How sequence populations persist inside bacterial genomes.
Ontology highlight
ABSTRACT:
SUBMITTER: Park HJ
PROVIDER: S-EPMC8049555 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Amplicon sequencing variants (ASVs) have been proposed as an alternative to operational taxonomic units (OTUs) for analyzing microbial communities. ASVs have grown in popularity, in part because of a desire to reflect a more refined level of taxonomy since they do not cluster sequences based on a distance-based threshold. However, ASVs and the use of overly narrow thresholds to identify OTUs increase the risk of splitting a single genome into separate clusters. To assess this risk, I analyzed the intragenomic variation of 16S rRNA genes from the bacterial genomes represented in an rrn copy number database, which contained 20,427 genomes from 5,972 species. As the number of copies of the 16S rRNA gene increased in a genome, the number of ASVs also increased. There was an average of 0.58 ASVs per copy of the 16S rRNA gene for full-length 16S rRNA genes. It was necessary to use a distance threshold of 5.25% to cluster full-length ASVs from the same genome into a single OTU with 95% confidence for genomes with 7 copies of the 16S rRNA, such as Escherichia coli. This research highlights the risk of splitting a single bacterial genome into separate clusters when ASVs are used to analyze 16S rRNA gene sequence data. Although there is also a risk of clustering ASVs from different species into the same OTU when using broad distance thresholds, these risks are of less concern than artificially splitting a genome into separate ASVs and OTUs. IMPORTANCE 16S rRNA gene sequencing has engendered significant interest in studying microbial communities. There has been tension between trying to classify 16S rRNA gene sequences to increasingly lower taxonomic levels and the reality that those levels were defined using more sequence and physiological information than is available from a fragment of the 16S rRNA gene. Furthermore, the naming of bacterial taxa reflects the biases of those who name them. One motivation for the recent push to adopt ASVs in place of OTUs in microbial community analyses is to allow researchers to perform their analyses at the finest possible level that reflects species-level taxonomy. The current research is significant because it quantifies the risk of artificially splitting bacterial genomes into separate clusters. Far from providing a better representation of bacterial taxonomy and biology, the ASV approach can lead to conflicting inferences about the ecology of different ASVs from the same genome.
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Project description:MotivationGenomic islands (GEIs) are clusters of genes in bacterial genomes that are typically acquired by horizontal gene transfer. GEIs play a crucial role in the evolution of bacteria by rapidly introducing genetic diversity and thus helping them adapt to changing environments. Specifically of interest to human health, many GEIs contain pathogenicity and antimicrobial resistance genes. Detecting GEIs is, therefore, an important problem in biomedical and environmental research. There have been many previous studies for computationally identifying GEIs. Still, most of these studies rely on detecting anomalies in the unannotated nucleotide sequences or on a fixed set of known features on annotated nucleotide sequences.ResultsHere, we present TreasureIsland, which uses a new unsupervised representation of DNA sequences to predict GEIs. We developed a high-precision boundary detection method featuring an incremental fine-tuning of GEI borders, and we evaluated the accuracy of this framework using a new comprehensive reference dataset, Benbow. We show that TreasureIsland's accuracy rivals other GEI predictors, enabling efficient and faster identification of GEIs in unannotated bacterial genomes.Availability and implementationTreasureIsland is available under an MIT license at: https://github.com/FriedbergLab/GenomicIslandPrediction.
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Project description:Determining the conditions under which populations may persist requires accurate estimates of demographic parameters, including immigration, local reproductive success, and mortality rates. In marine populations, empirical estimates of these parameters are rare, due at least in part to the pelagic dispersal stage common to most marine organisms. Here, we evaluate population persistence and turnover for a population of orange clownfish, Amphiprion percula, at Kimbe Island in Papua New Guinea. All fish in the population were sampled and genotyped on five occasions at 2-year intervals spanning eight years. The genetic data enabled estimates of reproductive success retained in the same population (reproductive success to self-recruitment), reproductive success exported to other subpopulations (reproductive success to local connectivity), and immigration and mortality rates of sub-adults and adults. Approximately 50% of the recruits were assigned to parents from the Kimbe Island population and this was stable through the sampling period. Stability in the proportion of local and immigrant settlers is likely due to: low annual mortality rates and stable egg production rates, and the short larval stages and sensory capacities of reef fish larvae. Biannual mortality rates ranged from 0.09 to 0.55 and varied significantly spatially. We used these data to parametrize a model that estimated the probability of the Kimbe Island population persisting in the absence of immigration. The Kimbe Island population was found to persist without significant immigration. Model results suggest the island population persists because the largest of the subpopulations are maintained due to having low mortality and high self-recruitment rates. Our results enable managers to appropriately target and scale actions to maximize persistence likelihood as disturbance frequencies increase.
