Analysis Of Metagenomic Datasets Research Articles

Recent Advances in Metagenomic Approaches, Applications, and Challenge.

Advances in metagenomics analysis with the advent of next-generation sequencing have extended our knowledge of microbial communities as compared to conventional techniques providing advanced approach to identify novel and uncultivable microorganisms based on their genetic information derived from a particular environment. Shotgun metagenomics involves investigating the DNA of the entire community without the requirement of PCR amplification. It provides access to study all genes present in the sample. On the other hand, amplicon sequencing targets taxonomically important marker genes, the analysis of which is restricted to previously known DNA sequences. While sequence-based metagenomics is used to analyze DNA sequences directly from the environment without the requirement of library construction and with limited identification of novel genes and products that can be complemented by functional genomics, function-based metagenomics requires fragmentation and cloning of extracted metagenome DNA in a suitable host with subsequent functional screening and sequencing clone for detection of a novel gene. Although advances were made in metagenomics, different challenges arise. This review provides insight into advances in the metagenomic approaches combined with next-generation sequencing, their recent applications highlighting the emerging ones, such as in astrobiology, forensic sciences, and SARS-CoV-2 infection diagnosis, and the challenges associated. This review further discusses the different types of metagenomics and outlines advancements in bioinformatics tools and their significance in the analysis of metagenomic datasets.

METAnnotatorX2: a Comprehensive Tool for Deep and Shallow Metagenomic Data Set Analyses.

ABSTRACTThe use of bioinformatic tools for read-based taxonomic and functional analyses of metagenomic data sets, including their assembly and management, is rather fragmentary due to the absence of an accepted gold standard. Moreover, most currently available software tools need input of millions of reads and rely on approximations in data analysis in order to reduce computing times. These issues result in suboptimal results in terms of accuracy, sensitivity, and specificity when used either for the reconstruction of taxonomic or functional profiles through read analysis or analysis of genomes reconstructed by metagenomic assembly. Moreover, the recent introduction of novel DNA sequencing technologies that generate long reads, such as Nanopore and PacBio, represent a valuable data resource that still suffers from a lack of dedicated tools to perform integrated hybrid analysis alongside short read data. In order to overcome these limitations, here we describe a comprehensive bioinformatic platform, METAnnotatorX2, aimed at providing an optimized user-friendly resource which maximizes output quality, while also allowing user-specific adaptation of the pipeline and straightforward integrated analysis of both short and long read data. To further improve performance quality and accuracy of taxonomic assignment of reads and contigs, custom preprocessed and taxonomically revised genomic databases for viruses, prokaryotes, and various eukaryotes were developed. The performance of METAnnotatorX2 was tested by analysis of artificial data sets encompassing viral, archaeal, bacterial, and eukaryotic (fungal) sequence reads that simulate different biological matrices. Moreover, real biological samples were employed to validate in silico results.IMPORTANCE We developed a novel tool, i.e., METAnnotatorX2, that includes a number of new advanced features for analysis of deep and shallow metagenomic data sets and is accompanied by (regularly updated) customized databases for archaea, bacteria, fungi, protists, and viruses. Both software and databases were developed so as to maximize sensitivity and specificity while including support for shallow metagenomic data sets. Through extensive tests performed on Illumina and Nanopore artificial data sets, we demonstrated the high performance of the software to not only extract taxonomic and functional information from sequence reads but also to assemble and process genomes from metagenomic data. The robustness of these functionalities was validated using “real-life” data sets obtained from Illumina and Nanopore sequencing of biological samples. Furthermore, the performance of METAnnotatorX2 was compared to other available software tools for analysis of shotgun metagenomics data.

The Human OncoBiome Database: A Database of Cancer Microbiome Datasets

<P>Background: The microbiome plays a very important role in many physiological processes including metabolism, inflammation, homeostasis and many biological pathways. Therefore, dysbiosis of the microbiome disrupts these pathways in different ways that may result in causing cancer. There is a complex connection between the microbiome and cancer. The human bodies are continuously exposed to microbial cells, both resident and transient, as well as their byproducts, including toxic metabolites. </P><P> Objective: To develop the manually curated, searchable and metagenomic resource to facilitate the investigation of Human Cancer microbiota and make it publicly accessible through a web interface which will help further in metagenomic studies. </P><P> Methods: In HOBD, the information on different cancers (Oral Cancer, Breast Cancer, Liver Cancer, and Colorectal Cancer) has been compiled. The main purpose of creating HOBD was to provide the scientific community with comprehensive information on the species that play a crucial role in various Human Cancers. </P><P> Result: Over time, this resource will grow to become a unique community resource of human cancer bacteria, providing an extra level of annotation for the analysis of metagenomic datasets. </P><P> Conclusion: The HOBD site offers easy to use tools for viewing all publicly available Human Cancer microbiota. The freely accessible website is available at http://www.juit.ac.in/hcmd/home.</P>

