MetaPGN: a pipeline for construction and graphical visualization of annotated pangenome networks.
ABSTRACT: Pangenome analyses facilitate the interpretation of genetic diversity and evolutionary history of a taxon. However, there is an urgent and unmet need to develop new tools for advanced pangenome construction and visualization, especially for metagenomic data. Here, we present an integrated pipeline, named MetaPGN, for construction and graphical visualization of pangenome networks from either microbial genomes or metagenomes. Given either isolated genomes or metagenomic assemblies coupled with a reference genome of the targeted taxon, MetaPGN generates a pangenome in a topological network, consisting of genes (nodes) and gene-gene genomic adjacencies (edges) of which biological information can be easily updated and retrieved. MetaPGN also includes a self-developed Cytoscape plugin for layout of and interaction with the resulting pangenome network, providing an intuitive and interactive interface for full exploration of genetic diversity. We demonstrate the utility of MetaPGN by constructing Escherichia coli pangenome networks from five E. coli pathogenic strains and 760 human gut microbiomes,revealing extensive genetic diversity of E. coli within both isolates and gut microbial populations. With the ability to extract and visualize gene contents and gene-gene physical adjacencies of a specific taxon from large-scale metagenomic data, MetaPGN provides advantages in expanding pangenome analysis to uncultured microbial taxa.
Project description:Pangenomes offer detailed characterizations of core and accessory genes found in a set of closely related microbial genomes, generally by clustering genes based on sequence homology. In comparison, metagenomes facilitate highly resolved investigations of the relative distribution of microbial genomes and individual genes across environments through read recruitment analyses. Combining these complementary approaches can yield unique insights into the functional basis of microbial niche partitioning and fitness, however, advanced software solutions are lacking. Here we present an integrated analysis and visualization strategy that provides an interactive and reproducible framework to generate pangenomes and to study them in conjunction with metagenomes. To investigate its utility, we applied this strategy to a Prochlorococcus pangenome in the context of a large-scale marine metagenomic survey. The resulting Prochlorococcus metapangenome revealed remarkable differential abundance patterns between very closely related isolates that belonged to the same phylogenetic cluster and that differed by only a small number of gene clusters in the pangenome. While the relationships between these genomes based on gene clusters correlated with their environmental distribution patterns, phylogenetic analyses using marker genes or concatenated single-copy core genes did not recapitulate these patterns. The metapangenome also revealed a small set of core genes that mostly occurred in hypervariable genomic islands of the Prochlorococcus populations, which systematically lacked read recruitment from surface ocean metagenomes. Notably, these core gene clusters were all linked to sugar metabolism, suggesting potential benefits to Prochlorococcus from a high sequence diversity of sugar metabolism genes. The rapidly growing number of microbial genomes and increasing availability of environmental metagenomes provide new opportunities to investigate the functioning and the ecology of microbial populations, and metapangenomes can provide unique insights for any taxon and biome for which genomic and sufficiently deep metagenomic data are available.
Project description:Shotgun metagenomics is a low biased technology for assessing environmental microbial diversity and function. However, the requirement for a sufficient amount of DNA and the contamination of inhibitors in environmental DNA leads to difficulties in constructing a shotgun metagenomic library. We herein examined metagenomic library construction from subnanogram amounts of input environmental DNA from subarctic surface water and deep-sea sediments using two library construction kits: the KAPA Hyper Prep Kit and Nextera XT DNA Library Preparation Kit, with several modifications. The influence of chemical contaminants associated with these environmental DNA samples on library construction was also investigated. Overall, shotgun metagenomic libraries were constructed from 1 pg to 1 ng of input DNA using both kits without harsh library microbial contamination. However, the libraries constructed from 1 pg of input DNA exhibited larger biases in GC contents, k-mers, or small subunit (SSU) rRNA gene compositions than those constructed from 10 pg to 1 ng DNA. The lower limit of input DNA for low biased library construction in this study was 10 pg. Moreover, we revealed that technology-dependent biases (physical fragmentation and linker ligation vs. tagmentation) were larger than those due to the amount of input DNA.
