Metagenomic analysis reveals the contribution of anaerobic methanotroph-1b in the oxidation of methane at the Ulleung Basin,East Sea of Korea

We have previously identified a sulfate methane transition zone (SMTZ) within the methane hydrate-bearing sediment in the Ulleung Basin, East Sea of Korea, and the presence of ANME-1b group in the sediment has been shown by phylogenetic analysis of a 16S rRNA gene. Herein, we describe taxonomic and functional profiling in the SMTZ sample by metagenomic analysis, comparing with that of surface sediment. Metagenomic sequences of 115 Mbp and 252 Mbp were obtained from SMTZ and surface sediments, respectively. The taxonomic profiling using BLASTX against the SEED within MG-RAST showed the prevalence of methanogens (19.1%), such as Methanosarcinales (12.0%) and Methanomicrobiales (4.1%) predominated within the SMTZ metagenome. A number of 185,200 SMTZ reads (38.9%) and 438,484 surface reads (62.5%) were assigned to functional categories, and methanogenesis-related reads were statistically significantly overrepresented in the SMTZ metagenome. However, the mapping analysis of metagenome reads to the reference genomes, most of the sequences of the SMTZ metagenome were mapped to ANME-1 draft genomes, rather than those of methanogens. Furthermore, the two copies of the methyl-coenzyme M reductase gene (mcrA) segments of the SMTZ metagenome were clustered with ANME-1b in the phylogenetic cluster. These results indicate that ANME-1b reads were miss-annotated to methanogens due to limitation of database. Many of key genes necessary for reverse methanogenesis were present in the SMTZ metagenome, except for N⁵,N¹⁰-methenyl-H₄MPT reductase (mer) and CoB-CoM heterodisulfide reductase subunits D and E (hdrDE). These data suggest that the ANME-1b represents the primary player the anaerobic methane oxidation in the SMTZ, of the methane hydrate-bearing sediment at the Ulleung Basin, East Sea of Korea.     

GRASPx: efficient homolog-search of short peptide metagenome database through simultaneous alignment and assembly

Background

Metagenomics is a cultivation-independent approach that enables the study of the genomic composition of microbes present in an environment. Metagenomic samples are routinely sequenced using next-generation sequencing technologies that generate short nucleotide reads. Proteins identified from these reads are mostly of partial length. On the other hand, de novo assembly of a large metagenomic dataset is computationally demanding and the assembled contigs are often fragmented, resulting in the identification of protein sequences that are also of partial length and incomplete. Annotation of an incomplete protein sequence often proceeds by identifying its homologs in a database of reference sequences. Identifying the homologs of incomplete sequences is a challenge and can result in substandard annotation of proteins from metagenomic datasets. To address this problem, we recently developed a homology detection algorithm named GRASP (Guided Reference-based Assembly of Short Peptides) that identifies the homologs of a given reference protein sequence in a database of short peptide metagenomic sequences. GRASP was developed to implement a simultaneous alignment and assembly algorithm for annotation of short peptides identified on metagenomic reads. The program achieves significantly improved recall rate at the cost of computational efficiency. In this article, we adopted three techniques to speed up the original version of GRASP, including the pre-construction of extension links, local assembly of individual seeds, and the implementation of query-level parallelism.

Results

The resulting new program, GRASPx, achieves >30X speedup compared to its predecessor GRASP. At the same time, we show that the performance of GRASPx is consistent with that of GRASP, and that both of them significantly outperform other popular homology-search tools including the BLAST and FASTA suites. GRASPx was also applied to a human saliva metagenome dataset and shows superior performance for both recall and precision rates.

Conclusions

In this article we present GRASPx, a fast and accurate homology-search program implementing a simultaneous alignment and assembly framework. GRASPx can be used for more comprehensive and accurate annotation of short peptides. GRASPx is freely available at http://graspx.sourceforge.net/.

     

Summarizing Specific Profiles in Illumina Sequencing from Whole-Genome Amplified DNA

Advances in both high-throughput sequencing and whole-genome amplification (WGA) protocols have allowed genomes to be sequenced from femtograms of DNA, for example from individual cells or from precious clinical and archived samples. Using the highly curated Caenorhabditis elegans genome as a reference, we have sequenced and identified errors and biases associated with Illumina library construction, library insert size, different WGA methods and genome features such as GC bias and simple repeat content. Detailed analysis of the reads from amplified libraries revealed characteristics suggesting that majority of amplified fragment ends are identical but inverted versions of each other. Read coverage in amplified libraries is correlated with both tandem and inverted repeat content, while GC content only influences sequencing in long-insert libraries. Nevertheless, single nucleotide polymorphism (SNP) calls and assembly metrics from reads in amplified libraries show comparable results with unamplified libraries. To utilize the full potential of WGA to reveal the real biological interest, this article highlights the importance of recognizing additional sources of errors from amplified sequence reads and discusses the potential implications in downstream analyses.     

