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Benavides, Andres, Friman Sanchez, Juan F. Alzate, and Felipe Cabarcas. "DATMA: Distributed AuTomatic Metagenomic Assembly and annotation framework." PeerJ 8 (September 3, 2020): e9762. http://dx.doi.org/10.7717/peerj.9762.
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Background A prime objective in metagenomics is to classify DNA sequence fragments into taxonomic units. It usually requires several stages: read’s quality control, de novo assembly, contig annotation, gene prediction, etc. These stages need very efficient programs because of the number of reads from the projects. Furthermore, the complexity of metagenomes requires efficient and automatic tools that orchestrate the different stages. Method DATMA is a pipeline for fast metagenomic analysis that orchestrates the following: sequencing quality control, 16S rRNA-identification, reads binning, de novo assembly and evaluation, gene prediction, and taxonomic annotation. Its distributed computing model can use multiple computing resources to reduce the analysis time. Results We used a controlled experiment to show DATMA functionality. Two pre-annotated metagenomes to compare its accuracy and speed against other metagenomic frameworks. Then, with DATMA we recovered a draft genome of a novel Anaerolineaceae from a biosolid metagenome. Conclusions DATMA is a bioinformatics tool that automatically analyzes complex metagenomes. It is faster than similar tools and, in some cases, it can extract genomes that the other tools do not. DATMA is freely available at https://github.com/andvides/DATMA.
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Olson, Nathan D., Todd J. Treangen, Christopher M. Hill, Victoria Cepeda-Espinoza, Jay Ghurye, Sergey Koren, and Mihai Pop. "Metagenomic assembly through the lens of validation: recent advances in assessing and improving the quality of genomes assembled from metagenomes." Briefings in Bioinformatics 20, no. 4 (August 7, 2017): 1140–50. http://dx.doi.org/10.1093/bib/bbx098.
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Abstract Metagenomic samples are snapshots of complex ecosystems at work. They comprise hundreds of known and unknown species, contain multiple strain variants and vary greatly within and across environments. Many microbes found in microbial communities are not easily grown in culture making their DNA sequence our only clue into their evolutionary history and biological function. Metagenomic assembly is a computational process aimed at reconstructing genes and genomes from metagenomic mixtures. Current methods have made significant strides in reconstructing DNA segments comprising operons, tandem gene arrays and syntenic blocks. Shorter, higher-throughput sequencing technologies have become the de facto standard in the field. Sequencers are now able to generate billions of short reads in only a few days. Multiple metagenomic assembly strategies, pipelines and assemblers have appeared in recent years. Owing to the inherent complexity of metagenome assembly, regardless of the assembly algorithm and sequencing method, metagenome assemblies contain errors. Recent developments in assembly validation tools have played a pivotal role in improving metagenomics assemblers. Here, we survey recent progress in the field of metagenomic assembly, provide an overview of key approaches for genomic and metagenomic assembly validation and demonstrate the insights that can be derived from assemblies through the use of assembly validation strategies. We also discuss the potential for impact of long-read technologies in metagenomics. We conclude with a discussion of future challenges and opportunities in the field of metagenomic assembly and validation.
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Pusadkar, Vaidehi, and Rajeev K. Azad. "Benchmarking Metagenomic Classifiers on Simulated Ancient and Modern Metagenomic Data." Microorganisms 11, no. 10 (October 2, 2023): 2478. http://dx.doi.org/10.3390/microorganisms11102478.
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Taxonomic profiling of ancient metagenomic samples is challenging due to the accumulation of specific damage patterns on DNA over time. Although a number of methods for metagenome profiling have been developed, most of them have been assessed on modern metagenomes or simulated metagenomes mimicking modern metagenomes. Further, a comparative assessment of metagenome profilers on simulated metagenomes representing a spectrum of degradation depth, from the extremity of ancient (most degraded) to current or modern (not degraded) metagenomes, has not yet been performed. To understand the strengths and weaknesses of different metagenome profilers, we performed their comprehensive evaluation on simulated metagenomes representing human dental calculus microbiome, with the level of DNA damage successively raised to mimic modern to ancient metagenomes. All classes of profilers, namely, DNA-to-DNA, DNA-to-protein, and DNA-to-marker comparison-based profilers were evaluated on metagenomes with varying levels of damage simulating deamination, fragmentation, and contamination. Our results revealed that, compared to deamination and fragmentation, human and environmental contamination of ancient DNA (with modern DNA) has the most pronounced effect on the performance of each profiler. Further, the DNA-to-DNA (e.g., Kraken2, Bracken) and DNA-to-marker (e.g., MetaPhlAn4) based profiling approaches showed complementary strengths, which can be leveraged to elevate the state-of-the-art of ancient metagenome profiling.
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Cameron, Ellen S., Mark L. Blaxter, and Robert D. Finn. "plastiC: A pipeline for recovery and characterization of plastid genomes from metagenomic datasets." Wellcome Open Research 8 (October 18, 2023): 475. http://dx.doi.org/10.12688/wellcomeopenres.19589.1.
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The use of culture independent molecular methods, often referred to as metagenomics, have revolutionized the ability to explore and characterize microbial communities from diverse environmental sources. Most metagenomic workflows have been developed for identification of prokaryotic and eukaryotic community constituents, but tools for identification of plastid genomes are lacking. The endosymbiotic origin of plastids also poses challenges where plastid metagenomic assembled genomes (MAGs) may be misidentified as low-quality bacterial MAGs. Current tools are limited to classification of contigs as plastid and do not provide further assessment or characterization of plastid MAGs. plastiC is a workflow that allows users to identify plastid genomes in metagenome assemblies, assess completeness, and predict taxonomic association from diverse environmental sources. plastiC is a Snakemake workflow available at https://github.com/Finn-Lab/plastiC. We demonstrate the utility of this workflow with the successful recover of algal plastid MAGs from publicly available lichen metagenomes.
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Vecherskii, M. V., M. V. Semenov, A. A. Lisenkova, and A. A. Stepankov. "Metagenomics: A New Direction in Ecology." Biology Bulletin 48, S3 (December 2021): S107—S117. http://dx.doi.org/10.1134/s1062359022010150.
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Abstract The prospects for application of metagenomic technologies in environmental studies are discussed. The advantages in investigating the taxonomic composition of aquatic and terrestrial ecosystems, as well as examples of trophic and phoric relationships found in ecosystems using the metagenomic approach, are described. The capabilities of metagenomics to study prokaryotic communities in complicated environments such as soils or animal intestines are shown. The role of relic DNA in the metagenome and the possibilities to study ancient organisms are highlighted. Particular attention is paid to the criticism of metagenomic technologies related to the low reproducibility of the sequencing data. Common methodological mistakes in bioinformatics processing of metagenomic data leading to misleading results are considered.
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New, Felicia N., and Ilana L. Brito. "What Is Metagenomics Teaching Us, and What Is Missed?" Annual Review of Microbiology 74, no. 1 (September 8, 2020): 117–35. http://dx.doi.org/10.1146/annurev-micro-012520-072314.
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Shotgun metagenomic sequencing has revolutionized our ability to detect and characterize the diversity and function of complex microbial communities. In this review, we highlight the benefits of using metagenomics as well as the breadth of conclusions that can be made using currently available analytical tools, such as greater resolution of species and strains across phyla and functional content, while highlighting challenges of metagenomic data analysis. Major challenges remain in annotating function, given the dearth of functional databases for environmental bacteria compared to model organisms, and the technical difficulties of metagenome assembly and phasing in heterogeneous environmental samples. In the future, improvements and innovation in technology and methodology will lead to lowered costs. Data integration using multiple technological platforms will lead to a better understanding of how to harness metagenomes. Subsequently, we will be able not only to characterize complex microbiomes but also to manipulate communities to achieve prosperous outcomes for health, agriculture, and environmental sustainability.
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Lüftinger, Lukas, Peter Májek, Thomas Rattei, and Stephan Beisken. "Metagenomic Antimicrobial Susceptibility Testing from Simulated Native Patient Samples." Antibiotics 12, no. 2 (February 9, 2023): 366. http://dx.doi.org/10.3390/antibiotics12020366.
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Genomic antimicrobial susceptibility testing (AST) has been shown to be accurate for many pathogens and antimicrobials. However, these methods have not been systematically evaluated for clinical metagenomic data. We investigate the performance of in-silico AST from clinical metagenomes (MG-AST). Using isolate sequencing data from a multi-center study on antimicrobial resistance (AMR) as well as shotgun-sequenced septic urine samples, we simulate over 2000 complicated urinary tract infection (cUTI) metagenomes with known resistance phenotype to 5 antimicrobials. Applying rule-based and machine learning-based genomic AST classifiers, we explore the impact of sequencing depth and technology, metagenome complexity, and bioinformatics processing approaches on AST accuracy. By using an optimized metagenomics assembly and binning workflow, MG-AST achieved balanced accuracy within 5.1% of isolate-derived genomic AST. For poly-microbial infections, taxonomic sample complexity and relatedness of taxa in the sample is a key factor influencing metagenomic binning and downstream MG-AST accuracy. We show that the reassignment of putative plasmid contigs by their predicted host range and investigation of whole resistome capabilities improved MG-AST performance on poly-microbial samples. We further demonstrate that machine learning-based methods enable MG-AST with superior accuracy compared to rule-based approaches on simulated native patient samples.
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Wang, Ziye, Ying Wang, Jed A. Fuhrman, Fengzhu Sun, and Shanfeng Zhu. "Assessment of metagenomic assemblers based on hybrid reads of real and simulated metagenomic sequences." Briefings in Bioinformatics 21, no. 3 (March 11, 2019): 777–90. http://dx.doi.org/10.1093/bib/bbz025.
