with unsupervised training

Ter-Hovhannisyan¹,

Lomsadze²,

Chernoff³

et al. 2008

Genome Res.

831

609

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We describe a new ab initio algorithm, GeneMark-ES version 2, that identifies protein-coding genes in fungal genomes. The algorithm does not require a predetermined training set to estimate parameters of the underlying hidden Markov model (HMM). Instead, the anonymous genomic sequence in question is used as an input for iterative unsupervised training. The algorithm extends our previously developed method tested on genomes of Arabidopsis thaliana, Caenorhabditis elegans, and Drosophila melanogaster. To better reflect features of fungal gene organization, we enhanced the intron submodel to accommodate sequences with and without branch point sites. This design enables the algorithm to work equally well for species with the kinds of variations in splicing mechanisms seen in the fungal phyla Ascomycota, Basidiomycota, and Zygomycota. Upon self-training, the intron submodel switches on in several steps to reach its full complexity. We demonstrate that the algorithm accuracy, both at the exon and the whole gene level, is favorably compared to the accuracy of gene finders that employ supervised training. Application of the new method to known fungal genomes indicates substantial improvement over existing annotations. By eliminating the effort necessary to build comprehensive training sets, the new algorithm can streamline and accelerate the process of annotation in a large number of fungal genome sequencing projects.[Supplemental material is available online at www.genome.org. The new software program GeneMark-ES version 2 is freely available for download from http://exon.gatech.edu/genemark/gmhmm- es-2008.] Reliable ab initio gene prediction in eukaryotic genomic sequences remains an open problem in spite of impressive progress made in developing gene prediction algorithms (Burge and Karlin 1997;Krogh 1997;Parra et al. 2000;Reese et al. 2000;Stanke and Waack 2003;Guigo et al. 2006). Much attention has been given to developing alternative, extrinsic methods that use EST/cDNA to genome mapping, spliced alignments of known protein sequences, or patterns of conservation between related genomes (Gelfand et al. 1996;Mott 1997;Mathe et al. 2002;Birney et al. 2004;Stanke et al. 2008). The extrinsic methods exhibit, as a rule, high specificity (Sp), while ab initio, intrinsic methods show high sensitivity (Sn). These properties make methods of both types indispensable in genome annotation pipelines. For accurate statistical description of protein-coding regions, efficient ab initio algorithms employ the fifth-order three periodic Markov chain models incorporated into hidden Markov models (HMMs) (Kulp et al. 1996;Burge and Karlin 1997) The number of algorithm parameters, several thousands, is high; thus, a training set of ∼1000 experimentally validated genes is necessary for parameter estimation. Compilation of such large training sets represents a bottleneck in genome annotation pipelines and a practical challenge that hampers the use of ab initio gene prediction algorithms.To circumvent this difficulty, we developed a gene...

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 96%

Gene prediction in novel fungal genomes using an ab initio algorithm with unsupervised training

Ter-Hovhannisyan¹,

Lomsadze²,

Chernoff³

et al. 2008

Genome Res.

831

609

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“…The coding percentage for both strains was ϳ86.8%; UJ308A and UJ816A contained approximately 4,720 and 4,710 protein coding sequences with average lengths of 869 and 873 bp, respectively. The data were further validated by Glimmer (7) and EasyGene (12). RNAmmer (11) revealed that the genome of UJ308A has 78 tRNA and 21 rRNA genes and the genome of UJ816A contains 77 tRNA and 22 rRNA genes.…”

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confidence: 99%

Whole-Genome Sequences and Comparative Genomics of Salmonella enterica Serovar Typhi Isolates from Patients with Fatal and Nonfatal Typhoid Fever in Papua New Guinea

et al. 2012

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Many of the developing countries of the Southeast Asian region are significantly affected by endemic typhoid fever, possibly as a result of marginal living standards. It is an important public health problem in countries such as Papua New Guinea, which is geographically close to some of the foci of endemicity in Asia. The severity of the disease varies in different regions, and this may be attributable to genetic diversity among the native strains. Genome sequence data on strains from different countries are needed to clearly understand their genetic makeup and virulence potential. We describe the genomes of two Salmonella Typhi isolates from patients with fatal and nonfatal cases of typhoid fever in Papua New Guinea. We discuss in brief the underlying sequencing methodology, assembly, genome statistics, and important features of the two draft genomes, which form an essential step in our functional molecular infection epidemiology program centering on typhoid fever. The comparative genomics of these and other isolates would enable us to identify genetic rearrangements and mechanisms responsible for endemicity and the differential severity of pathogenic salmonellae in Papua New Guinea and elsewhere.