CLAME: a new alignment-based binning algorithm allows the genomic description of a novel Xanthomonadaceae from the Colombian Andes

Background

Hot spring bacteria have unique biological adaptations to survive the extreme conditions of these environments; these bacteria produce thermostable enzymes that can be used in biotechnological and industrial applications. However, sequencing these bacteria is complex, since it is not possible to culture them. As an alternative, genome shotgun sequencing of whole microbial communities can be used. The problem is that the classification of sequences within a metagenomic dataset is very challenging particularly when they include unknown microorganisms since they lack genomic reference. We failed to recover a bacterium genome from a hot spring metagenome using the available software tools, so we develop a new tool that allowed us to recover most of this genome.

Results

We present a proteobacteria draft genome reconstructed from a Colombian’s Andes hot spring metagenome. The genome seems to be from a new lineage within the family Rhodanobacteraceae of the class Gammaproteobacteria, closely related to the genus Dokdonella. We were able to generate this genome thanks to CLAME. CLAME, from Spanish “CLAsificador MEtagenomico”, is a tool to group reads in bins. We show that most reads from each bin belong to a single chromosome. CLAME is very effective recovering most of the reads belonging to the predominant species within a metagenome.

Conclusions

We developed a tool that can be used to extract genomes (or parts of them) from a complex metagenome.

     

Metagenomics: Read Length Matters

Obtaining an unbiased view of the phylogenetic composition and functional diversity within a microbial community is one central objective of metagenomic analysis. New technologies, such as 454 pyrosequencing, have dramatically reduced sequencing costs, to a level where metagenomic analysis may become a viable alternative to more-focused assessments of the phylogenetic (e.g., 16S rRNA genes) and functional diversity of microbial communities. To determine whether the short (~100 to 200 bp) sequence reads obtained from pyrosequencing are appropriate for the phylogenetic and functional characterization of microbial communities, the results of BLAST and COG analyses were compared for long (~750 bp) and randomly derived short reads from each of two microbial and one virioplankton metagenome libraries. Overall, BLASTX searches against the GenBank nr database found far fewer homologs within the short-sequence libraries. This was especially pronounced for a Chesapeake Bay virioplankton metagenome library. Increasing the short-read sampling depth or the length of derived short reads (up to 400 bp) did not completely resolve the discrepancy in BLASTX homolog detection. Only in cases where the long-read sequence had a close homolog (low BLAST E-score) did the derived short-read sequence also find a significant homolog. Thus, more-distant homologs of microbial and viral genes are not detected by short-read sequences. Among COG hits, derived short reads sampled at a depth of two short reads per long read missed up to 72% of the COG hits found using long reads. Noting the current limitation in computational approaches for the analysis of short sequences, the use of short-read-length libraries does not appear to be an appropriate tool for the metagenomic characterization of microbial communities.     

Metagenomic microbial community profiling of Unnai hot spring by Ion-Torrent based shotgun sequencing

This is the first report on depicting the pioneering microbiota of Unnai hot spring using shotgun metagenome sequencing approach. Community analysis encompassed a total of 688,059 sequences with the total size 125.31 Mbp and 46% G + C content. Sequencing metagenome reported about 992 species belonged to 40 different phyla dominated by Firmicutes (97.49%), Proteobacteria (1.36%), and Actinobacteria (0.31%). In functional analysis, Non-Supervised Orthologous Groups (NOG) annotation revealed the predominance of poorly characterized reads (82.79%). Moreover, the subsystem classification displayed 19% genes assigned to carbohydrates metabolism, 12% genes allocated to clustering-based subsystems, 10% genes belonged to amino acids and its derivatives. The result suggests the huge bacterial diversity which will be useful for further characterizing the economically important bacteria for biotechnological applications.     