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Abstract In metagenomic studies of microbial communities, the short reads come from mixtures of genomes. Read assembly is usually an essential first step for the follow-up studies in metagenomic research. Understanding the power and limitations of various read assembly programs in practice is important for researchers to choose which programs to use in their investigations. Many studies evaluating different assembly programs used either simulated metagenomes or real metagenomes with unknown genome compositions. However, the simulated datasets may not reflect the real complexities of metagenomic samples and the estimated assembly accuracy could be misleading due to the unknown genomes in real metagenomes. Therefore, hybrid strategies are required to evaluate the various read assemblers for metagenomic studies. In this paper, we benchmark the metagenomic read assemblers by mixing reads from real metagenomic datasets with reads from known genomes and evaluating the integrity, contiguity and accuracy of the assembly using the reads from the known genomes. We selected four advanced metagenome assemblers, MEGAHIT, MetaSPAdes, IDBA-UD and Faucet, for evaluation. We showed the strengths and weaknesses of these assemblers in terms of integrity, contiguity and accuracy for different variables, including the genetic difference of the real genomes with the genome sequences in the real metagenomic datasets and the sequencing depth of the simulated datasets. Overall, MetaSPAdes performs best in terms of integrity and continuity at the species-level, followed by MEGAHIT. Faucet performs best in terms of accuracy at the cost of worst integrity and continuity, especially at low sequencing depth. MEGAHIT has the highest genome fractions at the strain-level and MetaSPAdes has the overall best performance at the strain-level. MEGAHIT is the most efficient in our experiments. Availability: The source code is available at https://github.com/ziyewang/MetaAssemblyEval.
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Prabhakara, Shruthi, and Raj Acharya. "Unsupervised Two-Way Clustering of Metagenomic Sequences." Journal of Biomedicine and Biotechnology 2012 (2012): 1–11. http://dx.doi.org/10.1155/2012/153647.
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A major challenge facing metagenomics is the development of tools for the characterization of functional and taxonomic content of vast amounts of short metagenome reads. The efficacy of clustering methods depends on the number of reads in the dataset, the read length and relative abundances of source genomes in the microbial community. In this paper, we formulate an unsupervised naive Bayes multispecies, multidimensional mixture model for reads from a metagenome. We use the proposed model to cluster metagenomic reads by their species of origin and to characterize the abundance of each species. We model the distribution of word counts along a genome as a Gaussian for shorter, frequent words and as a Poisson for longer words that are rare. We employ either a mixture of Gaussians or mixture of Poissons to model reads within each bin. Further, we handle the high-dimensionality and sparsity associated with the data, by grouping the set of words comprising the reads, resulting in a two-way mixture model. Finally, we demonstrate the accuracy and applicability of this method on simulated and real metagenomes. Our method can accurately cluster reads as short as 100 bps and is robust to varying abundances, divergences and read lengths.
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Greenman, Noah, Sayf Al-Deen Hassouneh, Latifa S. Abdelli, Catherine Johnston, and Taj Azarian. "Improving Bacterial Metagenomic Research through Long-Read Sequencing." Microorganisms 12, no. 5 (May 4, 2024): 935. http://dx.doi.org/10.3390/microorganisms12050935.
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Metagenomic sequencing analysis is central to investigating microbial communities in clinical and environmental studies. Short-read sequencing remains the primary approach for metagenomic research; however, long-read sequencing may offer advantages of improved metagenomic assembly and resolved taxonomic identification. To compare the relative performance for metagenomic studies, we simulated short- and long-read datasets using increasingly complex metagenomes comprising 10, 20, and 50 microbial taxa. Additionally, we used an empirical dataset of paired short- and long-read data generated from mouse fecal pellets to assess real-world performance. We compared metagenomic assembly quality, taxonomic classification, and metagenome-assembled genome (MAG) recovery rates. We show that long-read sequencing data significantly improve taxonomic classification and assembly quality. Metagenomic assemblies using simulated long reads were more complete and more contiguous with higher rates of MAG recovery. This resulted in more precise taxonomic classifications. Principal component analysis of empirical data demonstrated that sequencing technology affects compositional results as samples clustered by sequence type, not sample type. Overall, we highlight strengths of long-read metagenomic sequencing for microbiome studies, including improving the accuracy of classification and relative abundance estimates. These results will aid researchers when considering which sequencing approaches to use for metagenomic projects.
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Meier, Matthew J., E. Suzanne Paterson, and Iain B. Lambert. "Use of Substrate-Induced Gene Expression in Metagenomic Analysis of an Aromatic Hydrocarbon-Contaminated Soil." Applied and Environmental Microbiology 82, no. 3 (November 20, 2015): 897–909. http://dx.doi.org/10.1128/aem.03306-15.
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ABSTRACTMetagenomics allows the study of genes related to xenobiotic degradation in a culture-independent manner, but many of these studies are limited by the lack of genomic context for metagenomic sequences. This study combined a phenotypic screen known as substrate-induced gene expression (SIGEX) with whole-metagenome shotgun sequencing. SIGEX is a high-throughput promoter-trap method that relies on transcriptional activation of a green fluorescent protein (GFP) reporter gene in response to an inducing compound and subsequent fluorescence-activated cell sorting to isolate individual inducible clones from a metagenomic DNA library. We describe a SIGEX procedure with improved library construction from fragmented metagenomic DNA and improved flow cytometry sorting procedures. We used SIGEX to interrogate an aromatic hydrocarbon (AH)-contaminated soil metagenome. The recovered clones contained sequences with various degrees of similarity to genes (or partial genes) involved in aromatic metabolism, for example,nahG(salicylate oxygenase) family genes and their respective upstreamnahRregulators. To obtain a broader context for the recovered fragments, clones were mapped to contigs derived fromde novoassembly of shotgun-sequenced metagenomic DNA which, in most cases, contained complete operons involved in aromatic metabolism, providing greater insight into the origin of the metagenomic fragments. A comparable set of contigs was generated using a significantly less computationally intensive procedure in which assembly of shotgun-sequenced metagenomic DNA was directed by the SIGEX-recovered sequences. This methodology may have broad applicability in identifying biologically relevant subsets of metagenomes (including both novel and known sequences) that can be targeted computationally byin silicoassembly and prediction tools.
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SHARMA, ANU, DWIJESH CHANDRA MISHRA, NEERAJ BUDHLAKOTI, ANIL RAI, SHASHI BHUSHAN LAL, and SANJEEV KUMAR. "Algorithmic and computational comparison of metagenome assemblers." Indian Journal of Agricultural Sciences 90, no. 5 (September 4, 2020): 847–54. http://dx.doi.org/10.56093/ijas.v90i5.104327.
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Assembly of genome sequences of a microbial community is computationally challenging and complex than its single genome counterparts. Keeping in view the volume, diversity and varied abundance of different microbes, number of metagenome assemblers have been developed addressing specific associated computational issues mainly following De Bruijn Graph (DBG) and Overlap Layout Consensus (OLC) approaches. It is very pertinent to understand different computational approaches and issues of metagenomic assembly to further improve them with respect to time and computational resource requirements. Therefore, the main objective of this article is to discuss various metagenomics assemblers with respect to their development addressing major computational issues. Initially the computational perspective of single genome assemblers based on OLC and DBG graph construction approaches was described. This is followed by review of metagenomic assemblers with respect to the algorithm implemented for addressing issues in metagenome assembly. Further, performance of some of the popular metagenome assemblers were empirically evaluated with respect to their run time and memory requirements by taking diversified benchmark metagenomics data at ICAR-IASRI, New Delhi in 2019. It was concluded that performance of assemblers varied considerably on these datasets and there is further need to make an effort to develop new tools or to modify the existing ones using efficient algorithms and data structures.
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Sabater, Carlos, Natalia Molinero, Manuel Ferrer, Carmen María García Bernardo, Susana Delgado, and Abelardo Margolles. "Functional Characterisation of Bile Metagenome: Study of Metagenomic Dark Matter." Microorganisms 9, no. 11 (October 21, 2021): 2201. http://dx.doi.org/10.3390/microorganisms9112201.
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Gallbladder metagenome involves a wide range of unidentified sequences comprising the so-called metagenomic dark matter. Therefore, this study aimed to characterise three gallbladder metagenomes and a fosmid library with an emphasis on metagenomic dark matter fraction. For this purpose, a novel data analysis strategy based on the combination of remote homology and molecular modelling has been proposed. According to the results obtained, several protein functional domains were annotated in the metagenomic dark matter fraction including acetyltransferases, outer membrane transporter proteins, membrane assembly factors, DNA repair and recombination proteins and response regulator phosphatases. In addition, one deacetylase involved in mycothiol biosynthesis was found in the metagenomic dark matter fraction of the fosmid library. This enzyme may exert a protective effect in Actinobacteria against bile components exposure, in agreement with the presence of multiple antibiotic and multidrug resistance genes. Potential mechanisms of action of this novel deacetylase were elucidated by molecular simulations, highlighting the role of histidine and aspartic acid residues. Computational pipelines presented in this work may be of special interest to discover novel microbial enzymes which had not been previously characterised.
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Thomas, B. T., M. O. Efuntoye, R. M. Kolawole, O. D. Popoola, and A. O. Tajudeen. "Metagenomic tracking of microbial consortia of cassava flakes (garri)." Ife Journal of Science 23, no. 2 (November 18, 2021): 75–82. http://dx.doi.org/10.4314/ijs.v23i2.8.
