Assembly Of Microbial Genomes Research Articles

Many purported pseudogenes in bacterial genomes are bona fide genes

BackgroundMicrobial genomes are largely comprised of protein coding sequences, yet some genomes contain many pseudogenes caused by frameshifts or internal stop codons. These pseudogenes are believed to result from gene degradation during evolution but could also be technical artifacts of genome sequencing or assembly.ResultsUsing a combination of observational and experimental data, we show that many putative pseudogenes are attributable to errors that are incorporated into genomes during assembly. Within 126,564 publicly available genomes, we observed that nearly identical genomes often substantially differed in pseudogene counts. Causal inference implicated assembler, sequencing platform, and coverage as likely causative factors. Reassembly of genomes from raw reads confirmed that each variable affects the number of putative pseudogenes in an assembly. Furthermore, simulated sequencing reads corroborated our observations that the quality and quantity of raw data can significantly impact the number of pseudogenes in an assembler dependent fashion. The number of unexpected pseudogenes due to internal stops was highly correlated (R2 = 0.96) with average nucleotide identity to the ground truth genome, implying relative pseudogene counts can be used as a proxy for overall assembly correctness. Applying our method to assemblies in RefSeq resulted in rejection of 3.6% of assemblies due to significantly elevated pseudogene counts. Reassembly from real reads obtained from high coverage genomes showed considerable variability in spurious pseudogenes beyond that observed with simulated reads, reinforcing the finding that high coverage is necessary to mitigate assembly errors.ConclusionsCollectively, these results demonstrate that many pseudogenes in microbial genome assemblies are actually genes. Our results suggest that high read coverage is required for correct assembly and indicate an inflated number of pseudogenes due to internal stops is indicative of poor overall assembly quality.

Reference-based read clustering improves the de novo genome assembly of microbial strains

Constructing accurate microbial genome assemblies is necessary to understand genetic diversity in microbial genomes and its functional consequences. However, it still remains as a challenging task especially when only short-read sequencing technologies are used. Here, we present a new read-clustering algorithm, called RBRC, for improving de novo microbial genome assembly, by accurately estimating read proximity using multiple reference genomes. The performance of RBRC was confirmed by simulation-based evaluation in terms of assembly contiguity and the number of misassemblies, and was successfully applied to existing fungal and bacterial genomes by improving the quality of the assemblies without using additional sequencing data. RBRC is a very useful read-clustering algorithm that can be used (i) for generating high-quality genome assemblies of microbial strains when genome assemblies of related strains are available, and (ii) for upgrading existing microbial genome assemblies when the generation of additional sequencing data, such as long reads, is difficult.

Integrated Metabolomic-Genomic Workflows Accelerate Microbial Natural Product Discovery.

The pairing of analytical chemistry with genomic techniques represents a new wave in natural product chemistry. With an increase in the availability of sequencing and assembly of microbial genomes, interrogation into the biosynthetic capability of producers with valuable secondary metabolites is possible. However, without the development of robust, accessible, and medium to high throughput tools, the bottleneck in pairing metabolic potential and compound isolation will continue. Several innovative approaches have proven useful in the nascent stages of microbial genome-informed drug discovery. Here, we consider a number of these approaches which have led to prioritization of strain targets and have mitigated rediscovery rates. Likewise, we discuss integration of principles of comparative evolutionary studies and retrobiosynthetic predictions to better understand biosynthetic mechanistic details and link genome sequence to structure. Lastly, we discuss advances in engineering, chemistry, and molecular networking and other computational approaches that are accelerating progress in the field of omic-informed natural product drug discovery. Together, these strategies enhance the synergy between cutting edge omics, chemical characterization, and computational technologies that pitch the discovery of natural products with pharmaceutical and other potential applications to the crest of the wave where progress is ripe for rapid advances.

Single cell genome sequencing of laboratory mouse microbiota improves taxonomic and functional resolution of this model microbial community.

Laboratory mice are widely studied as models of mammalian biology, including the microbiota. However, much of the taxonomic and functional diversity of the mouse gut microbiome is missed in current metagenomic studies, because genome databases have not achieved a balanced representation of the diverse members of this ecosystem. Towards solving this problem, we used flow cytometry and low-coverage sequencing to capture the genomes of 764 single cells from the stool of three laboratory mice. From these, we generated 298 high-coverage microbial genome assemblies, which we annotated for open reading frames and phylogenetic placement. These genomes increase the gene catalog and phylogenetic breadth of the mouse microbiota, adding 135 novel species with the greatest increase in diversity to the Muribaculaceae and Bacteroidaceae families. This new diversity also improves the read mapping rate, taxonomic classifier performance, and gene detection rate of mouse stool metagenomes. The novel microbial functions revealed through our single-cell genomes highlight previously invisible pathways that may be important for life in the murine gastrointestinal tract.