Chitinases Encoded in the Genomes of Acidobacteria: Origin and Evolution

Acidobacteria are one of the widespread and numerically abundant groups of prokaryotes in soils and peatlands. The functional potential of these microorganisms remains poorly understood due to the limited number of cultured and characterized representatives. Acidisarcina polymorpha SBC82 is the only acidobacterium for which the ability to use chitin as a source of nitrogen and carbon has been experimentally proven. The genome of strain SBC82 encodes six proteins from the GH18 glycoside hydrolase family; each of these proteins can be considered as a representative of an independent subfamily. The present bioinformatics study shows that one of these subfamilies contains an extensive group of hypothetical chitinases from the Acidobacteria. This functional annotation was done based on the close relationship with the experimentally characterized enzyme from the proteobacterium Xylella fastidiosa. As shown by phylogenetic analysis, the studied group of acidobacterial proteins originated due to lateral transfer of the ancestral gene from the Firmicutes. Acidobacteria subsequently served as a source for multiple lateral gene transfers to a number of bacterial phyla, including the Proteobacteria. Analysis of metagenomic datasets revealed a wide distribution of chitinase genes in acidobacteria that inhabit wetlands and permafrost soils of the temperate and subarctic climatic zones.

Ubiquity and Diversity of Complete Ammonia Oxidizers (Comammox).

The discovery of complete ammonia oxidizers (comammox) refutes the century-old paradigm that nitrification requires the activity of two types of microbes. Determining the distribution and abundance of comammox in various environments is important for revealing the ecology of microbial nitrification within the global nitrogen cycle. In this study, the ubiquity and diversity of comammox were analyzed for samples from different types of environments, including soil, sediment, sludge, and water. The results of a two-step PCR using highly degenerate primers (THDP-PCR) and quantitative real-time PCR (qPCR) supported the relatively high abundance of comammox in nearly half of all samples tested, sometimes even outnumbering canonical ammonia-oxidizing bacteria (AOB). In addition, a relatively high proportion of comammox in tap and coastal water samples was confirmed via analysis of metagenomic data sets in public databases. The diversity of comammox was estimated by comammox-specific partial nested PCR amplification of the ammonia monooxygenase subunit A (amoA) gene, and phylogenetic analysis of comammox AmoA clearly showed a split of clade A into clades A.1 and A.2, with the proportions of clades A.1, A.2, and B differing among the various environmental samples. Moreover, compared to the amoA genes of AOB and ammonia-oxidizing archaea (AOA), the comammox amoA gene exhibited higher diversity indices. The ubiquitous distribution and high diversity of comammox indicate that they are likely overlooked contributors to nitrification in various ecosystems.IMPORTANCE The discovery of complete ammonia oxidizers (comammox), which oxidize ammonia to nitrate via nitrite, refutes the century-old paradigm that nitrification requires the activity of two types of microbes and redefines a key process in the biogeochemical nitrogen cycle. Understanding the functional relationships between comammox and other nitrifiers is important for ecological studies on the nitrogen cycle. Therefore, the diversity and contribution of comammox should be considered during ecological analyses of nitrifying microorganisms. In this study, a ubiquitous and highly diverse distribution of comammox was observed in various environmental samples, similar to the distribution of canonical ammonia-oxidizing bacteria. The proportion of comammox was relatively high in coastal water and sediment samples, whereas it was nearly undetectable in open-ocean samples. The ubiquitous distribution and high diversity of comammox indicate that these microorganisms might be important contributors to nitrification.

Flexible metagenome analysis using the MGX framework

BackgroundThe characterization of microbial communities based on sequencing and analysis of their genetic information has become a popular approach also referred to as metagenomics; in particular, the recent advances in sequencing technologies have enabled researchers to study even the most complex communities.Metagenome analysis, the assignment of sequences to taxonomic and functional entities, however, remains a tedious task: large amounts of data need to be processed. There are a number of approaches addressing particular aspects, but scientific questions are often too specific to be answered by a general-purpose method.ResultsWe present MGX, a flexible and extensible client/server-framework for the management and analysis of metagenomic datasets; MGX features a comprehensive set of adaptable workflows required for taxonomic and functional metagenome analysis, combined with an intuitive and easy-to-use graphical user interface offering customizable result visualizations. At the same time, MGX allows to include own data sources and devise custom analysis pipelines, thus enabling researchers to perform basic as well as highly specific analyses within a single application.ConclusionsWith MGX, we provide a novel metagenome analysis platform giving researchers access to the most recent analysis tools. MGX covers taxonomic and functional metagenome analysis, statistical evaluation, and a wide range of visualizations easing data interpretation. Its default taxonomic classification pipeline provides equivalent or superior results in comparison to existing tools.