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The affirmation of several cross-sectional studies on the vulnerability of cassava flakes commonly called 'garri' to microbial attack has long been documented. However, longitudinal data on metagenomic tracking of microbial consortia of this important staple food are scarce. Hence, this study was aimed at tracking the microbial consortia of garri. A total of eight samples (four each from both Nigeria and Republic of Benin markets) were randomly collected aseptically using pre-sterilized aluminum pans and processed through a metagenomic approach, while both the chemical and proximate components of garri were assessed following standard techniques. The analysis of the taxonomic consortia of garri reveals the predomination of bacteria (99.82 and 99.81% for samples from Nigeria and Republic of Benin, respectively) while the remaining sequences matched with the Archae (0.07%), fungi (0.09%) and protozoa (0.09%). A large proportion of the sequences were unclassified at the phylum level (approximately 84.10 and 86.2% for Nigerian and Beninese samples, respectively). The reads of cassava flakes metagenome of both Nigeria and Republic of Benin exhibited analogous level of average GC content with sequence count of between 187773-213444 for samples from Nigeria and 157784-198763 for samples from Republic of Benin. The functional characteristics of the inhabiting metagenomes were found containing the genes encoding for adhesins, bacteriocins, resistance to antibiotics, toxic chemicals as well as toxins and superantigens. Both the chemical and the proximate compositions of the examined garri samples, though exhibited significant disparity, but without any apparent variation in the patterns of metagenomic data. Our findings however revealed bacteria as the major contaminants of these cassava food products.
Keywords; Metagenomics, Microorganisms, Cassava flakes (garri), Proximate composition
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Uritskiy, Gherman, and Jocelyne DiRuggiero. "Applying Genome-Resolved Metagenomics to Deconvolute the Halophilic Microbiome." Genes 10, no. 3 (March 14, 2019): 220. http://dx.doi.org/10.3390/genes10030220.
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In the past decades, the study of microbial life through shotgun metagenomic sequencing has rapidly expanded our understanding of environmental, synthetic, and clinical microbial communities. Here, we review how shotgun metagenomics has affected the field of halophilic microbial ecology, including functional potential reconstruction, virus–host interactions, pathway selection, strain dispersal, and novel genome discoveries. However, there still remain pitfalls and limitations from conventional metagenomic analysis being applied to halophilic microbial communities. Deconvolution of halophilic metagenomes has been difficult due to the high G + C content of these microbiomes and their high intraspecific diversity, which has made both metagenomic assembly and binning a challenge. Halophiles are also underrepresented in public genome databases, which in turn slows progress. With this in mind, this review proposes experimental and analytical strategies to overcome the challenges specific to the halophilic microbiome, from experimental designs to data acquisition and the computational analysis of metagenomic sequences. Finally, we speculate about the potential applications of other next-generation sequencing technologies in halophilic communities. RNA sequencing, long-read technologies, and chromosome conformation assays, not initially intended for microbiomes, are becoming available in the study of microbial communities. Together with recent analytical advancements, these new methods and technologies have the potential to rapidly advance the field of halophile research.
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Ghosh, Tarini Shankar, Varun Mehra, and Sharmila S. Mande. "Grid-Assembly: An oligonucleotide composition-based partitioning strategy to aid metagenomic sequence assembly." Journal of Bioinformatics and Computational Biology 13, no. 03 (May 15, 2015): 1541004. http://dx.doi.org/10.1142/s0219720015410048.
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Metagenomics approach involves extraction, sequencing and characterization of the genomic content of entire community of microbes present in a given environment. In contrast to genomic data, accurate assembly of metagenomic sequences is a challenging task. Given the huge volume and the diverse taxonomic origin of metagenomic sequences, direct application of single genome assembly methods on metagenomes are likely to not only lead to an immense increase in requirements of computational infrastructure, but also result in the formation of chimeric contigs. A strategy to address the above challenge would be to partition metagenomic sequence datasets into clusters and assemble separately the sequences in individual clusters using any single-genome assembly method. The current study presents such an approach that uses tetranucleotide usage patterns to first represent sequences as points in a three dimensional (3D) space. The 3D space is subsequently partitioned into "Grids". Sequences within overlapping grids are then progressively assembled using any available assembler. We demonstrate the applicability of the current Grid-Assembly method using various categories of assemblers as well as different simulated metagenomic datasets. Validation results indicate that the Grid-Assembly approach helps in improving the overall quality of assembly, in terms of the purity and volume of the assembled contigs.
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Godlewska, Urszula, Piotr Brzoza, Kamila Kwiecień, Mateusz Kwitniewski, and Joanna Cichy. "Metagenomic Studies in Inflammatory Skin Diseases." Current Microbiology 77, no. 11 (August 19, 2020): 3201–12. http://dx.doi.org/10.1007/s00284-020-02163-4.
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Abstract Next-generation sequencing (NGS) technologies together with an improved access to compute performance led to a cost-effective genome sequencing over the past several years. This allowed researchers to fully unleash the potential of genomic and metagenomic analyses to better elucidate two-way interactions between host cells and microbiome, both in steady-state and in pathological conditions. Experimental research involving metagenomics shows that skin resident microbes can influence the cutaneous pathophysiology. Here, we review metagenome approaches to study microbiota at this barrier site. We also describe the consequences of changes in the skin microbiota burden and composition, mostly revealed by these technologies, in the development of common inflammatory skin diseases.
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Tadmor, Arbel D., and Rob Phillips. "MCRL: using a reference library to compress a metagenome into a non-redundant list of sequences, considering viruses as a case study." Bioinformatics 38, no. 3 (October 12, 2021): 631–47. http://dx.doi.org/10.1093/bioinformatics/btab703.
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Abstract Motivation Metagenomes offer a glimpse into the total genomic diversity contained within a sample. Currently, however, there is no straightforward way to obtain a non-redundant list of all putative homologs of a set of reference sequences present in a metagenome. Results To address this problem, we developed a novel clustering approach called ‘metagenomic clustering by reference library’ (MCRL), where a reference library containing a set of reference genes is clustered with respect to an assembled metagenome. According to our proposed approach, reference genes homologous to similar sets of metagenomic sequences, termed ‘signatures’, are iteratively clustered in a greedy fashion, retaining at each step the reference genes yielding the lowest E values, and terminating when signatures of remaining reference genes have a minimal overlap. The outcome of this computation is a non-redundant list of reference genes homologous to minimally overlapping sets of contigs, representing potential candidates for gene families present in the metagenome. Unlike metagenomic clustering methods, there is no need for contigs to overlap to be associated with a cluster, enabling MCRL to draw on more information encoded in the metagenome when computing tentative gene families. We demonstrate how MCRL can be used to extract candidate viral gene families from an oral metagenome and an oral virome that otherwise could not be determined using standard approaches. We evaluate the sensitivity, accuracy and robustness of our proposed method for the viral case study and compare it with existing analysis approaches. Availability and implementation https://github.com/a-tadmor/MCRL. Supplementary information Supplementary data are available at Bioinformatics online.
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Abdulkadir, Nafi’u, Joao Pedro Saraiva, Florian Schattenberg, Rodolfo Brizola Toscan, Felipe Borim Correa, Hauke Harms, Susann Müller, and Ulisses Nunes da Rocha. "Combining Flow Cytometry and Metagenomics Improves Recovery of Metagenome-Assembled Genomes in a Cell Culture from Activated Sludge." Microorganisms 11, no. 1 (January 10, 2023): 175. http://dx.doi.org/10.3390/microorganisms11010175.
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The recovery of metagenome-assembled genomes is biased towards the most abundant species in a given community. To improve the identification of species, even if only dominant species are recovered, we investigated the integration of flow cytometry cell sorting with bioinformatics tools to recover metagenome-assembled genomes. We used a cell culture of a wastewater microbial community as our model system. Cells were separated based on fluorescence signals via flow cytometry cell sorting into sub-communities: dominant gates, low abundant gates, and outer gates into subsets of the original community. Metagenome sequencing was performed for all groups. The unsorted community was used as control. We recovered a total of 24 metagenome-assembled genomes (MAGs) representing 11 species-level genome operational taxonomic units (gOTUs). In addition, 57 ribosomal operational taxonomic units (rOTUs) affiliated with 29 taxa at species level were reconstructed from metagenomic libraries. Our approach suggests a two-fold increase in the resolution when comparing sorted and unsorted communities. Our results also indicate that species abundance is one determinant of genome recovery from metagenomes as we can recover taxa in the sorted libraries that are not present in the unsorted community. In conclusion, a combination of cell sorting and metagenomics allows the recovery of MAGs undetected without cell sorting.
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Nalbantoglu, O. Ufuk. "Information Theoretic Metagenome Assembly Allows the Discovery of Disease Biomarkers in Human Microbiome." Entropy 23, no. 2 (February 2, 2021): 187. http://dx.doi.org/10.3390/e23020187.
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Quantitative metagenomics is an important field that has delivered successful microbiome biomarkers associated with host phenotypes. The current convention mainly depends on unsupervised assembly of metagenomic contigs with a possibility of leaving interesting genetic material unassembled. Additionally, biomarkers are commonly defined on the differential relative abundance of compositional or functional units. Accumulating evidence supports that microbial genetic variations are as important as the differential abundance content, implying the need for novel methods accounting for the genetic variations in metagenomics studies. We propose an information theoretic metagenome assembly algorithm, discovering genomic fragments with maximal self-information, defined by the empirical distributions of nucleotides across the phenotypes and quantified with the help of statistical tests. Our algorithm infers fragments populating the most informative genetic variants in a single contig, named supervariant fragments. Experiments on simulated metagenomes, as well as on a colorectal cancer and an atherosclerotic cardiovascular disease dataset consistently discovered sequences strongly associated with the disease phenotypes. Moreover, the discriminatory power of these putative biomarkers was mainly attributed to the genetic variations rather than relative abundance. Our results support that a focus on metagenomics methods considering microbiome population genetics might be useful in discovering disease biomarkers with a great potential of translating to molecular diagnostics and biotherapeutics applications.