Hackflex: low-cost, high-throughput, Illumina Nextera Flex library construction.

We developed a low-cost method for the production of Illumina-compatible sequencing libraries that allows up to 14 times more libraries for high-throughput Illumina sequencing to be generated for the same cost. We call this new method Hackflex. The quality of library preparation was tested by constructing libraries from Escherichia coli MG1655 genomic DNA using either Hackflex, standard Nextera Flex (recently renamed as Illumina DNA Prep) or a variation of standard Nextera Flex in which the bead-linked transposase is diluted prior to use. In order to test the library quality for genomes with a higher and a lower G+C content, library construction methods were also tested on Pseudomonas aeruginosa PAO1 and Staphylococcus aureus ATCC 25923, respectively. We demonstrated that Hackflex can produce high-quality libraries and yields a highly uniform coverage, equivalent to the standard Nextera Flex kit. We show that strongly size-selected libraries produce sufficient yield and complexity to support de novo microbial genome assembly, and that assemblies of the large-insert libraries can be much more contiguous than standard libraries without strong size selection. We introduce a new set of sample barcodes that are distinct from standard Illumina barcodes, enabling Hackflex samples to be multiplexed with samples barcoded using standard Illumina kits. Using Hackflex, we were able to achieve a per-sample reagent cost for library prep of A$7.22 (Australian dollars) (US $5.60; UK £3.87, £1=A$1.87), which is 9.87 times lower than the standard Nextera Flex protocol at advertised retail price. An additional simple modification and further simplification of the protocol by omitting the wash step enables a further price reduction to reach an overall 14-fold cost saving. This method will allow researchers to construct more libraries within a given budget, thereby yielding more data and facilitating research programmes where sequencing large numbers of libraries is beneficial.

Comparative evaluation of Nanopore polishing tools for microbial genome assembly and polishing strategies for downstream analysis

Assembling high-quality microbial genomes using only cost-effective Nanopore long-read systems such as Flongle is important to accelerate research on the microbial genome and the most critical point for this is the polishing process. In this study, we performed an evaluation based on BUSCO and Prokka gene prediction in terms of microbial genome assembly for eight state-of-the-art Nanopore polishing tools and combinations available. In the evaluation of individual tools, Homopolish, PEPPER, and Medaka demonstrated better results than others. In combination polishing, the second round Homopolish, and the PEPPER × medaka combination also showed better results than others. However, individual tools and combinations have specific limitations on usage and results. Depending on the target organism and the purpose of the downstream research, it is confirmed that there remain some difficulties in perfectly replacing the hybrid polishing carried out by the addition of a short-read. Nevertheless, through continuous improvement of the protein pores, related base-calling algorithms, and polishing tools based on improved error models, a high-quality microbial genome can be achieved using only Nanopore reads without the production of additional short-read data. The polishing strategy proposed in this study is expected to provide useful information for assembling the microbial genome using only Nanopore reads depending on the target microorganism and the purpose of the research.

Reconstruction of ancient microbial genomes from the human gut

Loss of gut microbial diversity1–6 in industrial populations is associated with chronic diseases7, underscoring the importance of studying our ancestral gut microbiome. However, relatively little is known about the composition of pre-industrial gut microbiomes. Here we performed a large-scale de novo assembly of microbial genomes from palaeofaeces. From eight authenticated human palaeofaeces samples (1,000–2,000 years old) with well-preserved DNA from southwestern USA and Mexico, we reconstructed 498 medium- and high-quality microbial genomes. Among the 181 genomes with the strongest evidence of being ancient and of human gut origin, 39% represent previously undescribed species-level genome bins. Tip dating suggests an approximate diversification timeline for the key human symbiont Methanobrevibacter smithii. In comparison to 789 present-day human gut microbiome samples from eight countries, the palaeofaeces samples are more similar to non-industrialized than industrialized human gut microbiomes. Functional profiling of the palaeofaeces samples reveals a markedly lower abundance of antibiotic-resistance and mucin-degrading genes, as well as enrichment of mobile genetic elements relative to industrial gut microbiomes. This study facilitates the discovery and characterization of previously undescribed gut microorganisms from ancient microbiomes and the investigation of the evolutionary history of the human gut microbiota through genome reconstruction from palaeofaeces.

