DIYA: a bacterial annotation pipeline for any genomics lab

Stewart, Andrew C.; Osborne, Brian I.; Read, Timothy D.

doi:10.1093/bioinformatics/btp097

Cited by 75 publications

(59 citation statements)

References 16 publications

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“…Nine newly sequenced draft genomes and three publicly available complete genomes of different A. pleuropneumoniae serovars were annotated by using an automated bacterial annotation pipeline DIYA (40) and custom Perl scripts. Using DIYA, the genomic contigs were tiled against a complete genome (from strain L20) by the promer script in the MUMmer package (28).…”

Section: Methodsmentioning

confidence: 99%

Comparative Genomic Characterization of Actinobacillus pleuropneumoniae

Zhao

Chen

et al. 2010

J Bacteriol

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The Gram-negative bacterium Actinobacillus pleuropneumoniae is the etiologic agent of porcine contagious pleuropneumoniae, a lethal respiratory infectious disease causing great economic losses in the swine industry worldwide. In order to better interpret the genetic background of serotypic diversity, nine genomes of A. pleuropneumoniae reference strains of serovars 1, 2, 4, 6, 9, 10, 11, 12, and 13 were sequenced by using rapid high-throughput approach. Based on 12 genomes of corresponding serovar reference strains including three publicly available complete genomes (serovars 3, 5b, and 7) of this bacterium, we performed a comprehensive analysis of comparative genomics and first reported a global genomic characterization for this pathogen. Clustering of 26,012 predicted protein-coding genes showed that the pan genome of A. pleuropneumoniae consists of 3,303 gene clusters, which contain 1,709 core genome genes, 822 distributed genes, and 772 strain-specific genes. The genome components involved in the biogenesis of capsular polysaccharide and lipopolysaccharide O antigen relative to serovar diversity were compared, and their genetic diversity was depicted. Our findings shed more light on genomic features associated with serovar diversity of A. pleuropneumoniae and provide broader insight into both pathogenesis research and clinical/epidemiological application against the severe disease caused by this swine pathogen.Actinobacillus pleuropneumoniae, a Gram-negative facultative anaerobic encapsulated coccobacillus, belongs to the Actinobacillus genus of the Pasteurellaceae family (19). A. pleuropneumoniae is a primary bacterial etiologic agent of porcine contagious pleuropneumonia, a severe respiratory disease leading to great economic losses to the global swine industry (7). The cases usually display pleuropneumonia and pulmonary lesions characterized by serious hemorrhage and necrosis. To date, several factors involved in the virulence of A. pleuropneumoniae have been described, including Apx exotoxins, capsular polysaccharides (CPS), lipopolysaccharides (LPS), outer membrane proteins, iron-acquisition proteins and adhesin factors (11,19,24). However, the genetic differences of pathogenesis remain poorly characterized and are worth interpreting from the perspective of comparative genomics for this bacterium.Thus far, 15 serovars and two biotypes of A. pleuropneumoniae have been recognized, with great variations in virulence and interlocal distributions (6). The predominant serovar-specific antigens are composed of CPS, which could rigorously define serovars of A. pleuropneumoniae (6, 34). Antigenic differences in the LPS can further determine A. pleuropneumoniae subtypes within a same capsular serovar (13). The metabolic and virulent characteristics of this pathogen have been systematically described based on the prior knowledge and two complete genomes (18, 47), but the molecular basis and evolutionary mechanism of serotypic diversity are still not well explained due to the lack of sequence information. To invest...

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Section: Methodsmentioning

confidence: 99%

Comparative Genomic Characterization of Actinobacillus pleuropneumoniae

Zhao

Chen

et al. 2010

J Bacteriol

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“…We used the DIYA Perl-based pipeline (Stewart et al 2009) for automated annotation of contigs generated by WGS data. As the first step of the DIYA pipeline, the contigs generated by Newbler were mapped against the B. anthracis Ames ancestor sequence ) using the MUMmer alignment tool to create a concatenated ordered ''pseudocontig.''…”

Section: Automated Annotationmentioning

confidence: 99%

Genomic characterization of theBacillus cereussensu lato species: Backdrop to the evolution ofBacillus anthracis

Zwick¹,

Joseph²,

Didelot³

et al. 2012

Genome Res.

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151

139

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The key genes required for Bacillus anthracis to cause anthrax have been acquired recently by horizontal gene transfer. To understand the genetic background for the evolution of B. anthracis virulence, we obtained high-redundancy genome sequences of 45 strains of the Bacillus cereus sensu lato (s.l.) species that were chosen for their genetic diversity within the species based on the existing multilocus sequence typing scheme. From the resulting data, we called more than 324,000 new genes representing more than 12,333 new gene families for this group. The core genome size for the B. cereus s.l. group was ∼1750 genes, with another 2150 genes found in almost every genome constituting the extended core. There was a paucity of genes specific and conserved in any clade. We found no evidence of recent large-scale gene loss in B. anthracis or for unusual accumulation of nonsynonymous DNA substitutions in the chromosome; however, several B. cereus genomes isolated from soil and not previously associated with human disease were degraded to various degrees. Although B. anthracis has undergone an ecological shift within the species, its chromosome does not appear to be exceptional on a macroscopic scale compared with close relatives.

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“…Identification of genes is one of the most important and challenging problems in whole-microbial genomesequencing projects (11)(12)(13). In metagenomics, gene finding can provide the opportunity to elucidate the activities and interactions of genes within an environmental sample, from which the metabolic and signaling pathways specific to the environment can be reconstructed and identified (14).…”

Section: Introductionmentioning

confidence: 99%

FragGeneScan: Predicting Genes in Short and Error-Prone Reads

Ye¹

2014

Encyclopedia of Metagenomics

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The advances of next-generation sequencing technology have facilitated metagenomics research that attempts to determine directly the whole collection of genetic material within an environmental sample (i.e. the metagenome). Identification of genes directly from short reads has become an important yet challenging problem in annotating metagenomes, since the assembly of metagenomes is often not available. Gene predictors developed for whole genomes (e.g. Glimmer) and recently developed for metagenomic sequences (e.g. MetaGene) show a significant decrease in performance as the sequencing error rates increase, or as reads get shorter. We have developed a novel gene prediction method FragGeneScan, which combines sequencing error models and codon usages in a hidden Markov model to improve the prediction of protein-coding region in short reads. The performance of FragGeneScan was comparable to Glimmer and MetaGene for complete genomes. But for short reads, FragGeneScan consistently outperformed MetaGene (accuracy improved 62% for reads of 400 bases with 1% sequencing errors, and 18% for short reads of 100 bases that are error free). When applied to metagenomes, FragGeneScan recovered substantially more genes than MetaGene predicted (>90% of the genes identified by homology search), and many novel genes with no homologs in current protein sequence database.

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DIYA: a bacterial annotation pipeline for any genomics lab

Cited by 75 publications

References 16 publications

Comparative Genomic Characterization of Actinobacillus pleuropneumoniae

Comparative Genomic Characterization of Actinobacillus pleuropneumoniae

Genomic characterization of theBacillus cereussensu lato species: Backdrop to the evolution ofBacillus anthracis

FragGeneScan: Predicting Genes in Short and Error-Prone Reads

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