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Krishnan, Sidaswar, Matthew Z. DeMaere, Dominik Beck, Martin Ostrowski, Justin R. Seymour, and Aaron E. Darling. "Rhometa: Population recombination rate estimation from metagenomic read datasets." PLOS Genetics 19, no. 3 (March 27, 2023): e1010683. http://dx.doi.org/10.1371/journal.pgen.1010683.
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Prokaryotic evolution is influenced by the exchange of genetic information between species through a process referred to as recombination. The rate of recombination is a useful measure for the adaptive capacity of a prokaryotic population. We introduce Rhometa (https://github.com/sid-krish/Rhometa), a new software package to determine recombination rates from shotgun sequencing reads of metagenomes. It extends the composite likelihood approach for population recombination rate estimation and enables the analysis of modern short-read datasets. We evaluated Rhometa over a broad range of sequencing depths and complexities, using simulated and real experimental short-read data aligned to external reference genomes. Rhometa offers a comprehensive solution for determining population recombination rates from contemporary metagenomic read datasets. Rhometa extends the capabilities of conventional sequence-based composite likelihood population recombination rate estimators to include modern aligned metagenomic read datasets with diverse sequencing depths, thereby enabling the effective application of these techniques and their high accuracy rates to the field of metagenomics. Using simulated datasets, we show that our method performs well, with its accuracy improving with increasing numbers of genomes. Rhometa was validated on a real S. pneumoniae transformation experiment, where we show that it obtains plausible estimates of the rate of recombination. Finally, the program was also run on ocean surface water metagenomic datasets, through which we demonstrate that the program works on uncultured metagenomic datasets.
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Puranik, Sampada, Rajesh Ramavadh Pal, Ravi Prabhakar More, and Hemant J. Purohit. "Metagenomic approach to characterize soil microbial diversity of Phumdi at Loktak Lake." Water Science and Technology 74, no. 9 (August 9, 2016): 2075–86. http://dx.doi.org/10.2166/wst.2016.370.
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Loktak, one of the largest freshwater lakes of India, is known for floating islands (Phumdi), being made up of a heterogeneous biomass of vegetation and soil. This ecological site represents an exclusive environmental habitat wherein the rhizospheric microbial community of Phumdi plays a key role in biogeochemical cycling of nutrients. A culture-independent whole genome shotgun sequencing based metagenomic approach was employed to unravel the composition of the microbial community and its corresponding functional potential at this environmental habitat. Proteobacteria (51%) was found to be the most dominant bacterial phylum followed by Acidobacteria (10%), Actinobacteria (9%) and Bacteroidetes (7%). Furthermore, Loktak metagenome data were compared with available metagenomes from four other aquatic habitats, varying from pristine to highly polluted eutrophic habitats. The comparative metagenomics approach aided by statistical analysis revealed that Candidatus Solibacter, Bradyrhizobium, Candidatus Koribacter, Pedosphaera, Methylobacterium, Anaeromyxobacter, Sorangium, Opitutus and Acidobacterium genera are selectively dominant at this habitat. Correspondingly, 12 different functional categories were found to be exclusively prevalent at Phumdi compared to other freshwater habitats. These differential features have been attributed to the unique habitat at Phumdi and correlated to the phenomenon of bioremediation at Loktak Lake.
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Simon, Carola, and Rolf Daniel. "Metagenomic Analyses: Past and Future Trends." Applied and Environmental Microbiology 77, no. 4 (December 17, 2010): 1153–61. http://dx.doi.org/10.1128/aem.02345-10.
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ABSTRACTMetagenomics has revolutionized microbiology by paving the way for a cultivation-independent assessment and exploitation of microbial communities present in complex ecosystems. Metagenomics comprising construction and screening of metagenomic DNA libraries has proven to be a powerful tool to isolate new enzymes and drugs of industrial importance. So far, the majority of the metagenomically exploited habitats comprised temperate environments, such as soil and marine environments. Recently, metagenomes of extreme environments have also been used as sources of novel biocatalysts. The employment of next-generation sequencing techniques for metagenomics resulted in the generation of large sequence data sets derived from various environments, such as soil, the human body, and ocean water. Analyses of these data sets opened a window into the enormous taxonomic and functional diversity of environmental microbial communities. To assess the functional dynamics of microbial communities, metatranscriptomics and metaproteomics have been developed. The combination of DNA-based, mRNA-based, and protein-based analyses of microbial communities present in different environments is a way to elucidate the compositions, functions, and interactions of microbial communities and to link these to environmental processes.
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Castillo Villamizar, Genis Andrés, Heiko Nacke, Marc Boehning, Kristin Herz, and Rolf Daniel. "Functional Metagenomics Reveals an Overlooked Diversity and Novel Features of Soil-Derived Bacterial Phosphatases and Phytases." mBio 10, no. 1 (January 29, 2019): e01966-18. http://dx.doi.org/10.1128/mbio.01966-18.
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ABSTRACTPhosphatases, including phytases, play a major role in cell metabolism, phosphorus cycle, biotechnology, and pathogenic processes. Nevertheless, their discovery by functional metagenomics is challenging. Here, soil metagenomic libraries were successfully screened for genes encoding phosphatase activity. In this context, we report the largest number and diversity of phosphatase genes derived from functional metagenome analysis. Two of the detected gene products carry domains which have never been associated with phosphatase activity before. One of these domains, the SNARE-associated domain DedA, harbors a so-far-overlooked motif present in numerous bacterial SNARE-associated proteins. Our analysis revealed a previously unreported phytase activity of the alkaline phosphatase and sulfatase superfamily (cl23718) and of purple acid phosphatases from nonvegetal origin. This suggests that the classical concept comprising four classes of phytases should be modified and indicates high performance of our screening method for retrieving novel types of phosphatases/phytases hidden in metagenomes of complex environments.IMPORTANCEPhosphorus (P) is a key element involved in numerous cellular processes and essential to meet global food demand. Phosphatases play a major role in cell metabolism and contribute to control the release of P from phosphorylated organic compounds, including phytate. Apart from the relationship with pathogenesis and the enormous economic relevance, phosphatases/phytases are also important for reduction of phosphorus pollution. Almost all known functional phosphatases/phytases are derived from cultured individual microorganisms. We demonstrate here for the first time the potential of functional metagenomics to exploit the phosphatase/phytase pools hidden in environmental soil samples. The recovered diversity of phosphatases/phytases comprises new types and proteins exhibiting largely unknown characteristics, demonstrating the potential of the screening method for retrieving novel target enzymes. The insights gained into the unknown diversity of genes involved in the P cycle highlight the power of function-based metagenomic screening strategies to study Earth’s phosphatase pools.
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Filipic, Brankica, Katarina Novovic, David J. Studholme, Milka Malesevic, Nemanja Mirkovic, Milan Kojic, and Branko Jovcic. "Shotgun metagenomics reveals differences in antibiotic resistance genes among bacterial communities in Western Balkans glacial lakes sediments." Journal of Water and Health 18, no. 3 (May 21, 2020): 383–97. http://dx.doi.org/10.2166/wh.2020.227.
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Abstract Long-term overuse of antibiotics has driven the propagation and spreading of antibiotic resistance genes (ARGs) such as efflux pumps in the environment, which can be transferred to clinically relevant pathogens. This study explored the abundance and diversity of ARGs and mobile genetic elements within bacterial communities from sediments of three Western Balkans glacial lakes: Plav Lake (high impact of human population), Black Lake (medium impact of human population) and Donje Bare Lake (remote lake, minimal impact of human population) via shotgun metagenomics. Assembled metagenomic sequences revealed that Resistance-Nodulation-Division (RND) efflux pumps genes were most abundant in metagenome from the Plav Lake. The Integron Finder bioinformatics tool detected 38 clusters of attC sites lacking integron-integrases (CALIN) elements: 20 from Plav Lake, four from Black Lake and 14 from Donje Bare Lake. A complete integron sequence was recovered only from the assembled metagenome from Plav Lake. Plasmid contents within the metagenomes were similar, with proportions of contigs being plasmid-related: 1.73% for Plav Lake, 1.59% for Black Lake and 1.64% for Donje Bare Lake. The investigation showed that RNDs and mobile genetic elements content correlated with human population impact.
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Elcheninov, Alexander G., Kseniya S. Zayulina, Alexandra A. Klyukina, Mariia K. Kremneva, Ilya V. Kublanov, and Tatiana V. Kochetkova. "Metagenomic Insights into the Taxonomic and Functional Features of Traditional Fermented Milk Products from Russia." Microorganisms 12, no. 1 (December 21, 2023): 16. http://dx.doi.org/10.3390/microorganisms12010016.