MEGAN Community Edition - Interactive Exploration and Analysis of Large-Scale Microbiome Sequencing Data

There is increasing interest in employing shotgun sequencing, rather than amplicon sequencing, to analyze microbiome samples. Typical projects may involve hundreds of samples and billions of sequencing reads. The comparison of such samples against a protein reference database generates billions of alignments and the analysis of such data is computationally challenging. To address this, we have substantially rewritten and extended our widely-used microbiome analysis tool MEGAN so as to facilitate the interactive analysis of the taxonomic and functional content of very large microbiome datasets. Other new features include a functional classifier called InterPro2GO, gene-centric read assembly, principal coordinate analysis of taxonomy and function, and support for metadata. The new program is called MEGAN Community Edition (CE) and is open source. By integrating MEGAN CE with our high-throughput DNA-to-protein alignment tool DIAMOND and by providing a new program MeganServer that allows access to metagenome analysis files hosted on a server, we provide a straightforward, yet powerful and complete pipeline for the analysis of metagenome shotgun sequences. We illustrate how to perform a full-scale computational analysis of a metagenomic sequencing project, involving 12 samples and 800 million reads, in less than three days on a single server. All source code is available here: https://github.com/danielhuson/megan-ce     

MALINA: a web service for visual analytics of human gut microbiota whole-genome metagenomic reads

MALINA is a web service for bioinformatic analysis of whole-genome metagenomic data obtained from human gut microbiota sequencing. As input data, it accepts metagenomic reads of various sequencing technologies, including long reads (such as Sanger and 454 sequencing) and next-generation (including SOLiD and Illumina). It is the first metagenomic web service that is capable of processing SOLiD color-space reads, to authors’ knowledge. The web service allows phylogenetic and functional profiling of metagenomic samples using coverage depth resulting from the alignment of the reads to the catalogue of reference sequences which are built into the pipeline and contain prevalent microbial genomes and genes of human gut microbiota. The obtained metagenomic composition vectors are processed by the statistical analysis and visualization module containing methods for clustering, dimension reduction and group comparison. Additionally, the MALINA database includes vectors of bacterial and functional composition for human gut microbiota samples from a large number of existing studies allowing their comparative analysis together with user samples, namely datasets from Russian Metagenome project, MetaHIT and Human Microbiome Project (downloaded fromhttp://hmpdacc.org). MALINA is made freely available on the web athttp://malina.metagenome.ru. The website is implemented in JavaScript (using Ext JS), Microsoft .NET Framework, MS SQL, Python, with all major browsers supported.     

MetaSim: a sequencing simulator for genomics and metagenomics

Background

The new research field of metagenomics is providing exciting insights into various, previously unclassified ecological systems. Next-generation sequencing technologies are producing a rapid increase of environmental data in public databases. There is great need for specialized software solutions and statistical methods for dealing with complex metagenome data sets.

Methodology/Principal Findings

To facilitate the development and improvement of metagenomic tools and the planning of metagenomic projects, we introduce a sequencing simulator called MetaSim. Our software can be used to generate collections of synthetic reads that reflect the diverse taxonomical composition of typical metagenome data sets. Based on a database of given genomes, the program allows the user to design a metagenome by specifying the number of genomes present at different levels of the NCBI taxonomy, and then to collect reads from the metagenome using a simulation of a number of different sequencing technologies. A population sampler optionally produces evolved sequences based on source genomes and a given evolutionary tree.

Conclusions/Significance

MetaSim allows the user to simulate individual read datasets that can be used as standardized test scenarios for planning sequencing projects or for benchmarking metagenomic software.     

Amplification of uncultured single-stranded DNA viruses from rice paddy soil

Viruses are known to be the most numerous biological entities in soil; however, little is known about their diversity in this environment. In order to explore the genetic diversity of soil viruses, we isolated viruses by centrifugation and sequential filtration before performing a metagenomic investigation. We adopted multiple-displacement amplification (MDA), an isothermal whole-genome amplification method with phi29 polymerase and random hexamers, to amplify viral DNA and construct clone libraries for metagenome sequencing. By the MDA method, the diversity of both single-stranded DNA (ssDNA) viruses and double-stranded DNA viruses could be investigated at the same time. On the contrary, by eliminating the denaturing step in the MDA reaction, only ssDNA viral diversity could be explored selectively. Irrespective of the denaturing step, more than 60% of the soil metagenome sequences did not show significant hits (E-value criterion, 0.001) with previously reported viral sequences. Those hits that were considered to be significant were also distantly related to known ssDNA viruses (average amino acid similarity, approximately 34%). Phylogenetic analysis showed that replication-related proteins (which were the most frequently detected proteins) related to those of ssDNA viruses obtained from the metagenomic sequences were diverse and novel. Putative circular genome components of ssDNA viruses that are unrelated to known viruses were assembled from the metagenomic sequences. In conclusion, ssDNA viral diversity in soil is more complex than previously thought. Soil is therefore a rich pool of previously unknown ssDNA viruses.     