Evaluation of nine popular de novo assemblers in microbial genome assembly

Next generation sequencing (NGS) technologies are revolutionizing biology, with Illumina being the most popular NGS platform. Short read assembly is a critical part of most genome studies using NGS. Hence, in this study, the performance of nine well-known assemblers was evaluated in the assembly of seven different microbial genomes. Effect of different read coverage and k-mer parameters on the quality of the assembly were also evaluated on both simulated and actual read datasets. Our results show that the performance of assemblers on real and simulated datasets could be significantly different, mainly because of coverage bias. According to outputs on actual read datasets, for all studied read coverages (of 7×, 25× and 100×), SPAdes and IDBA-UD clearly outperformed other assemblers based on NGA50 and accuracy metrics. Velvet is the most conservative assembler with the lowest NGA50 and error rate.

A world of opportunities with nanopore sequencing.

Oxford Nanopore Technologies' MinION sequencer was launched in pre-release form in 2014 and represents an exciting new sequencing paradigm. The device offers multi-kilobase reads and a streamed mode of operation that allows processing of reads as they are generated. Crucially, it is an extremely compact device that is powered from the USB port of a laptop computer, enabling it to be taken out of the lab and facilitating previously impossible in-field sequencing experiments to be undertaken. Many of the initial publications concerning the platform focused on provision of tools to access and analyse the new sequence formats and then demonstrating the assembly of microbial genomes. More recently, as throughput and accuracy have increased, it has been possible to begin work involving more complex genomes and metagenomes. With the release of the high-throughput GridION X5 and PromethION platforms, the sequencing of large genomes will become more cost efficient, and enable the leveraging of extremely long (>100 kb) reads for resolution of complex genomic structures. This review provides a brief overview of nanopore sequencing technology, describes the growing range of nanopore bioinformatics tools, and highlights some of the most influential publications that have emerged over the last 2 years. Finally, we look to the future and the potential the platform has to disrupt work in human, microbiome, and plant genomics.

A Case Study into Microbial Genome Assembly Gap Sequences and Finishing Strategies.

This study characterized regions of DNA which remained unassembled by either PacBio and Illumina sequencing technologies for seven bacterial genomes. Two genomes were manually finished using bioinformatics and PCR/Sanger sequencing approaches and regions not assembled by automated software were analyzed. Gaps present within Illumina assemblies mostly correspond to repetitive DNA regions such as multiple rRNA operon sequences. PacBio gap sequences were evaluated for several properties such as GC content, read coverage, gap length, ability to form strong secondary structures, and corresponding annotations. Our hypothesis that strong secondary DNA structures blocked DNA polymerases and contributed to gap sequences was not accepted. PacBio assemblies had few limitations overall and gaps were explained as cumulative effect of lower than average sequence coverage and repetitive sequences at contig termini. An important aspect of the present study is the compilation of biological features that interfered with assembly and included active transposons, multiple plasmid sequences, phage DNA integration, and large sequence duplication. Our targeted genome finishing approach and systematic evaluation of the unassembled DNA will be useful for others looking to close, finish, and polish microbial genome sequences.

Microbiome profiling in fresh-cut products

Fresh-cut fruits and vegetables are highly perishable and minimally processed products. One of the main issues related to their safety is the potential microbial contamination and spoilage that can significantly reduce their shelf-life. To tackle this problem, the complete identification of fresh-cut product microbiota is of primary importance. Indeed, despite the huge amount of metagenomics data generated in the last decade to profile food microbiome, a specific knowledge about fresh fruits and vegetables microbiome is still scarce. Production of additional food-related data, such as improved microbial genome assemblies and increased number of microbial reference genome sequences, are needed to perform a robust and reliable analysis of food-related metagenomics data. These data can be successfully applied to model the contribution of individual microorganisms for the quality and safety of fresh-cut products.

Microbial metagenomics in the Baltic Sea: Recent advancements and prospects for environmental monitoring.

Metagenomics refers to the analysis of DNA from a whole community. Metagenomic sequencing of environmental DNA has greatly improved our knowledge of the identity and function of microorganisms in aquatic, terrestrial, and human biomes. Although open oceans have been the primary focus of studies on aquatic microbes, coastal and brackish ecosystems are now being surveyed. Here, we review so far published studies on microbes in the Baltic Sea, one of the world’s largest brackish water bodies, using high throughput sequencing of environmental DNA and RNA. Collectively the data illustrate that Baltic Sea microbes are unique and highly diverse, and well adapted to this brackish-water ecosystem, findings that represent a novel base-line knowledge necessary for monitoring purposes and a sustainable management. More specifically, the data relate to environmental drivers for microbial community composition and function, assessments of the microbial biodiversity, adaptations and role of microbes in the nitrogen cycle, and microbial genome assembly from metagenomic sequences. With these discoveries as background, prospects of using metagenomics for Baltic Sea environmental monitoring are discussed.