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Fermented milk products (FMPs) contain probiotics that are live bacteria considered to be beneficial to human health due to the production of various bioactive molecules. In this study, nine artisanal FMPs (kefir, ayran, khurunga, shubat, two cottage cheeses, bryndza, khuruud and suluguni-like cheese) from different regions of Russia were characterized using metagenomics. A metagenomic sequencing of ayran, khurunga, shubat, khuruud and suluguni-like cheese was performed for the first time. The taxonomic profiling of metagenomic reads revealed that Lactococcus species, such as Lc. lactis and Lc. cremoris prevailed in khuruud, bryndza, one sample of cottage cheese and khurunga. The latter one together with suluguni-like cheese microbiome was dominated by bacteria, affiliated to Lactobacillus helveticus (32–35%). In addition, a high proportion of sequences belonging to the genera Lactobacillus, Lactococcus and Streptococcus but not classified at the species level were found in the suluguni-like cheese. Lactobacillus delbrueckii, as well as Streptococcus thermophilus constituted the majority in another cottage cheese, kefir and ayran metagenomes. The microbiome of shubat, produced from camel’s milk, was significantly distinctive, and Lentilactobacillus kefiri, Lactobacillus kefiranofaciens and Bifidobacterium mongoliense represented the dominant components (42, 7.4 and 5.6%, respectively). In total, 78 metagenome-assembled genomes with a completeness ≥ 50.2% and a contamination ≤ 8.5% were recovered: 61 genomes were assigned to the Enterococcaceae, Lactobacillaceae and Streptococcaceae families (the Lactobacillales order within Firmicutes), 4 to Bifidobacteriaceae (the Actinobacteriota phylum) and 2 to Acetobacteraceae (the Proteobacteria phylum). A metagenomic analysis revealed numerous genes, from 161 to 1301 in different products, encoding glycoside hydrolases and glycosyltransferases predicted to participate in lactose, alpha-glucans and peptidoglycan hydrolysis as well as exopolysaccharides synthesis. A large number of secondary metabolite biosynthetic gene clusters, such as lanthipeptides, unclassified bacteriocins, nonribosomal peptides and polyketide synthases were also detected. Finally, the genes involved in the synthesis of bioactive compounds like β-lactones, terpenes and furans, nontypical for fermented milk products, were also found. The metagenomes of kefir, ayran and shubat was shown to contain either no or a very low count of antibiotic resistance genes. Altogether, our results show that traditional indigenous fermented products are a promising source of novel probiotic bacteria with beneficial properties for medical and food industries.
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Zorrilla, Francisco, Filip Buric, Kiran R. Patil, and Aleksej Zelezniak. "metaGEM: reconstruction of genome scale metabolic models directly from metagenomes." Nucleic Acids Research 49, no. 21 (October 6, 2021): e126-e126. http://dx.doi.org/10.1093/nar/gkab815.
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Abstract Metagenomic analyses of microbial communities have revealed a large degree of interspecies and intraspecies genetic diversity through the reconstruction of metagenome assembled genomes (MAGs). Yet, metabolic modeling efforts mainly rely on reference genomes as the starting point for reconstruction and simulation of genome scale metabolic models (GEMs), neglecting the immense intra- and inter-species diversity present in microbial communities. Here, we present metaGEM (https://github.com/franciscozorrilla/metaGEM), an end-to-end pipeline enabling metabolic modeling of multi-species communities directly from metagenomes. The pipeline automates all steps from the extraction of context-specific prokaryotic GEMs from MAGs to community level flux balance analysis (FBA) simulations. To demonstrate the capabilities of metaGEM, we analyzed 483 samples spanning lab culture, human gut, plant-associated, soil, and ocean metagenomes, reconstructing over 14,000 GEMs. We show that GEMs reconstructed from metagenomes have fully represented metabolism comparable to isolated genomes. We demonstrate that metagenomic GEMs capture intraspecies metabolic diversity and identify potential differences in the progression of type 2 diabetes at the level of gut bacterial metabolic exchanges. Overall, metaGEM enables FBA-ready metabolic model reconstruction directly from metagenomes, provides a resource of metabolic models, and showcases community-level modeling of microbiomes associated with disease conditions allowing generation of mechanistic hypotheses.
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YE, YUZHEN, and HAIXU TANG. "AN ORFOME ASSEMBLY APPROACH TO METAGENOMICS SEQUENCES ANALYSIS." Journal of Bioinformatics and Computational Biology 07, no. 03 (June 2009): 455–71. http://dx.doi.org/10.1142/s0219720009004151.
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Metagenomics is an emerging methodology for the direct genomic analysis of a mixed community of uncultured microorganisms. The current analyses of metagenomics data largely rely on the computational tools originally designed for microbial genomics projects. The challenge of assembling metagenomic sequences arises mainly from the short reads and the high species complexity of the community. Alternatively, individual (short) reads will be searched directly against databases of known genes (or proteins) to identify homologous sequences. The latter approach may have low sensitivity and specificity in identifying homologous sequences, which may further bias the subsequent diversity analysis. In this paper, we present a novel approach to metagenomic data analysis, called Metagenomic ORFome Assembly (MetaORFA). The whole computational framework consists of three steps. Each read from a metagenomics project will first be annotated with putative open reading frames (ORFs) that likely encode proteins. Next, the predicted ORFs are assembled into a collection of peptides using an EULER assembly method. Finally, the assembled peptides (i.e. ORFome) are used for database searching of homologs and subsequent diversity analysis. We applied MetaORFA approach to several metagenomics datasets with low coverage short reads. The results show that MetaORFA can produce long peptides even when the sequence coverage of reads is extremely low. Hence, the ORFome assembly significantly increases the sensitivity of homology searching, and may potentially improve the diversity analysis of the metagenomic data. This improvement is especially useful for metagenomic projects when the genome assembly does not work because of the low sequence coverage.
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Zhang, Shao-Wu, Xiang-Yang Jin, and Teng Zhang. "Gene Prediction in Metagenomic Fragments with Deep Learning." BioMed Research International 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/4740354.
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Next generation sequencing technologies used in metagenomics yield numerous sequencing fragments which come from thousands of different species. Accurately identifying genes from metagenomics fragments is one of the most fundamental issues in metagenomics. In this article, by fusing multifeatures (i.e., monocodon usage, monoamino acid usage, ORF length coverage, and Z-curve features) and using deep stacking networks learning model, we present a novel method (called Meta-MFDL) to predict the metagenomic genes. The results with 10 CV and independent tests show that Meta-MFDL is a powerful tool for identifying genes from metagenomic fragments.
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Yasir, Muhammad, Areej A. Alkhaldy, Samah Abdullah Soliman, Safaa A. Turkistani, and Esam I. Azhar. "Metagenomic Insights into the Microbiome and Resistance Genes of Traditional Fermented Foods in Arabia." Foods 12, no. 18 (September 6, 2023): 3342. http://dx.doi.org/10.3390/foods12183342.
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This study uncovered microbial communities and evaluated the microbiological safety of traditional fermented foods consumed in the Arab region. Samples of dairy and non-dairy fermented foods—mish, jibneh, zabadi, and pickles—were collected from local markets in Saudi Arabia. Using the MiSeq system, samples were sequenced using 16S amplicons and shotgun metagenomics. Alpha and beta diversity indicated inter- and intra-variation in the studied fermented foods’ bacterial communities. In the case of mish, the replicates were clustered. Twenty-one genera were found to be significantly different (FDR < 0.05) in abundance in pairwise comparison of fermented foods. Five high-quality, metagenome-assembled genomes (MAGs) of Lactococcus lactis, Lactobacillus helveticus, Pseudoalteromonas nigrifaciens, Streptococcus thermophiles, and Lactobacillus acetotolerans were retrieved from the shotgun sequencing representing the dominant taxa in the studied fermented foods. Additionally, 33 genes that cause antimicrobial resistance (ARGs) against ten different antibiotic classes were detected. Metabolic pathways were abundant in the studied metagenomes, such as amino acid metabolism, carbohydrate metabolism, cofactors, and vitamin biosynthesis. Metagenomic evaluation of Arabian fermented foods, including the identification of probiotics, pathogenic bacteria, and ARGs, illustrates the importance of microbiological analysis in evaluating their health effects.
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Cres, Cecile M., Andrew Tritt, Kristofer E. Bouchard, and Ying Zhang. "DL-TODA: A Deep Learning Tool for Omics Data Analysis." Biomolecules 13, no. 4 (March 24, 2023): 585. http://dx.doi.org/10.3390/biom13040585.
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Metagenomics is a technique for genome-wide profiling of microbiomes; this technique generates billions of DNA sequences called reads. Given the multiplication of metagenomic projects, computational tools are necessary to enable the efficient and accurate classification of metagenomic reads without needing to construct a reference database. The program DL-TODA presented here aims to classify metagenomic reads using a deep learning model trained on over 3000 bacterial species. A convolutional neural network architecture originally designed for computer vision was applied for the modeling of species-specific features. Using synthetic testing data simulated with 2454 genomes from 639 species, DL-TODA was shown to classify nearly 75% of the reads with high confidence. The classification accuracy of DL-TODA was over 0.98 at taxonomic ranks above the genus level, making it comparable with Kraken2 and Centrifuge, two state-of-the-art taxonomic classification tools. DL-TODA also achieved an accuracy of 0.97 at the species level, which is higher than 0.93 by Kraken2 and 0.85 by Centrifuge on the same test set. Application of DL-TODA to the human oral and cropland soil metagenomes further demonstrated its use in analyzing microbiomes from diverse environments. Compared to Centrifuge and Kraken2, DL-TODA predicted distinct relative abundance rankings and is less biased toward a single taxon.
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Jaiani, Ekaterine, Ia Kusradze, Tamar Kokashvili, Natia Geliashvili, Nino Janelidze, Adam Kotorashvili, Nato Kotaria, Archil Guchmanidze, Marina Tediashvili, and David Prangishvili. "Microbial Diversity and Phage–Host Interactions in the Georgian Coastal Area of the Black Sea Revealed by Whole Genome Metagenomic Sequencing." Marine Drugs 18, no. 11 (November 14, 2020): 558. http://dx.doi.org/10.3390/md18110558.