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Project description:Draft genomes of main human and animal bacterial pathogens Genome sequencing and assembly
| PRJNA700858 | ENA
Project description:BackgroundThe recent introduction of the Pacific Biosciences RS single molecule sequencing technology has opened new doors to scaffolding genome assemblies in a cost-effective manner. The long read sequence information is promised to enhance the quality of incomplete and inaccurate draft assemblies constructed from Next Generation Sequencing (NGS) data.ResultsHere we propose a novel hybrid assembly methodology that aims to scaffold pre-assembled contigs in an iterative manner using PacBio RS long read information as a backbone. On a test set comprising six bacterial draft genomes, assembled using either a single Illumina MiSeq or Roche 454 library, we show that even a 50× coverage of uncorrected PacBio RS long reads is sufficient to drastically reduce the number of contigs. Comparisons to the AHA scaffolder indicate our strategy is better capable of producing (nearly) complete bacterial genomes.ConclusionsThe current work describes our SSPACE-LongRead software which is designed to upgrade incomplete draft genomes using single molecule sequences. We conclude that the recent advances of the PacBio sequencing technology and chemistry, in combination with the limited computational resources required to run our program, allow to scaffold genomes in a fast and reliable manner.
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Project description:BackgroundAnalysis of non-coding sequences in several bacterial genomes brought to the identification of families of repeated sequences, able to fold as secondary structures. These sequences have often been claimed to be transcribed and fulfill a functional role. A previous systematic analysis of a representative set of 40 bacterial genomes produced a large collection of sequences, potentially able to fold as stem-loop structures (SLS). Computational analysis of these sequences was carried out by searching for families of repetitive nucleic acid elements sharing a common secondary structure.ResultsThe initial clustering procedure identified clusters of similar sequences in 29 genomes, corresponding to about 1% of the whole population. Sequences selected in this way have a substantially higher aptitude to fold into a stable secondary structure than the initial set. Removal of redundancies and regrouping of the selected sequences resulted in a final set of 92 families, defined by HMM analysis. 25 of them include all well-known SLS containing repeats and others reported in literature, but not analyzed in detail. The remaining 67 families have not been previously described. Two thirds of the families share a common predicted secondary structure and are located within intergenic regions.ConclusionSystematic analysis of 40 bacterial genomes revealed a large number of repeated sequence families, including known and novel ones. Their predicted structure and genomic location suggest that, even in compact bacterial genomes, a relatively large fraction of the genome consists of non-protein-coding sequences, possibly functioning at the RNA level.
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Project description:Public sequencing databases are invaluable resources to biological researchers, but assessing data veracity as well as the curation and maintenance of such large collections of data can be challenging. Genomes of eukaryotic organelles, such as chloroplasts and other plastids, are particularly susceptible to assembly errors and misrepresentations in these databases due to their close evolutionary relationships with bacteria, which may co-occur within the same environment, as can be the case when sequencing plants. Here, based on sequence similarities with bacterial genomes, we identified several suspicious chloroplast assemblies present in the National Institutes of Health (NIH) Reference Sequence (RefSeq) collection. Investigations into these chloroplast assemblies reveal examples of erroneous integration of bacterial sequences into chloroplast ribosomal RNA (rRNA) loci, often within the rRNA genes, presumably due to the high similarity between plastid and bacterial rRNAs. The bacterial lineages identified within the examined chloroplasts as the most likely source of contamination are either known associates of plants, or co-occur in the same environmental niches as the examined plants. Modifications to the methods used to process untargeted 'raw' shotgun sequencing data from whole genome sequencing efforts, such as the identification and removal of bacterial reads prior to plastome assembly, could eliminate similar errors in the future.
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