An ensemble strategy that significantly improves de novo assembly of microbial genomes from metagenomic next-generation sequencing data.

Next-generation sequencing (NGS) approaches rapidly produce millions to billions of short reads, which allow pathogen detection and discovery in human clinical, animal and environmental samples. A major limitation of sequence homology-based identification for highly divergent microorganisms is the short length of reads generated by most highly parallel sequencing technologies. Short reads require a high level of sequence similarities to annotated genes to confidently predict gene function or homology. Such recognition of highly divergent homologues can be improved by reference-free (de novo) assembly of short overlapping sequence reads into larger contigs. We describe an ensemble strategy that integrates the sequential use of various de Bruijn graph and overlap-layout-consensus assemblers with a novel partitioned sub-assembly approach. We also proposed new quality metrics that are suitable for evaluating metagenome de novo assembly. We demonstrate that this new ensemble strategy tested using in silico spike-in, clinical and environmental NGS datasets achieved significantly better contigs than current approaches.

Update on RefSeq microbial genomes resources.

NCBI RefSeq genome collection http://www.ncbi.nlm.nih.gov/genome represents all three major domains of life: Eukarya, Bacteria and Archaea as well as Viruses. Prokaryotic genome sequences are the most rapidly growing part of the collection. During the year of 2014 more than 10 000 microbial genome assemblies have been publicly released bringing the total number of prokaryotic genomes close to 30 000. We continue to improve the quality and usability of the microbial genome resources by providing easy access to the data and the results of the pre-computed analysis, and improving analysis and visualization tools. A number of improvements have been incorporated into the Prokaryotic Genome Annotation Pipeline. Several new features have been added to RefSeq prokaryotic genomes data processing pipeline including the calculation of genome groups (clades) and the optimization of protein clusters generation using pan-genome approach.

A5-miseq: an updated pipeline to assemble microbial genomes from Illumina MiSeq data

Open-source bacterial genome assembly remains inaccessible to many biologists because of its complexity. Few software solutions exist that are capable of automating all steps in the process of de novo genome assembly from Illumina data. A5-miseq can produce high-quality microbial genome assemblies on a laptop computer without any parameter tuning. A5-miseq does this by automating the process of adapter trimming, quality filtering, error correction, contig and scaffold generation and detection of misassemblies. Unlike the original A5 pipeline, A5-miseq can use long reads from the Illumina MiSeq, use read pairing information during contig generation and includes several improvements to read trimming. Together, these changes result in substantially improved assemblies that recover a more complete set of reference genes than previous methods. A5-miseq is licensed under the GPL open-source license. Source code and precompiled binaries for Mac OS X 10.6+ and Linux 2.6.15+ are available from http://sourceforge.net/projects/ngopt aaron.darling@uts.edu.au Supplementary data are available at Bioinformatics online.

Estimating the composition of species in metagenomes by clustering of next-generation read sequences

Faster and cheaper sequencing technologies together with the ability to sequence uncultured microbes collected from any environment present us an opportunity to distill meaningful information from the millions of new genomic sequences from environmental samples, called metagenome. Contrary to conventional cultured microbes, however, the metagenomic data is extremely heterogeneous and noisy. Therefore the separation of the sets of sequenced genomic fragments that belong to different microbes is essential for successful assembly of microbial genomes. In this paper, we present a novel clustering method for a given metagenomic dataset. The metagenomic dataset has some distinguished features because (i) it is possible that similar sequence patterns may exist in different species and (ii) each species has different number of individuals in the given metagenomic dataset. Our method overcomes these obstacles by using the Gaussian mixture model and analysis of mixture profiles, and taking advantage of genomic signatures extracted from the metagenomic dataset. Unlike conventional clustering methods where clusters are discovered through global similarities of data instances, our method builds clusters by combining the data instances sharing local similarities captured by mixture analysis. By considering shared mixture components, our method is able to create clusters of genomic sequences although they are globally distinct each other. We applied our method to an artificial metagenomic dataset comprised of simulated 47 million reads from 25 real microbial genomes, and analyzed the resulting clusters in terms of the number of clusters, the number of participating species and dominant species in each cluster. Even though our approach cannot address all challenges in the field of metagenome sequence clustering, we believe that out method can contribute to take a step forward to achieve the goals.

Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes.