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Viruses have the greatest abundance and highest genetic diversity in marine ecosystems. The interactions between viruses and their hosts is one of the hot spots of marine ecology. Besides their important role in various ecosystems, viruses, especially bacteriophages and their gene pool, are of enormous interest for the development of new gene products with high innovation value. Various studies have been conducted in diverse ecosystems to understand microbial diversity and phage–host interactions; however, the Black Sea, especially the Eastern coastal area, remains among the least studied ecosystems in this regard. This study was aimed at to fill this gap by analyzing microbial diversity and bacteriophage–host interactions in the waters of Eastern Black Sea using a metagenomic approach. To this end, prokaryotic and viral metagenomic DNA from two sampling sites, Poti and Gonio, were sequenced on the Illumina Miseq platform and taxonomic and functional profiles of the metagenomes were obtained using various bioinformatics tools. Our metagenomics analyses allowed us to identify the microbial communities, with Proteobacteria, Cyanobacteria, Actinibacteria, and Firmicutes found to be the most dominant bacterial phyla and Synechococcus and Candidatus Pelagibacter phages found to be the most dominant viral groups in the Black Sea. As minor groups, putative phages specific to human pathogens were identified in the metagenomes. We also characterized interactions between the phages and prokaryotic communities by determining clustered regularly interspaced short palindromic repeats (CRISPR), prophage-like sequences, and integrase/excisionase sequences in the metagenomes, along with identification of putative horizontally transferred genes in the viral contigs. In addition, in the viral contig sequences related to peptidoglycan lytic activity were identified as well. This is the first study on phage and prokaryote diversity and their interactions in the Eastern coastal area of the Black Sea using a metagenomic approach.
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Moller, Abraham G., and Chun Liang. "MetaCRAST: reference-guided extraction of CRISPR spacers from unassembled metagenomes." PeerJ 5 (September 7, 2017): e3788. http://dx.doi.org/10.7717/peerj.3788.
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Clustered regularly interspaced short palindromic repeat (CRISPR) systems are the adaptive immune systems of bacteria and archaea against viral infection. While CRISPRs have been exploited as a tool for genetic engineering, their spacer sequences can also provide valuable insights into microbial ecology by linking environmental viruses to their microbial hosts. Despite this importance, metagenomic CRISPR detection remains a major challenge. Here we present a reference-guided CRISPR spacer detection tool (Metagenomic CRISPR Reference-Aided Search Tool—MetaCRAST) that constrains searches based on user-specified direct repeats (DRs). These DRs could be expected from assembly or taxonomic profiles of metagenomes. We compared the performance of MetaCRAST to those of two existing metagenomic CRISPR detection tools—Crass and MinCED—using both real and simulated acid mine drainage (AMD) and enhanced biological phosphorus removal (EBPR) metagenomes. Our evaluation shows MetaCRAST improves CRISPR spacer detection in real metagenomes compared to the de novo CRISPR detection methods Crass and MinCED. Evaluation on simulated metagenomes show it performs better than de novo tools for Illumina metagenomes and comparably for 454 metagenomes. It also has comparable performance dependence on read length and community composition, run time, and accuracy to these tools. MetaCRAST is implemented in Perl, parallelizable through the Many Core Engine (MCE), and takes metagenomic sequence reads and direct repeat queries (FASTA or FASTQ) as input. It is freely available for download at https://github.com/molleraj/MetaCRAST.
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Becker, Burkhard, and Ekaterina Pushkareva. "Metagenomics Provides a Deeper Assessment of the Diversity of Bacterial Communities in Polar Soils Than Metabarcoding." Genes 14, no. 4 (March 28, 2023): 812. http://dx.doi.org/10.3390/genes14040812.
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The diversity of soil bacteria was analyzed via metabarcoding and metagenomic approaches using DNA samples isolated from the biocrusts of 12 different Arctic and Antarctic sites. For the metabarcoding approach, the V3-4 region of the 16S rRNA was targeted. Our results showed that nearly all operational taxonomic units (OTUs = taxa) found in metabarcoding analyses were recovered in metagenomic analyses. In contrast, metagenomics identified a large number of additional OTUs absent in metabarcoding analyses. In addition, we found huge differences in the abundance of OTUs between the two methods. The reasons for these differences seem to be (1) the higher sequencing depth in metagenomics studies, which allows the detection of low-abundance community members in metagenomics, and (2) bias of primer pairs used to amplify the targeted sequence in metabarcoding, which can change the community composition dramatically even at the lower taxonomic levels. We strongly recommend using only metagenomic approaches when establishing the taxonomic profiles of whole biological communities.
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Kerkvliet, Jesse J., Alex Bossers, Jannigje G. Kers, Rodrigo Meneses, Rob Willems, and Anita C. Schürch. "Metagenomic assembly is the main bottleneck in the identification of mobile genetic elements." PeerJ 12 (January 4, 2024): e16695. http://dx.doi.org/10.7717/peerj.16695.
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Antimicrobial resistance genes (ARG) are commonly found on acquired mobile genetic elements (MGEs) such as plasmids or transposons. Understanding the spread of resistance genes associated with mobile elements (mARGs) across different hosts and environments requires linking ARGs to the existing mobile reservoir within bacterial communities. However, reconstructing mARGs in metagenomic data from diverse ecosystems poses computational challenges, including genome fragment reconstruction (assembly), high-throughput annotation of MGEs, and identification of their association with ARGs. Recently, several bioinformatics tools have been developed to identify assembled fragments of plasmids, phages, and insertion sequence (IS) elements in metagenomic data. These methods can help in understanding the dissemination of mARGs. To streamline the process of identifying mARGs in multiple samples, we combined these tools in an automated high-throughput open-source pipeline, MetaMobilePicker, that identifies ARGs associated with plasmids, IS elements and phages, starting from short metagenomic sequencing reads. This pipeline was used to identify these three elements on a simplified simulated metagenome dataset, comprising whole genome sequences from seven clinically relevant bacterial species containing 55 ARGs, nine plasmids and five phages. The results demonstrated moderate precision for the identification of plasmids (0.57) and phages (0.71), and moderate sensitivity of identification of IS elements (0.58) and ARGs (0.70). In this study, we aim to assess the main causes of this moderate performance of the MGE prediction tools in a comprehensive manner. We conducted a systematic benchmark, considering metagenomic read coverage, contig length cutoffs and investigating the performance of the classification algorithms. Our analysis revealed that the metagenomic assembly process is the primary bottleneck when linking ARGs to identified MGEs in short-read metagenomics sequencing experiments rather than ARGs and MGEs identification by the different tools.
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Graham, Elaina D., John F. Heidelberg, and Benjamin J. Tully. "BinSanity: unsupervised clustering of environmental microbial assemblies using coverage and affinity propagation." PeerJ 5 (March 8, 2017): e3035. http://dx.doi.org/10.7717/peerj.3035.
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Metagenomics has become an integral part of defining microbial diversity in various environments. Many ecosystems have characteristically low biomass and few cultured representatives. Linking potential metabolisms to phylogeny in environmental microorganisms is important for interpreting microbial community functions and the impacts these communities have on geochemical cycles. However, with metagenomic studies there is the computational hurdle of ‘binning’ contigs into phylogenetically related units or putative genomes. Binning methods have been implemented with varying approaches such as k-means clustering, Gaussian mixture models, hierarchical clustering, neural networks, and two-way clustering; however, many of these suffer from biases against low coverage/abundance organisms and closely related taxa/strains. We are introducing a new binning method, BinSanity, that utilizes the clustering algorithm affinity propagation (AP), to cluster assemblies using coverage with compositional based refinement (tetranucleotide frequency and percent GC content) to optimize bins containing multiple source organisms. This separation of composition and coverage based clustering reduces bias for closely related taxa. BinSanity was developed and tested on artificial metagenomes varying in size and complexity. Results indicate that BinSanity has a higher precision, recall, and Adjusted Rand Index compared to five commonly implemented methods. When tested on a previously published environmental metagenome, BinSanity generated high completion and low redundancy bins corresponding with the published metagenome-assembled genomes.
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Owen, Jeremy G., Zachary Charlop-Powers, Alexandra G. Smith, Melinda A. Ternei, Paula Y. Calle, Boojala Vijay B. Reddy, Daniel Montiel, and Sean F. Brady. "Multiplexed metagenome mining using short DNA sequence tags facilitates targeted discovery of epoxyketone proteasome inhibitors." Proceedings of the National Academy of Sciences 112, no. 14 (March 23, 2015): 4221–26. http://dx.doi.org/10.1073/pnas.1501124112.
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In molecular evolutionary analyses, short DNA sequences are used to infer phylogenetic relationships among species. Here we apply this principle to the study of bacterial biosynthesis, enabling the targeted isolation of previously unidentified natural products directly from complex metagenomes. Our approach uses short natural product sequence tags derived from conserved biosynthetic motifs to profile biosynthetic diversity in the environment and then guide the recovery of gene clusters from metagenomic libraries. The methodology is conceptually simple, requires only a small investment in sequencing, and is not computationally demanding. To demonstrate the power of this approach to natural product discovery we conducted a computational search for epoxyketone proteasome inhibitors within 185 globally distributed soil metagenomes. This led to the identification of 99 unique epoxyketone sequence tags, falling into 6 phylogenetically distinct clades. Complete gene clusters associated with nine unique tags were recovered from four saturating soil metagenomic libraries. Using heterologous expression methodologies, seven potent epoxyketone proteasome inhibitors (clarepoxcins A–E and landepoxcins A and B) were produced from these pathways, including compounds with different warhead structures and a naturally occurring halohydrin prodrug. This study provides a template for the targeted expansion of bacterially derived natural products using the global metagenome.
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Banar, Maryam, Dinesh Rokaya, Reza Azizian, Zohaib Khurshid, and Morteza Banakar. "Oral bacteriophages: metagenomic clues to interpret microbiomes." PeerJ 12 (February 20, 2024): e16947. http://dx.doi.org/10.7717/peerj.16947.