VIROME: a standard operating procedure for analysis of viral metagenome sequences

One consistent finding among studies using shotgun metagenomics to analyze whole viral communities is that most viral sequences show no significant homology to known sequences. Thus, bioinformatic analyses based on sequence collections such as GenBank nr, which are largely comprised of sequences from known organisms, tend to ignore a majority of sequences within most shotgun viral metagenome libraries. Here we describe a bioinformatic pipeline, the Viral Informatics Resource for Metagenome Exploration (VIROME), that emphasizes the classification of viral metagenome sequences (predicted open-reading frames) based on homology search results against both known and environmental sequences. Functional and taxonomic information is derived from five annotated sequence databases which are linked to the UniRef 100 database. Environmental classifications are obtained from hits against a custom database, MetaGenomes On-Line, which contains 49 million predicted environmental peptides. Each predicted viral metagenomic ORF run through the VIROME pipeline is placed into one of seven ORF classes, thus, every sequence receives a meaningful annotation. Additionally, the pipeline includes quality control measures to remove contaminating and poor quality sequence and assesses the potential amount of cellular DNA contamination in a viral metagenome library by screening for rRNA genes. Access to the VIROME pipeline and analysis results are provided through a web-application interface that is dynamically linked to a relational back-end database. The VIROME web-application interface is designed to allow users flexibility in retrieving sequences (reads, ORFs, predicted peptides) and search results for focused secondary analyses.     

Comparative Analysis of Functional Metagenomic Annotation and the Mappability of Short Reads

To assess the functional capacities of microbial communities, including those inhabiting the human body, shotgun metagenomic reads are often aligned to a database of known genes. Such homology-based annotation practices critically rely on the assumption that short reads can map to orthologous genes of similar function. This assumption, however, and the various factors that impact short read annotation, have not been systematically evaluated. To address this challenge, we generated an extremely large database of simulated reads (totaling 15.9 Gb), spanning over 500,000 microbial genes and 170 curated genomes and including, for many genomes, every possible read of a given length. We annotated each read using common metagenomic protocols, fully characterizing the effect of read length, sequencing error, phylogeny, database coverage, and mapping parameters. We additionally rigorously quantified gene-, genome-, and protocol-specific annotation biases. Overall, our findings provide a first comprehensive evaluation of the capabilities and limitations of functional metagenomic annotation, providing crucial goal-specific best-practice guidelines to inform future metagenomic research.     

Random Whole Metagenomic Sequencing for Forensic Discrimination of Soils

Here we assess the ability of random whole metagenomic sequencing approaches to discriminate between similar soils from two geographically distinct urban sites for application in forensic science. Repeat samples from two parklands in residential areas separated by approximately 3 km were collected and the DNA was extracted. Shotgun, whole genome amplification (WGA) and single arbitrarily primed DNA amplification (AP-PCR) based sequencing techniques were then used to generate soil metagenomic profiles. Full and subsampled metagenomic datasets were then annotated against M5NR/M5RNA (taxonomic classification) and SEED Subsystems (metabolic classification) databases. Further comparative analyses were performed using a number of statistical tools including: hierarchical agglomerative clustering (CLUSTER); similarity profile analysis (SIMPROF); non-metric multidimensional scaling (NMDS); and canonical analysis of principal coordinates (CAP) at all major levels of taxonomic and metabolic classification. Our data showed that shotgun and WGA-based approaches generated highly similar metagenomic profiles for the soil samples such that the soil samples could not be distinguished accurately. An AP-PCR based approach was shown to be successful at obtaining reproducible site-specific metagenomic DNA profiles, which in turn were employed for successful discrimination of visually similar soil samples collected from two different locations.     

Metagenomic Sequencing of an In Vitro-Simulated Microbial Community

Background

Microbial life dominates the earth, but many species are difficult or even impossible to study under laboratory conditions. Sequencing DNA directly from the environment, a technique commonly referred to as metagenomics, is an important tool for cataloging microbial life. This culture-independent approach involves collecting samples that include microbes in them, extracting DNA from the samples, and sequencing the DNA. A sample may contain many different microorganisms, macroorganisms, and even free-floating environmental DNA. A fundamental challenge in metagenomics has been estimating the abundance of organisms in a sample based on the frequency with which the organism''s DNA was observed in reads generated via DNA sequencing.

Methodology/Principal Findings

We created mixtures of ten microbial species for which genome sequences are known. Each mixture contained an equal number of cells of each species. We then extracted DNA from the mixtures, sequenced the DNA, and measured the frequency with which genomic regions from each organism was observed in the sequenced DNA. We found that the observed frequency of reads mapping to each organism did not reflect the equal numbers of cells that were known to be included in each mixture. The relative organism abundances varied significantly depending on the DNA extraction and sequencing protocol utilized.