Most current approaches for analyzing metagenomic data rely on comparisons to reference genomes, but the microbial diversity of many environments extends far beyond what is covered by reference databases. De novo segregation of complex metagenomic data into specific biological entities, such as particular bacterial strains or viruses, remains a largely unsolved problem. Here we present a method, based on binning co-abundant genes across a series of metagenomic samples, that enables comprehensive discovery of new microbial organisms, viruses and co-inherited genetic entities and aids assembly of microbial genomes without the need for reference sequences. We demonstrate the method on data from 396 human gut microbiome samples and identify 7,381 co-abundance gene groups (CAGs), including 741 metagenomic species (MGS). We use these to assemble 238 high-quality microbial genomes and identify affiliations between MGS and hundreds of viruses or genetic entities. Our method provides the means for comprehensive profiling of the diversity within complex metagenomic samples.

Nonhybrid, finished microbial genome assemblies from long-read SMRT sequencing data

We present a hierarchical genome-assembly process (HGAP) for high-quality de novo microbial genome assemblies using only a single, long-insert shotgun DNA library in conjunction with Single Molecule, Real-Time (SMRT) DNA sequencing. Our method uses the longest reads as seeds to recruit all other reads for construction of highly accurate preassembled reads through a directed acyclic graph-based consensus procedure, which we follow with assembly using off-the-shelf long-read assemblers. In contrast to hybrid approaches, HGAP does not require highly accurate raw reads for error correction. We demonstrate efficient genome assembly for several microorganisms using as few as three SMRT Cell zero-mode waveguide arrays of sequencing and for BACs using just one SMRT Cell. Long repeat regions can be successfully resolved with this workflow. We also describe a consensus algorithm that incorporates SMRT sequencing primary quality values to produce de novo genome sequence exceeding 99.999% accuracy.

Reducing assembly complexity of microbial genomes with single-molecule sequencing

BackgroundThe short reads output by first- and second-generation DNA sequencing instruments cannot completely reconstruct microbial chromosomes. Therefore, most genomes have been left unfinished due to the significant resources required to manually close gaps in draft assemblies. Third-generation, single-molecule sequencing addresses this problem by greatly increasing sequencing read length, which simplifies the assembly problem.ResultsTo measure the benefit of single-molecule sequencing on microbial genome assembly, we sequenced and assembled the genomes of six bacteria and analyzed the repeat complexity of 2,267 complete bacteria and archaea. Our results indicate that the majority of known bacterial and archaeal genomes can be assembled without gaps, at finished-grade quality, using a single PacBio RS sequencing library. These single-library assemblies are also more accurate than typical short-read assemblies and hybrid assemblies of short and long reads.ConclusionsAutomated assembly of long, single-molecule sequencing data reduces the cost of microbial finishing to $1,000 for most genomes, and future advances in this technology are expected to drive the cost lower. This is expected to increase the number of completed genomes, improve the quality of microbial genome databases, and enable high-fidelity, population-scale studies of pan-genomes and chromosomal organization.

The Fast Changing Landscape of Sequencing Technologies and Their Impact on Microbial Genome Assemblies and Annotation

BackgroundThe emergence of next generation sequencing (NGS) has provided the means for rapid and high throughput sequencing and data generation at low cost, while concomitantly creating a new set of challenges. The number of available assembled microbial genomes continues to grow rapidly and their quality reflects the quality of the sequencing technology used, but also of the analysis software employed for assembly and annotation.Methodology/Principal FindingsIn this work, we have explored the quality of the microbial draft genomes across various sequencing technologies. We have compared the draft and finished assemblies of 133 microbial genomes sequenced at the Department of Energy-Joint Genome Institute and finished at the Los Alamos National Laboratory using a variety of combinations of sequencing technologies, reflecting the transition of the institute from Sanger-based sequencing platforms to NGS platforms. The quality of the public assemblies and of the associated gene annotations was evaluated using various metrics. Results obtained with the different sequencing technologies, as well as their effects on downstream processes, were analyzed. Our results demonstrate that the Illumina HiSeq 2000 sequencing system, the primary sequencing technology currently used for de novo genome sequencing and assembly at JGI, has various advantages in terms of total sequence throughput and cost, but it also introduces challenges for the downstream analyses. In all cases assembly results although on average are of high quality, need to be viewed critically and consider sources of errors in them prior to analysis.ConclusionThese data follow the evolution of microbial sequencing and downstream processing at the JGI from draft genome sequences with large gaps corresponding to missing genes of significant biological role to assemblies with multiple small gaps (Illumina) and finally to assemblies that generate almost complete genomes (Illumina+PacBio).