Dataset Information

Tracing lifestyle adaptation in prokaryotic genomes.

ABSTRACT: Lifestyle adaptation of microbes due to changes in their ecological niches or acquisition of new environments is a major driving force for genetic changes in their respective genomes. Moving into more specialized niches often results in the acquisition of new gene sets via horizontal gene transfer to utilize previously unavailable metabolites, while genetic ballast is shed by gene loss and/or gene inactivation. In some cases, larger genome rearrangements can be observed, such as the incorporation of whole genetic islands, providing a range of new phenotypic capabilities. Until recently these changes could not be comprehensively followed and identified due to the lack of complete microbial genome sequences. The advent of high-throughput DNA sequencing has dramatically changed the scientific landscape and today microbial genomes have become increasingly abundant. Currently, more than 2,900 genomes are published and more than 11,000 genome projects are listed in the Genomes Online Database. Although this wealth of information provides many new opportunities to assess microbial functionality, it also creates a new array of challenges when a comparison between multiple microbial genomes is required. Here, functional genome distribution (FGD) is introduced, analyzing the diversity between microbes based on their predicted ORFeome. FGD is therefore a comparative genomics approach, emphasizing the assessments of gene complements. To further facilitate the comparison between two or more genomes, degrees of amino-acid similarities between ORFeomes can be visualized in the Artemis comparison tool, graphically depicting small and large scale genome rearrangements, insertion and deletion events, and levels of similarity between individual open reading frames. FGD provides a new tool for comparative microbial genomics and the interpretation of differences in the genetic makeup of bacteria.

SUBMITTER: Altermann E

PROVIDER: S-EPMC3282942 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Tracing lifestyle adaptation in prokaryotic genomes.

Altermann Eric E

Frontiers in microbiology 20120221

Lifestyle adaptation of microbes due to changes in their ecological niches or acquisition of new environments is a major driving force for genetic changes in their respective genomes. Moving into more specialized niches often results in the acquisition of new gene sets via horizontal gene transfer to utilize previously unavailable metabolites, while genetic ballast is shed by gene loss and/or gene inactivation. In some cases, larger genome rearrangements can be observed, such as the incorporatio ...[more]

PMID: 22363326

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Project description:BackgroundAccurate 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.ResultsBased 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, i.e. 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.ConclusionLarge-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.

Dataset Information

Tracing lifestyle adaptation in prokaryotic genomes.

Publications

Tracing lifestyle adaptation in prokaryotic genomes.

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