Detection of low-abundance bacterial strains in metagenomic datasets by eigengenome partitioning.

Analyses of metagenomic datasets that are sequenced to a depth of billions or trillions of bases can uncover hundreds of microbial genomes, but naive assembly of these data is computationally intensive, requiring hundreds of gigabytes to terabytes of RAM. We present latent strain analysis (LSA), a scalable, de novo pre-assembly method that separates reads into biologically informed partitions and thereby enables assembly of individual genomes. LSA is implemented with a streaming calculation of unobserved variables that we call eigengenomes. Eigengenomes reflect covariance in the abundance of short, fixed-length sequences, or k-mers. As the abundance of each genome in a sample is reflected in the abundance of each k-mer in that genome, eigengenome analysis can be used to partition reads from different genomes. This partitioning can be done in fixed memory using tens of gigabytes of RAM, which makes assembly and downstream analyses of terabytes of data feasible on commodity hardware. Using LSA, we assemble partial and near-complete genomes of bacterial taxa present at relative abundances as low as 0.00001%. We also show that LSA is sensitive enough to separate reads from several strains of the same species.

Genomes of “Spiribacter”, a streamlined, successful halophilic bacterium

BackgroundThalassosaline waters produced by the concentration of seawater are widespread and common extreme aquatic habitats. Their salinity varies from that of sea water (ca. 3.5%) to saturation for NaCl (ca. 37%). Obviously the microbiota varies dramatically throughout this range. Recent metagenomic analysis of intermediate salinity waters (19%) indicated the presence of an abundant and yet undescribed gamma-proteobacterium. Two strains belonging to this group have been isolated from saltern ponds of intermediate salinity in two Spanish salterns and were named “Spiribacter”.ResultsThe genomes of two isolates of “Spiribacter” have been fully sequenced and assembled. The analysis of metagenomic datasets indicates that microbes of this genus are widespread worldwide in medium salinity habitats representing the first ecologically defined moderate halophile. The genomes indicate that the two isolates belong to different species within the same genus. Both genomes are streamlined with high coding densities, have few regulatory mechanisms and no motility or chemotactic behavior. Metabolically they are heterotrophs with a subgroup II xanthorhodopsin as an additional energy source when light is available.ConclusionsThis is the first bacterium that has been proven by culture independent approaches to be prevalent in hypersaline habitats of intermediate salinity (half a way between the sea and NaCl saturation). Predictions from the proteome and analysis of transporter genes, together with a complete ectoine biosynthesis gene cluster are consistent with these microbes having the salt-out-organic-compatible solutes type of osmoregulation. All these features are also consistent with a well-adapted fully planktonic microbe while other halophiles with more complex genomes such as Salinibacter ruber might have particle associated microniches.

Role of Toxicogenomics in the Development of Safe, Efficacious and Novel Anti-microbial Therapies.

Over the last two decades, occurrence of bacterial resistance to commonly used antibiotics has necessitated the development of safer and more potent anti-microbial drugs. However, the development of novel antibiotics is severely hampered by adverse side effects, such as drug-induced liver toxicity. Several antibacterial drugs are known to have the potential to cause severe liver damage. The major challenge in developing novel anti-microbial drugs is to predict, with certain amount of probability, the drug-induced toxicity during the pre-clinical stages, thus optimizing and reducing the time and cost of drug development. Toxicogenomics approach is generally used to harness the potential of genomic tools and to understand the physiological basis of drug-induced toxicity based on the in-depth analysis of Metagenomic data sets, i.e., transcriptional, translational or metabolomic profiles. Toxicogenomics, therefore, represents a new paradigm in the drug development process, and is anticipated to play an invaluable role in future to develop safe and efficacious medicines, by predicting the toxic potential of a new chemical entity (NCE) in early stages of drug discovery. This review examines the toxicogenomic approach in predicting the safety/toxicity of novel anti-microbial drugs, and analyses the promises, pitfalls and challenges of applying this powerful technology to the drug development process.

