GUNC: detection of chimerism and contamination in prokaryotic genomes.
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ABSTRACT:
SUBMITTER: Orakov A
PROVIDER: S-EPMC8201837 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Endosymbiotic theory in eukaryotic-cell evolution rests upon a foundation of three cornerstone partners--the plastid (a cyanobacterium), the mitochondrion (a proteobacterium), and its host (an archaeon)--and carries a corollary that, over time, the majority of genes once present in the organelle genomes were relinquished to the chromosomes of the host (endosymbiotic gene transfer). However, notwithstanding eukaryote-specific gene inventions, single-gene phylogenies have never traced eukaryotic genes to three single prokaryotic sources, an issue that hinges crucially upon factors influencing phylogenetic inference. In the age of genomes, single-gene trees, once used to test the predictions of endosymbiotic theory, now spawn new theories that stand to eventually replace endosymbiotic theory with descriptive, gene tree-based variants featuring supernumerary symbionts: prokaryotic partners distinct from the cornerstone trio and whose existence is inferred solely from single-gene trees. We reason that the endosymbiotic ancestors of mitochondria and chloroplasts brought into the eukaryotic--and plant and algal--lineage a genome-sized sample of genes from the proteobacterial and cyanobacterial pangenomes of their respective day and that, even if molecular phylogeny were artifact-free, sampling prokaryotic pangenomes through endosymbiotic gene transfer would lead to inherited chimerism. Recombination in prokaryotes (transduction, conjugation, transformation) differs from recombination in eukaryotes (sex). Prokaryotic recombination leads to pangenomes, and eukaryotic recombination leads to vertical inheritance. Viewed from the perspective of endosymbiotic theory, the critical transition at the eukaryote origin that allowed escape from Muller's ratchet--the origin of eukaryotic recombination, or sex--might have required surprisingly little evolutionary innovation.
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Project description:We present a study on computational identification of uber-operons in a prokaryotic genome, each of which represents a group of operons that are evolutionarily or functionally associated through operons in other (reference) genomes. Uber-operons represent a rich set of footprints of operon evolution, whose full utilization could lead to new and more powerful tools for elucidation of biological pathways and networks than what operons have provided, and a better understanding of prokaryotic genome structures and evolution. Our prediction algorithm predicts uber-operons through identifying groups of functionally or transcriptionally related operons, whose gene sets are conserved across the target and multiple reference genomes. Using this algorithm, we have predicted uber-operons for each of a group of 91 genomes, using the other 90 genomes as references. In particular, we predicted 158 uber-operons in Escherichia coli K12 covering 1830 genes, and found that many of the uber-operons correspond to parts of known regulons or biological pathways or are involved in highly related biological processes based on their Gene Ontology (GO) assignments. For some of the predicted uber-operons that are not parts of known regulons or pathways, our analyses indicate that their genes are highly likely to work together in the same biological processes, suggesting the possibility of new regulons and pathways. We believe that our uber-operon prediction provides a highly useful capability and a rich information source for elucidation of complex biological processes, such as pathways in microbes. All the prediction results are available at our Uber-Operon Database: http://csbl.bmb.uga.edu/uber, the first of its kind.
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Project description:Simple sequence repeats (SSRs) in DNA sequences are composed of tandem iterations of short oligonucleotides and may have functional and/or structural properties that distinguish them from general DNA sequences. They are variable in length because of slip-strand mutations and may also affect local structure of the DNA molecule or the encoded proteins. Long SSRs (LSSRs) are common in eukaryotes but rare in most prokaryotes. In pathogens, SSRs can enhance antigenic variance of the pathogen population in a strategy that counteracts the host immune response. We analyze representations of SSRs in >300 prokaryotic genomes and report significant differences among different prokaryotes as well as among different types of SSRs. LSSRs composed of short oligonucleotides (1-4 bp length, designated LSSR(1-4)) are often found in host-adapted pathogens with reduced genomes that are not known to readily survive in a natural environment outside the host. In contrast, LSSRs composed of longer oligonucleotides (5-11 bp length, designated LSSR(5-11)) are found mostly in nonpathogens and opportunistic pathogens with large genomes. Comparisons among SSRs of different lengths suggest that LSSR(1-4) are likely maintained by selection. This is consistent with the established role of some LSSR(1-4) in enhancing antigenic variance. By contrast, abundance of LSSR(5-11) in some genomes may reflect the SSRs' general tendency to expand rather than their specific role in the organisms' physiology. Differences among genomes in terms of SSR representations and their possible interpretations are discussed.