Conclusions/Significance

We describe a new data resource for measuring the accuracy of metagenomic binning methods, created by in vitro-simulation of a metagenomic community. Our in vitro simulation can be used to complement previous in silico benchmark studies. In constructing a synthetic community and sequencing its metagenome, we encountered several sources of observation bias that likely affect most metagenomic experiments to date and present challenges for comparative metagenomic studies. DNA preparation methods have a particularly profound effect in our study, implying that samples prepared with different protocols are not suitable for comparative metagenomics.     

Comparative Metagenomic Profiling of Viromes Associated with Four Common Mosquito Species in China

Vast viruses are thought to be associated with mosquitoes. Anopheles sinensis, Armigeres subalbatus, Culex quinquefasciatus, and Culex tritaeniorhynchus are very common mosquito species in China, and whether the virome structure in each species is species-specific has not been evaluated. In this study, a total of 2222 mosquitoes were collected from the same geographic location, and RNAs were sequenced using the Illumina Miseq platform. After querying to the Refseq database, a total of 3,435,781, 2,223,509, 5,727,523, and 6,387,867 paired-end reads were classified under viral sequences from An. sinensis, Ar. subalbatus, Cx. quinquefasciatus, and Cx. tritaeniorhynchus, respectively, with the highest prevalence of virus-associated reads being observed in Cx. quinquefasciatus. The metagenomic comparison analysis showed that the virus-related reads were distributed across 26 virus families, together with an unclassified group of viruses. Anelloviridae, Circoviridae, Genomoviridae, Iridoviridae, Mesoniviridae, Microviridae, Myoviridae, Parvoviridae, Phenuiviridae, and Podoviridae were the top ten significantly different viral families among the four species. Further analysis reveals that the virome is species-specific in four mosquito samples, and several viral sequences which maybe belong to novel viruses are discovered for the first time in those mosquitoes. This investigation provides a basis for a comprehensive knowledge on the mosquito virome status in China.     

Combining gene prediction methods to improve metagenomic gene annotation

Background  

Traditional gene annotation methods rely on characteristics that may not be available in short reads generated from next generation technology, resulting in suboptimal performance for metagenomic (environmental) samples. Therefore, in recent years, new programs have been developed that optimize performance on short reads. In this work, we benchmark three metagenomic gene prediction programs and combine their predictions to improve metagenomic read gene annotation.     

Inference of Microbial Recombination Rates from Metagenomic Data

Metagenomic sequencing projects from environments dominated by a small number of species produce genome-wide population samples. We present a two-site composite likelihood estimator of the scaled recombination rate, ρ = 2N_ec, that operates on metagenomic assemblies in which each sequenced fragment derives from a different individual. This new estimator properly accounts for sequencing error, as quantified by per-base quality scores, and missing data, as inferred from the placement of reads in a metagenomic assembly. We apply our estimator to data from a sludge metagenome project to demonstrate how this method will elucidate the rates of exchange of genetic material in natural microbial populations. Surprisingly, for a fixed amount of sequencing, this estimator has lower variance than similar methods that operate on more traditional population genetic samples of comparable size. In addition, we can infer variation in recombination rate across the genome because metagenomic projects sample genetic diversity genome-wide, not just at particular loci. The method itself makes no assumption specific to microbial populations, opening the door for application to any mixed population sample where the number of individuals sampled is much greater than the number of fragments sequenced.     

CloudLCA: finding the lowest common ancestor in metagenome analysis using cloud computing

Estimating taxonomic content constitutes a key problem in metagenomic sequencing data analysis. However, extracting such content from high-throughput data of next-generation sequencing is very time-consuming with the currently available software. Here, we present CloudLCA, a parallel LCA algorithm that significantly improves the efficiency of determining taxonomic composition in metagenomic data analysis. Results show that CloudLCA (1) has a running time nearly linear with the increase of dataset magnitude, (2) displays linear speedup as the number of processors grows, especially for large datasets, and (3) reaches a speed of nearly 215 million reads each minute on a cluster with ten thin nodes. In comparison with MEGAN, a well-known metagenome analyzer, the speed of CloudLCA is up to 5 more times faster, and its peak memory usage is approximately 18.5% that of MEGAN, running on a fat node. CloudLCA can be run on one multiprocessor node or a cluster. It is expected to be part of MEGAN to accelerate analyzing reads, with the same output generated as MEGAN, which can be import into MEGAN in a direct way to finish the following analysis. Moreover, CloudLCA is a universal solution for finding the lowest common ancestor, and it can be applied in other fields requiring an LCA algorithm.