Crass: identification and reconstruction of CRISPR from unassembled metagenomic data

Clustered regularly interspaced short palindromic repeats (CRISPR) constitute a bacterial and archaeal adaptive immune system that protect against bacteriophage (phage). Analysis of CRISPR loci reveals the history of phage infections and provides a direct link between phage and their hosts. All current tools for CRISPR identification have been developed to analyse completed genomes and are not well suited to the analysis of metagenomic data sets, where CRISPR loci are difficult to assemble owing to their repetitive structure and population heterogeneity. Here, we introduce a new algorithm, Crass, which is designed to identify and reconstruct CRISPR loci from raw metagenomic data without the need for assembly or prior knowledge of CRISPR in the data set. CRISPR in assembled data are often fragmented across many contigs/scaffolds and do not fully represent the population heterogeneity of CRISPR loci. Crass identified substantially more CRISPR in metagenomes previously analysed using assembly-based approaches. Using Crass, we were able to detect CRISPR that contained spacers with sequence homology to phage in the system, which would not have been identified using other approaches. The increased sensitivity, specificity and speed of Crass will facilitate comprehensive analysis of CRISPRs in metagenomic data sets, increasing our understanding of phage-host interactions and co-evolution within microbial communities.

Meeting Report: Metagenomics, Metadata and MetaAnalysis (M3) at ISMB 2010

This report summarizes the proceedings of the first day of the Metagenomics, Metadata and MetaAnalysis (M3) workshop held at the Intelligent Systems for Molecular Biology 2010 conference. The second day, which was dedicated to the inaugural meeting of the BioSharing initiative is presented in a separate report. The Genomic Standards Consortium (GSC) hosted the first day of this Special Interest Group (SIG) at ISMB to continue exploring the bottlenecks and emerging solutions for obtaining biological insights through large-scale comparative analysis of metagenomic datasets. The M3 SIG included invited and selected talks and a panel discussion at the end of the day involving the plenary speakers. Further information about the GSC and its range of activities can be found at http://gensc.org. Information about the newly established BioSharing effort can be found at http://biosharing.org/.

Meeting Report: Metagenomics, Metadata and Meta-analysis; (M3) Special Interest Group at ISMB 2009

This report summarizes the proceedings of the “Metagenomics, Metadata and Meta-analysis” (M3) Special Interest Group (SIG) meeting held at the Intelligent Systems for Molecular Biology 2009 conference. The Genomic Standards Consortium (GSC) hosted this meeting to explore the bottlenecks and emerging solutions for obtaining biological insights through large-scale comparative analysis of metagenomic datasets. The M3 SIG included 16 talks, half of which were selected from submitted abstracts, a poster session and a panel discussion involving members of the GSC Board. This report summarizes this one-day SIG, attempts to identify shared themes and recapitulates community recommendations for the future of this field. The GSC will also host an M3 workshop at the Pacific Symposium on Biocomputing (PSB) in January 2010. Further information about the GSC and its range of activities can be found at http://gensc.org/.

TACOA – Taxonomic classification of environmental genomic fragments using a kernelized nearest neighbor approach

BackgroundMetagenomics, or the sequencing and analysis of collective genomes (metagenomes) of microorganisms isolated from an environment, promises direct access to the "unculturable majority". This emerging field offers the potential to lay solid basis on our understanding of the entire living world. However, the taxonomic classification is an essential task in the analysis of metagenomics data sets that it is still far from being solved. We present a novel strategy to predict the taxonomic origin of environmental genomic fragments. The proposed classifier combines the idea of the k-nearest neighbor with strategies from kernel-based learning.ResultsOur novel strategy was extensively evaluated using the leave-one-out cross validation strategy on fragments of variable length (800 bp – 50 Kbp) from 373 completely sequenced genomes. TACOA is able to classify genomic fragments of length 800 bp and 1 Kbp with high accuracy until rank class. For longer fragments ≥ 3 Kbp accurate predictions are made at even deeper taxonomic ranks (order and genus). Remarkably, TACOA also produces reliable results when the taxonomic origin of a fragment is not represented in the reference set, thus classifying such fragments to its known broader taxonomic class or simply as "unknown". We compared the classification accuracy of TACOA with the latest intrinsic classifier PhyloPythia using 63 recently published complete genomes. For fragments of length 800 bp and 1 Kbp the overall accuracy of TACOA is higher than that obtained by PhyloPythia at all taxonomic ranks. For all fragment lengths, both methods achieved comparable high specificity results up to rank class and low false negative rates are also obtained.ConclusionAn accurate multi-class taxonomic classifier was developed for environmental genomic fragments. TACOA can predict with high reliability the taxonomic origin of genomic fragments as short as 800 bp. The proposed method is transparent, fast, accurate and the reference set can be easily updated as newly sequenced genomes become available. Moreover, the method demonstrated to be competitive when compared to the most current classifier PhyloPythia and has the advantage that it can be locally installed and the reference set can be kept up-to-date.