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Bacteriophages are bacterial viruses that are distributed throughout the environment. Lytic phages and prophages in saliva, oral mucosa, and dental plaque interact with the oral microbiota and can change biofilm formation. The interactions between phages and bacteria can be considered a portion of oral metagenomics. The metagenomic profile of the oral microbiome indicates various bacteria. Indeed, there are various phages against these bacteria in the oral cavity. However, some other phages, like phages against Absconditabacteria, Chlamydiae, or Chloroflexi, have not been identified in the oral cavity. This review gives an overview of oral bacteriophage and used for metagenomics. Metagenomics of these phages deals with multi-drug-resistant bacterial plaques (biofilms) in oral cavities and oral infection. Hence, dentists and pharmacologists should know this metagenomic profile to cope with predental and dental infectious diseases.
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Bardou, Philippe, Sandrine Laguerre, Sarah Maman Haddad, Sabrina Legoueix Rodriguez, Elisabeth Laville, Claire Dumon, Gabrielle Potocki-Veronese, and Christophe Klopp. "MINTIA: a metagenomic INserT integrated assembly and annotation tool." PeerJ 9 (September 27, 2021): e11885. http://dx.doi.org/10.7717/peerj.11885.
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The earth harbors trillions of bacterial species adapted to very diverse ecosystems thanks to specific metabolic function acquisition. Most of the genes responsible for these functions belong to uncultured bacteria and are still to be discovered. Functional metagenomics based on activity screening is a classical way to retrieve these genes from microbiomes. This approach is based on the insertion of large metagenomic DNA fragments into a vector and transformation of a host to express heterologous genes. Metagenomic libraries are then screened for activities of interest, and the metagenomic DNA inserts of active clones are extracted to be sequenced and analysed to identify genes that are responsible for the detected activity. Hundreds of metagenomics sequences found using this strategy have already been published in public databases. Here we present the MINTIA software package enabling biologists to easily generate and analyze large metagenomic sequence sets, retrieved after activity-based screening. It filters reads, performs assembly, removes cloning vector, annotates open reading frames and generates user friendly reports as well as files ready for submission to international sequence repositories. The software package can be downloaded from https://github.com/Bios4Biol/MINTIA.
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Kwon, Minji, Sang-Soo Seo, Mi Kim, Dong Lee, and Myoung Lim. "Compositional and Functional Differences between Microbiota and Cervical Carcinogenesis as Identified by Shotgun Metagenomic Sequencing." Cancers 11, no. 3 (March 5, 2019): 309. http://dx.doi.org/10.3390/cancers11030309.
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Recent studies have reported the potential role of microbiomes in cervical disease. However, little is known about the microbiome composition and function in cervical carcinogenesis. We aimed to identify the compositional and functional alterations of cervical microbiomes in cases of cervical carcinogenesis of Korean women using shotgun metagenomic sequencing. In this study, using shotgun sequencing, we sequenced the cervical metagenomes of cervical intraneoplasia 2/3 (n = 17), cervical cancer (n = 12), and normal controls (n = 18) to identify the microbial abundances and enriched metabolic functions in cervical metagenomes. At the genus level, the microbiota of cervical cancer were differentially enriched with genera Alkaliphilus, Pseudothermotoga, and Wolbachia. Cervical intraepithelial neoplasia (CIN) 2/3 were enriched with Lactobacillus, Staphylococcus, and Candidatus Endolissoclinum. The normal group was enriched with Pseudoalteromonas and Psychrobacter. Further characterization of the functionalities of the metagenomes may suggest that six Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologies (KOs) that are involved in 10 pathways are associated with an increased risk of CIN2/3 and cervical cancer. Specifically, cervical metagenomes were enriched in the course of peptidoglycan synthesis and depleted by dioxin degradation and 4-oxalocrotonate tautomerase. The Cluster of Orthologous Groups (COG) category ‘Defense mechanisms’ was depleted in cervical cancer patients. Our findings based on shotgun metagenomic sequencing suggest that cervical microbiome community compositions and their metagenomics profiles differed between cervical lesions and normal subjects. Future studies should have larger sample sizes and/or aggregate their results to have sufficient power to detect reproducible and significant associations.
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Van Damme, Renaud, Martin Hölzer, Adrian Viehweger, Bettina Müller, Erik Bongcam-Rudloff, and Christian Brandt. "Metagenomics workflow for hybrid assembly, differential coverage binning, metatranscriptomics and pathway analysis (MUFFIN)." PLOS Computational Biology 17, no. 2 (February 9, 2021): e1008716. http://dx.doi.org/10.1371/journal.pcbi.1008716.
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Metagenomics has redefined many areas of microbiology. However, metagenome-assembled genomes (MAGs) are often fragmented, primarily when sequencing was performed with short reads. Recent long-read sequencing technologies promise to improve genome reconstruction. However, the integration of two different sequencing modalities makes downstream analyses complex. We, therefore, developed MUFFIN, a complete metagenomic workflow that uses short and long reads to produce high-quality bins and their annotations. The workflow is written by using Nextflow, a workflow orchestration software, to achieve high reproducibility and fast and straightforward use. This workflow also produces the taxonomic classification and KEGG pathways of the bins and can be further used for quantification and annotation by providing RNA-Seq data (optionally). We tested the workflow using twenty biogas reactor samples and assessed the capacity of MUFFIN to process and output relevant files needed to analyze the microbial community and their function. MUFFIN produces functional pathway predictions and, if provided de novo metatranscript annotations across the metagenomic sample and for each bin. MUFFIN is available on github under GNUv3 licence: https://github.com/RVanDamme/MUFFIN.
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Jiang, Song, Jie Nie, Yuxing Chen, Shiying Zhang, Xiaoyan Wang, and Feng Chen. "Dataset for Genome Sequencing and De Novo Assembly of the Candidate Phyla Radiation in Supragingival Plaque." Canadian Journal of Infectious Diseases and Medical Microbiology 2022 (March 19, 2022): 1–10. http://dx.doi.org/10.1155/2022/4899824.
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The Candidate Phyla Radiation (CPR), as a newly discovered and difficult-to-culture bacterium, accounts for the majority of the bacterial domain, which may be related to various oral diseases, including dental caries. Restricted by laboratory culture conditions, there is limited knowledge about oral CPR. Advances in metagenomics provide a new way to study CPR through molecular biology. Here, we used metagenomic assembly and binning to reconstruct more and higher quality metagenome-assembled genomes (MAGs) of CPR from oral dental plaque. These MAGs represent novel CPR species, which differed from all known CPR organisms. Relative abundance of different CPR MAGs in the caries and caries-free group was estimated by mapping metagenomic reads to newly constructed MAGs. The relative abundance of two CPR MAGs was significantly increased in the caries group, indicating that there might be a relationship with caries activity. The detection of a large number of unclassified CPR MAGs in the dataset implies that the phylogenetic diversity of CPR is enormous. The results provide a reference value for exploring the ecological distribution and function of uncultured or difficult-to-culture microorganisms.
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Gilroy, Rachel, Joy Leng, Anuradha Ravi, Evelien M. Adriaenssens, Aharon Oren, Dave Baker, Roberto M. La Ragione, Christopher Proudman, and Mark J. Pallen. "Metagenomic investigation of the equine faecal microbiome reveals extensive taxonomic diversity." PeerJ 10 (March 23, 2022): e13084. http://dx.doi.org/10.7717/peerj.13084.
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Background The horse plays crucial roles across the globe, including in horseracing, as a working and companion animal and as a food animal. The horse hindgut microbiome makes a key contribution in turning a high fibre diet into body mass and horsepower. However, despite its importance, the horse hindgut microbiome remains largely undefined. Here, we applied culture-independent shotgun metagenomics to thoroughbred equine faecal samples to deliver novel insights into this complex microbial community. Results We performed metagenomic sequencing on five equine faecal samples to construct 123 high- or medium-quality metagenome-assembled genomes from Bacteria and Archaea. In addition, we recovered nearly 200 bacteriophage genomes. We document surprising taxonomic diversity, encompassing dozens of novel or unnamed bacterial genera and species, to which we have assigned new Candidatus names. Many of these genera are conserved across a range of mammalian gut microbiomes. Conclusions Our metagenomic analyses provide new insights into the bacterial, archaeal and bacteriophage components of the horse gut microbiome. The resulting datasets provide a key resource for future high-resolution taxonomic and functional studies on the equine gut microbiome.
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Robinson, Serina L., Jörn Piel, and Shinichi Sunagawa. "A roadmap for metagenomic enzyme discovery." Natural Product Reports, 2021. http://dx.doi.org/10.1039/d1np00006c.
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Shotgun metagenomic approaches to uncover new enzymes are underdeveloped relative to PCR- or activity-based functional metagenomics. Here we review computational and experimental strategies to discover biosynthetic enzymes from metagenomes.
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Patel, Tithi, Hiral G. Chaudhari, Vimalkumar Prajapati, Swati Patel, Vaibhavkumar Mehta, and Niti Soni. "A brief account on enzyme mining using metagenomic approach." Frontiers in Systems Biology 2 (December 14, 2022). http://dx.doi.org/10.3389/fsysb.2022.1046230.
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Metagenomics is an approach for directly analyzing the genomes of microbial communities in the environment. The use of metagenomics to investigate novel enzymes is critical because it allows researchers to acquire data on microbial diversity, with a 99% success rate, and different kinds of genes encode an enzyme that has yet to be found. Basic metagenomic approaches have been created and are widely used in numerous studies. To promote the success of the advance research, researchers, particularly young researchers, must have a fundamental understanding of metagenomics. As a result, this review was conducted to provide a thorough insight grasp of metagenomics. It also covers the application and fundamental methods of metagenomics in the discovery of novel enzymes, focusing on recent studies. Moreover, the significance of novel biocatalysts anticipated from varied microbial metagenomes and their relevance to future research for novel industrial applications, the ramifications of Next-Generation Sequencing (NGS), sophisticated bio-informatic techniques, and the prospects of the metagenomic approaches are discussed. The current study additionally explores metagenomic research on enzyme exploration, specifically for key enzymes like lipase, protease, and cellulase of microbial origin.