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Project description:Prokaryotic restriction-modification (R-M) systems defend the host cell from the invasion of a foreign DNA. They comprise two enzymatic activities: specific DNA cleavage activity and DNA methylation activity preventing cleavage. Typically, these activities are provided by two separate enzymes: a DNA methyltransferase (MTase) and a restriction endonuclease (RE). In the absence of a corresponding MTase, an RE of Type II R-M system is highly toxic for the cell. Genes of the R-M system are linked in the genome in the vast majority of annotated cases. There are only a few reported cases in which the genes of MTase and RE from one R-M system are not linked. Nevertheless, a few hundreds solitary RE genes are present in the Restriction Enzyme Database (http://rebase.neb.com) annotations. Using the comparative genomic approach, we analysed 272 solitary RE genes. For 57 solitary RE genes we predicted corresponding MTase genes located distantly in a genome. Of the 272 solitary RE genes, 99 are likely to be fragments of RE genes. Various explanations for the existence of the remaining 116 solitary RE genes are also discussed.
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Project description: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.
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Project description:BackgroundIn silco Biology is increasingly important and is often based on public data. While the problem of contamination is well recognised in microbiology labs the corresponding problem of database corruption has received less attention.ResultsMapping 50 billion next generation DNA sequences from The Thousand Genome Project against published genomes reveals many that match one or more Mycoplasma but are not included in the reference human genome GRCh37.p5. Many of these are of low quality but NCBI BLAST searches confirm some high quality, high entropy sequences match Mycoplasma but no human sequences.ConclusionsIt appears at least 7% of 1000G samples are contaminated.
| S-EPMC4022254 | biostudies-literature
Project description:Bacterial genomics studies are getting more extensive and complex, requiring new ways to envision analyses. Using the Ray Surveyor software, we demonstrate that comparison of genomes based on their k-mer content allows reconstruction of phenetic trees without the need of prior data curation, such as core genome alignment of a species. We validated the methodology using simulated genomes and previously published phylogenomic studies of Streptococcus pneumoniae and Pseudomonas aeruginosa. We also investigated the relationship of specific genetic determinants with bacterial population structures. By comparing clusters from the complete genomic content of a genome population with clusters from specific functional categories of genes, we can determine how the population structures are correlated. Indeed, the strain clustering based on a subset of k-mers allows determination of its similarity with the whole genome clusters. We also applied this methodology on 42 species of bacteria to determine the correlational significance of five important bacterial genomic characteristics. For example, intrinsic resistance is more important in P. aeruginosa than in S. pneumoniae, and the former has increased correlation of its population structure with antibiotic resistance genes. The global view of the pangenome of bacteria also demonstrated the taxa-dependent interaction of population structure with antibiotic resistance, bacteriophage, plasmid, and mobile element k-mer data sets.
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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.
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Project description:BackgroundProtein-coding gene detection in prokaryotic genomes is considered a much simpler problem than in intron-containing eukaryotic genomes. However there have been reports that prokaryotic gene finder programs have problems with small genes (either over-predicting or under-predicting). Therefore the question arises as to whether current genome annotations have systematically missing, small genes.ResultsWe have developed a high-performance computing methodology to investigate this problem. In this methodology we compare all ORFs larger than or equal to 33 aa from all fully-sequenced prokaryotic replicons. Based on that comparison, and using conservative criteria requiring a minimum taxonomic diversity between conserved ORFs in different genomes, we have discovered 1,153 candidate genes that are missing from current genome annotations. These missing genes are similar only to each other and do not have any strong similarity to gene sequences in public databases, with the implication that these ORFs belong to missing gene families. We also uncovered 38,895 intergenic ORFs, readily identified as putative genes by similarity to currently annotated genes (we call these absent annotations). The vast majority of the missing genes found are small (less than 100 aa). A comparison of select examples with GeneMark, EasyGene and Glimmer predictions yields evidence that some of these genes are escaping detection by these programs.ConclusionsProkaryotic gene finders and prokaryotic genome annotations require improvement for accurate prediction of small genes. The number of missing gene families found is likely a lower bound on the actual number, due to the conservative criteria used to determine whether an ORF corresponds to a real gene.
| S-EPMC3098052 | biostudies-literature
Project description:BackgroundThe integration of genomic information with quantitative experimental data is a key component of systems biology. An increasing number of microbial genomes are being sequenced, leading to an increasing amount of data from post-genomics technologies. The genomes of prokaryotes contain many structures of interest, such as operons, pathogenicity islands and prophage sequences, whose behaviour is of interest during infection and disease. There is a need for simple and novel tools to display and analyse data from these integrated datasets, and we have developed ProGenExpress as a tool for visualising arbitrarily complex numerical data in the context of prokaryotic genomes.ResultsHere we describe ProGenExpress, an R package that allows researchers to easily and quickly visualize quantitative measurements, such as those produced by microarray experiments, in the context of the genome organization of sequenced prokaryotes. Data from microarrays, proteomics or other whole-genome technologies can be accurately displayed on the genome. ProGenExpress can also search for novel regions of interest that consist of groups of adjacent genes that show similar patterns across the experimental data set. We demonstrate ProGenExpress with microarray data from a time-course experiment involving Salmonella typhimurium.ConclusionProGenExpress can be used to visualize quantitative data from complex experiments in the context of the genome of sequenced prokaryotes, and to find novel regions of interest.
| S-EPMC1087476 | biostudies-literature