Project description:<h4>Background</h4>The first step in understanding ecological community diversity and dynamics is quantifying community membership. An increasingly common method for doing so is through metagenomics. Because of the rapidly increasing popularity of this approach, a large number of computational tools and pipelines are available for analysing metagenomic data. However, the majority of these tools have been designed and benchmarked using highly accurate short read data (i.e. Illumina), with few studies benchmarking classification accuracy for long error-prone reads (PacBio or Oxford Nanopore). In addition, few tools have been benchmarked for non-microbial communities.<h4>Results</h4>Here we compare simulated long reads from Oxford Nanopore and Pacific Biosciences (PacBio) with high accuracy Illumina read sets to systematically investigate the effects of sequence length and taxon type on classification accuracy for metagenomic data from both microbial and non-microbial communities. We show that very generally, classification accuracy is far lower for non-microbial communities, even at low taxonomic resolution (e.g. family rather than genus). We then show that for two popular taxonomic classifiers, long reads can significantly increase classification accuracy, and this is most pronounced for non-microbial communities.<h4>Conclusions</h4>This work provides insight on the expected accuracy for metagenomic analyses for different taxonomic groups, and establishes the point at which read length becomes more important than error rate for assigning the correct taxon.
Project description:Phylogenetic diversity--patterns of phylogenetic relatedness among organisms in ecological communities--provides important insights into the mechanisms underlying community assembly. Studies that measure phylogenetic diversity in microbial communities have primarily been limited to a single marker gene approach, using the small subunit of the rRNA gene (SSU-rRNA) to quantify phylogenetic relationships among microbial taxa. In this study, we present an approach for inferring phylogenetic relationships among microorganisms based on the random metagenomic sequencing of DNA fragments. To overcome challenges caused by the fragmentary nature of metagenomic data, we leveraged fully sequenced bacterial genomes as a scaffold to enable inference of phylogenetic relationships among metagenomic sequences from multiple phylogenetic marker gene families. The resulting metagenomic phylogeny can be used to quantify the phylogenetic diversity of microbial communities based on metagenomic data sets. We applied this method to understand patterns of microbial phylogenetic diversity and community assembly along an oceanic depth gradient, and compared our findings to previous studies of this gradient using SSU-rRNA gene and metagenomic analyses. Bacterial phylogenetic diversity was highest at intermediate depths beneath the ocean surface, whereas taxonomic diversity (diversity measured by binning sequences into taxonomically similar groups) showed no relationship with depth. Phylogenetic diversity estimates based on the SSU-rRNA gene and the multi-gene metagenomic phylogeny were broadly concordant, suggesting that our approach will be applicable to other metagenomic data sets for which corresponding SSU-rRNA gene sequences are unavailable. Our approach opens up the possibility of using metagenomic data to study microbial diversity in a phylogenetic context.
Project description:The increase in available microbial genome sequences has resulted in an increase in the size of the pangenomes being analyzed. Current pangenome visualizations are not intended for the pangenome sizes possible today and new approaches are necessary in order to convert the increase in available information to increase in knowledge. As the pangenome data structure is essentially a collection of sets we explore the potential for scalable set visualization as a tool for pangenome analysis.We present a new hierarchical clustering algorithm based on set arithmetics that optimizes the intersection sizes along the branches. The intersection and union sizes along the hierarchy are visualized using a composite dendrogram and icicle plot, which, in pangenome context, shows the evolution of pangenome and core size along the evolutionary hierarchy. Outlying elements, i.e. elements whose presence pattern do not correspond with the hierarchy, can be visualized using hierarchical edge bundles. When applied to pangenome data this plot shows putative horizontal gene transfers between the genomes and can highlight relationships between genomes that is not represented by the hierarchy. We illustrate the utility of hierarchical sets by applying it to a pangenome based on 113 Escherichia and Shigella genomes and find it provides a powerful addition to pangenome analysis.The described clustering algorithm and visualizations are implemented in the hierarchicalSets R package available from CRAN ( https://cran.r-project.org/web/packages/hierarchicalSets ).email@example.com.Supplementary data are available at Bioinformatics online.
Project description:Recent progress in molecular microbial ecology has revealed that traditional culturing methods fail to represent the scope of microbial diversity in nature, since only a small proportion of viable microorganisms in a sample are recovered by culturing techniques. To develop methods to investigate the full extent of microbial diversity, we used a bacterial artificial chromosome (BAC) vector to construct libraries of genomic DNA isolated directly from soil (termed metagenomic libraries). To date, we have constructed two such libraries, which contain more than 1 Gbp of DNA. Phylogenetic analysis of 16S rRNA gene sequences recovered from one of the libraries indicates that the BAC libraries contain DNA from a wide diversity of microbial phyla, including sequences from diverse taxa such as the low-G+C, gram-positive Acidobacterium, Cytophagales, and Proteobacteria. Initial screening of the libraries in Escherichia coli identified several clones that express heterologous genes from the inserts, confirming that the BAC vector can be used to maintain, express, and analyze environmental DNA. The phenotypes expressed by these clones include antibacterial, lipase, amylase, nuclease, and hemolytic activities. Metagenomic libraries are a powerful tool for exploring soil microbial diversity, providing access to the genetic information of uncultured soil microorganisms. Such libraries will be the basis of new initiatives to conduct genomic studies that link phylogenetic and functional information about the microbiota of environments dominated by microorganisms that are refractory to cultivation.