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“…Coding sequences (CDSs) were determined by using the EasyGene (20) and Prodigal (21) programs, followed by a comparison of their outputs and manual resolution of their discrepancies based on the presence of potential ribosomal binding sites, similarity searches, multiple-sequence alignments, and data reported previously. Gene functions were assigned based on a comparison of the outputs of two annotation tools: RAST (22) and PANNZER (23).…”

Section: Methodsmentioning

confidence: 99%

Genome Sequence and Transcriptome Analysis of Meat-Spoilage-Associated Lactic Acid Bacterium Lactococcus piscium MKFS47

Andreevskaya

Johansson

Laine

et al. 2015

Appl Environ Microbiol

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bLactococcus piscium is a psychrotrophic lactic acid bacterium and is known to be one of the predominant species within spoilage microbial communities in cold-stored packaged foods, particularly in meat products. Its presence in such products has been associated with the formation of buttery and sour off-odors. Nevertheless, the spoilage potential of L. piscium varies dramatically depending on the strain and growth conditions. Additional knowledge about the genome is required to explain such variation, understand its phylogeny, and study gene functions. Here, we present the complete and annotated genomic sequence of L. piscium MKFS47, combined with a time course analysis of the glucose catabolism-based transcriptome. In addition, a comparative analysis of gene contents was done for L. piscium MKFS47 and 29 other lactococci, revealing three distinct clades within the genus. The genome of L. piscium MKFS47 consists of one chromosome, carrying 2,289 genes, and two plasmids. A wide range of carbohydrates was predicted to be fermented, and growth on glycerol was observed. Both carbohydrate and glycerol catabolic pathways were significantly upregulated in the course of time as a result of glucose exhaustion. At the same time, differential expression of the pyruvate utilization pathways, implicated in the formation of spoilage substances, switched the metabolism toward a heterofermentative mode. In agreement with data from previous inoculation studies, L. piscium MKFS47 was identified as an efficient producer of buttery-odor compounds under aerobic conditions. Finally, genes and pathways that may contribute to increased survival in meat environments were considered. Bacterial spoilage of perishable cold-stored foods is still a serious problem in the modern world. Modified-atmosphere packaging (MAP), based on the inhibitory effect of CO 2 on spoilage bacteria, is often used by the food industry to achieve a longer product shelf life. While it eliminates aerobic Gram-negative spoilage bacteria, MAP creates a positive selective pressure for lactic acid bacteria (LAB), which are resistant to high CO 2 concentrations (1, 2). Even though spoilage LAB grow slower than aerobic Gram-negative spoilage bacteria, they cause organoleptic and textural spoilage changes, such as discoloration and the formation of slime, gas, biogenic amines, and volatile organic compounds (1), in MAP food products.The most abundant genera in MAP food spoilage communities are Carnobacterium, Lactobacillus, Lactococcus, Leuconostoc, and Weissella (3). So far, Lactococcus piscium has been the only Lactococcus species with reported food spoilage activity (4, 5). It was first isolated from a diseased salmonid fish (6) but was later detected in MAP meat products, including beef, poultry, pork, and fish, where it frequently belongs to the predominant microbiota at the end of shelf life. L. piscium has been associated with meat spoilage (3, 5, 7-9), and it was recently also shown to be one of the predominating species in chilled packaged vegetable salads (10)...

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

Cited by 137 publications

References 33 publications

Gene prediction in novel fungal genomes using an ab initio algorithm with unsupervised training

Gene prediction in novel fungal genomes using an ab initio algorithm with unsupervised training

Whole-Genome Sequences and Comparative Genomics of Salmonella enterica Serovar Typhi Isolates from Patients with Fatal and Nonfatal Typhoid Fever in Papua New Guinea

Genome Sequence and Transcriptome Analysis of Meat-Spoilage-Associated Lactic Acid Bacterium Lactococcus piscium MKFS47

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