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Guerrini, Veronica, Felipe A. Louza, and Giovanna Rosone. "Metagenomic analysis through the extended Burrows-Wheeler transform." BMC Bioinformatics 21, S8 (September 2020). http://dx.doi.org/10.1186/s12859-020-03628-w.
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Abstract Background The development of Next Generation Sequencing (NGS) has had a major impact on the study of genetic sequences. Among problems that researchers in the field have to face, one of the most challenging is the taxonomic classification of metagenomic reads, i.e., identifying the microorganisms that are present in a sample collected directly from the environment. The analysis of environmental samples (metagenomes) are particularly important to figure out the microbial composition of different ecosystems and it is used in a wide variety of fields: for instance, metagenomic studies in agriculture can help understanding the interactions between plants and microbes, or in ecology, they can provide valuable insights into the functions of environmental communities. Results In this paper, we describe a new lightweight alignment-free and assembly-free framework for metagenomic classification that compares each unknown sequence in the sample to a collection of known genomes. We take advantage of the combinatorial properties of an extension of the Burrows-Wheeler transform, and we sequentially scan the required data structures, so that we can analyze unknown sequences of large collections using little internal memory. The tool LiME (Lightweight Metagenomics via eBWT) is available at https://github.com/veronicaguerrini/LiME. Conclusions In order to assess the reliability of our approach, we run several experiments on NGS data from two simulated metagenomes among those provided in benchmarking analysis and on a real metagenome from the Human Microbiome Project. The experiment results on the simulated data show that LiME is competitive with the widely used taxonomic classifiers. It achieves high levels of precision and specificity – e.g. 99.9% of the positive control reads are correctly assigned and the percentage of classified reads of the negative control is less than 0.01% – while keeping a high sensitivity. On the real metagenome, we show that LiME is able to deliver classification results comparable to that of MagicBlast. Overall, the experiments confirm the effectiveness of our method and its high accuracy even in negative control samples.
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Motro, Yair, Denise Wajnsztajn, Ayelet Michael-Gayego, Shubham Mathur, Roberto BM Marano, Ikram Salah, Chaggai Rosenbluh, Violeta Temper, Jacob Strahilevitz, and Jacob Moran-Gilad. "Metagenomic sequencing for investigation of a national keratoconjunctivitis outbreak, Israel, 2022." Eurosurveillance 28, no. 31 (August 3, 2023). http://dx.doi.org/10.2807/1560-7917.es.2023.28.31.2300010.
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Background Epidemics of keratoconjunctivitis may involve various aetiological agents. Microsporidia are an uncommon difficult-to-diagnose cause of such outbreaks. Aim During the third quarter of 2022, a keratoconjunctivitis outbreak was reported across Israel, related to common water exposure to the Sea of Galilee. We report a comprehensive diagnostic approach that identified Vittaforma corneae as the aetiology, serving as proof of concept for using real-time metagenomics for outbreak investigation. Methods Corneal scraping samples from a clinical case were subjected to standard microbiological testing. Samples were tested by calcofluor white staining and metagenomic short-read sequencing. We analysed the metagenome for taxonomical assignment and isolation of metagenome-assembled genome (MAG). Targets for a novel PCR were identified, and the assay was applied to clinical and environmental samples and confirmed by long-read metagenomic sequencing. Results Fluorescent microscopy was suggestive of microsporidiosis. The most abundant species (96.5%) on metagenomics analysis was V. corneae. Annotation of the MAG confirmed the species assignment. A unique PCR target in the microsporidian rRNA gene was identified and validated against the clinical sample. The assay and metagenomic sequencing confirmed V. corneae in an environmental sludge sample collected at the exposure site. Conclusions The real-time utilisation of metagenomics allowed species detection and development of diagnostic tools, which aided in outbreak source tracking and can be applied for future cases. Metagenomics allows a fully culture-independent investigation and is an important modality for public health microbiology.
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Liu, Pu, Shuofeng Hu, Zhen He, Chao Feng, Guohua Dong, Sijing An, Runyan Liu, Fang Xu, Yaowen Chen, and Xiaomin Ying. "Towards Strain-Level Complexity: Sequencing Depth Required for Comprehensive Single-Nucleotide Polymorphism Analysis of the Human Gut Microbiome." Frontiers in Microbiology 13 (May 5, 2022). http://dx.doi.org/10.3389/fmicb.2022.828254.
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Intestinal bacteria strains play crucial roles in maintaining host health. Researchers have increasingly recognized the importance of strain-level analysis in metagenomic studies. Many analysis tools and several cutting-edge sequencing techniques like single cell sequencing have been proposed to decipher strains in metagenomes. However, strain-level complexity is far from being well characterized up to date. As the indicator of strain-level complexity, metagenomic single-nucleotide polymorphisms (SNPs) have been utilized to disentangle conspecific strains. Lots of SNP-based tools have been developed to identify strains in metagenomes. However, the sufficient sequencing depth for SNP and strain-level analysis remains unclear. We conducted ultra-deep sequencing of the human gut microbiome and constructed an unbiased framework to perform reliable SNP analysis. SNP profiles of the human gut metagenome by ultra-deep sequencing were obtained. SNPs identified from conventional and ultra-deep sequencing data were thoroughly compared and the relationship between SNP identification and sequencing depth were investigated. The results show that the commonly used shallow-depth sequencing is incapable to support a systematic metagenomic SNP discovery. In contrast, ultra-deep sequencing could detect more functionally important SNPs, which leads to reliable downstream analyses and novel discoveries. We also constructed a machine learning model to provide guidance for researchers to determine the optimal sequencing depth for their projects (SNPsnp, https://github.com/labomics/SNPsnp). To conclude, the SNP profiles based on ultra-deep sequencing data extend current knowledge on metagenomics and highlights the importance of evaluating sequencing depth before starting SNP analysis. This study provides new ideas and references for future strain-level investigations.
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Song, Kai. "Reads Binning Improves the Assembly of Viral Genome Sequences From Metagenomic Samples." Frontiers in Microbiology 12 (May 21, 2021). http://dx.doi.org/10.3389/fmicb.2021.664560.
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Metagenomes can be considered as mixtures of viral, bacterial, and other eukaryotic DNA sequences. Mining viral sequences from metagenomes could shed insight into virus–host relationships and expand viral databases. Current alignment-based methods are unsuitable for identifying viral sequences from metagenome sequences because most assembled metagenomic contigs are short and possess few or no predicted genes, and most metagenomic viral genes are dissimilar to known viral genes. In this study, I developed a Markov model-based method, VirMC, to identify viral sequences from metagenomic data. VirMC uses Markov chains to model sequence signatures and construct a scoring model using a likelihood test to distinguish viral and bacterial sequences. Compared with the other two state-of-the-art viral sequence-prediction methods, VirFinder and PPR-Meta, my proposed method outperformed VirFinder and had similar performance with PPR-Meta for short contigs with length less than 400 bp. VirMC outperformed VirFinder and PPR-Meta for identifying viral sequences in contaminated metagenomic samples with eukaryotic sequences. VirMC showed better performance in assembling viral-genome sequences from metagenomic data (based on filtering potential bacterial reads). Applying VirMC to human gut metagenomes from healthy subjects and patients with type-2 diabetes (T2D) revealed that viral contigs could help classify healthy and diseased statuses. This alignment-free method complements gene-based alignment approaches and will significantly improve the precision of viral sequence identification.
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Silva, Raíssa, Kleber Padovani, Fabiana Góes, and Ronnie Alves. "geneRFinder: gene finding in distinct metagenomic data complexities." BMC Bioinformatics 22, no. 1 (February 25, 2021). http://dx.doi.org/10.1186/s12859-021-03997-w.
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Abstract Background Microbes perform a fundamental economic, social, and environmental role in our society. Metagenomics makes it possible to investigate microbes in their natural environments (the complex communities) and their interactions. The way they act is usually estimated by looking at the functions they play in those environments and their responsibility is measured by their genes. The advances of next-generation sequencing technology have facilitated metagenomics research however it also creates a heavy computational burden. Large and complex biological datasets are available as never before. There are many gene predictors available that can aid the gene annotation process though they lack handling appropriately metagenomic data complexities. There is no standard metagenomic benchmark data for gene prediction. Thus, gene predictors may inflate their results by obfuscating low false discovery rates. Results We introduce geneRFinder, an ML-based gene predictor able to outperform state-of-the-art gene prediction tools across this benchmark by using only one pre-trained Random Forest model. Average prediction rates of geneRFinder differed in percentage terms by 54% and 64%, respectively, against Prodigal and FragGeneScan while handling high complexity metagenomes. The specificity rate of geneRFinder had the largest distance against FragGeneScan, 79 percentage points, and 66 more than Prodigal. According to McNemar’s test, all percentual differences between predictors performances are statistically significant for all datasets with a 99% confidence interval. Conclusions We provide geneRFinder, an approach for gene prediction in distinct metagenomic complexities, available at gitlab.com/r.lorenna/generfinder and https://osf.io/w2yd6/, and also we provide a novel, comprehensive benchmark data for gene prediction—which is based on The Critical Assessment of Metagenome Interpretation (CAMI) challenge, and contains labeled data from gene regions—available at https://sourceforge.net/p/generfinder-benchmark.