Project description:The development of DNA sequencing methods for characterizing microbial communities has evolved rapidly over the past decades. To evaluate more traditional, as well as newer methodologies for DNA library preparation and sequencing, we compared fosmid, short-insert shotgun and 454 pyrosequencing libraries prepared from the same metagenomic DNA samples. GC content was elevated in all fosmid libraries, compared with shotgun and 454 libraries. Taxonomic composition of the different libraries suggested that this was caused by a relative underrepresentation of dominant taxonomic groups with low GC content, notably Prochlorales and the SAR11 cluster, in fosmid libraries. While these abundant taxa had a large impact on library representation, we also observed a positive correlation between taxon GC content and fosmid library representation in other low-GC taxa, suggesting a general trend. Analysis of gene category representation in different libraries indicated that the functional composition of a library was largely a reflection of its taxonomic composition, and no additional systematic biases against particular functional categories were detected at the level of sequencing depth in our samples. Another important but less predictable factor influencing the apparent taxonomic and functional library composition was the read length afforded by the different sequencing technologies. Our comparisons and analyses provide a detailed perspective on the influence of library type on the recovery of microbial taxa in metagenomic libraries and underscore the different uses and utilities of more traditional, as well as contemporary 'next-generation' DNA library construction and sequencing technologies for exploring the genomics of the natural microbial world.
Project description:Microbial organisms inhabit virtually all environments and encompass a vast biological diversity. The pangenome concept aims to facilitate an understanding of diversity within defined phylogenetic groups. Hence, pangenomes are increasingly used to characterize the strain diversity of prokaryotic species. To understand the interdependence of pangenome features (such as the number of core and accessory genes) and to study the impact of environmental and phylogenetic constraints on the evolution of conspecific strains, we computed pangenomes for 155 phylogenetically diverse species (from ten phyla) using 7,000 high-quality genomes to each of which the respective habitats were assigned. Species habitat ubiquity was associated with several pangenome features. In particular, core-genome size was more important for ubiquity than accessory genome size. In general, environmental preferences had a stronger impact on pangenome evolution than phylogenetic inertia. Environmental preferences explained up to 49% of the variance for pangenome features, compared with 18% by phylogenetic inertia. This observation was robust when the dataset was extended to 10,100 species (59 phyla). The importance of environmental preferences was further accentuated by convergent evolution of pangenome features in a given habitat type across different phylogenetic clades. For example, the soil environment promotes expansion of pangenome size, while host-associated habitats lead to its reduction. Taken together, we explored the global principles of pangenome evolution, quantified the influence of habitat, and phylogenetic inertia on the evolution of pangenomes and identified criteria governing species ubiquity and habitat specificity.
Project description:SAR86 is an abundant and ubiquitous heterotroph in the surface ocean that plays a central role in the function of marine ecosystems. We hypothesized that despite its ubiquity, different SAR86 subgroups may be endemic to specific ocean regions and functionally specialized for unique marine environments. However, the global biogeographical distributions of SAR86 genes, and the manner in which these distributions correlate with marine environments, have not been investigated. We quantified SAR86 gene content across globally distributed metagenomic samples and modeled these gene distributions as a function of 51 environmental variables. We identified five distinct clusters of genes within the SAR86 pangenome, each with a unique geographic distribution associated with specific environmental characteristics. Gene clusters are characterized by the strong taxonomic enrichment of distinct SAR86 genomes and partial assemblies, as well as differential enrichment of certain functional groups, suggesting differing functional and ecological roles of SAR86 ecotypes. We then leveraged our models and high-resolution, remote sensing-derived environmental data to predict the distributions of SAR86 gene clusters across the world's oceans, creating global maps of SAR86 ecotype distributions. Our results reveal that SAR86 exhibits previously unknown, complex biogeography, and provide a framework for exploring geographic distributions of genetic diversity from other microbial clades.