Relevant bibliographies by topics / Metagenomic

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Metagenomic'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Metagenomic"

1

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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2

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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3

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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More sources

Dissertations / Theses on the topic "Metagenomic"

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Meyer, Quinton Christian. "Metagenomic approaches to gene discovery." Thesis, University of the Western Cape, 2006. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_7031_1182747173.

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The classical approach to gene discovery has been to culture micro-organisms demonstrating a specific enzyme activity and then to recover the gene of interest through shotgun cloning. The realization that these standard microbiological methods provide limited access to the true microbial biodiversity and therefore the available microbial genetic diversity (collectively termed the Metagenome) has resulted in the development of environmental nucleic acid extraction technologies designed to access this wealth of genetic information, thereby avoiding the limitations of culture dependent genetic exploitation. In this work several gene discovery technologies was employed in an attempt to recover novel bacterial laccase genes (EC 1.10.3.2), a group of enzymes in which considerable biotechnological interest has been expressed. Metagenomic DNA extracted from two organic rich environmental samples was used as the source material for the construction of two genomic DNA libraries. The small insert plasmid based library derived from compost DNA consisted of approximately 106 clones at an average insert size of 2.7Kbp, equivalent to 2.6 Gbp of cloned environmental DNA. A Fosmid based large insert library derived from grape waste DNA consisted of approximately 44000 cfu at an average insert size of 25Kbp (1.1 Gbp cloned DNA). Both libraries were screened for laccase activity but failed to produce novel laccase genes. As an alternative approach, a multicopper oxidase specific PCR detection assay was developed using a laccase positive Streptomyces strain as a model organism. The newly designed primers were used to detect the presence of bacterial multicopper oxidases in environmental samples. This resulted in the identification of nine novel gene fragments showing identity ranging from 37 to 94% to published putative bacterial multicopper oxidase gene sequences. Three clones pMCO6, pMCO8 and pMCO9 were significantly smaller than those typically reported for bacterial laccases and were assigned to a recently described clade of Streptomyces bacterial multicopper oxidases.


Two PCR based techniques were employed to attempt the recovery of flanking regions for two of these genes (pMCO7 and pMCO8). The use of TAIL-PCR resulted in the recovery of 90% of the pMCO7 ORF. As an alternative approach the Vectorette&trade
system was employed to recover the 3&rsquo
downstream region of pMCO8. The complexity of the DNA sample proved to be a considerable technical challenge for the implementation of both these techniques. The feasibility of both these approaches were however demonstrated in principle. Finally, in an attempt to expedite the recovery of fulllength copies of these genes a subtractive hybridization magnetic bead capture technique was adapted and employed to recover a full &ndash
length putative multicopper oxidase gene from a Streptomyces strain in a proof of concept experiment. The StrepA06pMCO gene fragment was used as a &lsquo
driver&rsquo
against fragmented Streptomyces genomic DNA (&lsquo
tester&rsquo
) and resulted in the recovery of a 1215 bp open reading frame. Unexpectedly, this ORF showed only 80% identity to the StrepA06pMCO gene sequence at nucleotide level, and 48% amino acid identity to a putative mco gene derived from a Norcardioides sp JS614.

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2

Gaspar, John M. "Denoising amplicon-based metagenomic data." Thesis, University of New Hampshire, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3581214.

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Reducing the effects of sequencing errors and PCR artifacts has emerged as an essential component in amplicon-based metagenomic studies. Denoising algorithms have been written that can reduce error rates in mock community data, in which the true sequences are known, but they were designed to be used in studies of real communities. To evaluate the outcome of the denoising process, we developed methods that do not rely on a priori knowledge of the correct sequences, and we applied these methods to a real-world dataset. We found that the denoising algorithms had substantial negative side-effects on the sequence data. For example, in the most widely used denoising pipeline, AmpliconNoise, the algorithm that was designed to remove pyrosequencing errors changed the reads in a manner inconsistent with the known spectrum of these errors, until one of the parameters was increased substantially from its default value.

With these shortcomings in mind, we developed a novel denoising program, FlowClus. FlowClus uses a systematic approach to filter and denoise reads efficiently. When denoising real datasets, FlowClus provides feedback about the process that can be used as the basis to adjust the parameters of the algorithm to suit the particular dataset. FlowClus produced a lower error rate compared to other denoising algorithms when analyzing a mock community dataset, while retaining significantly more sequence information. Among its other attributes, FlowClus can analyze longer reads being generated from current protocols and irregular flow orders. It has processed a full plate (1.5 million reads) in less than four hours; using its more efficient (but less precise) trie analysis option, this time was further reduced, to less than seven minutes.

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Devakandan, Keshini. "Metagenomic characterization of the vaginal microbiome." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/60127.

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Background: The vaginal microbiome is a dynamic environment colonized by a wide array of microorganisms. Although bacterial vaginosis (BV) is characterized by a disruption in the normal bacterial microbiome of the vagina, the factors contributing to recurrent BV remain unknown. In addition, very little is known about the role of viruses in the vaginal microbiome and associated dysbioses. Objectives: 1) characterize the vaginal bacteriome of women with recurrent BV using cpn60 sequencing, compare bacterial profiles to healthy-asymptomatic cohort, and correlate profiles to descriptive characteristics; and 2) characterize the vaginal virome of healthy-asymptomatic, HIV-positive women and women with recurrent BV, and correlate profiles to descriptive characteristics. Methods: Twenty-six women were recruited into the recurrent BV bacteriome study. Vaginal swabs were obtained for cpn60 sequencing and Gram stain Nugent scoring. Additionally, samples from 54 women were analyzed in the virome study: 21 healthy-asymptomatic, 25 HIV-positive and eight recurrent BV. The vaginal swabs were processed to enrich for viruses and then subjected to metagenomics shotgun sequencing. Demographic, behavioural and clinical information was collected for all participants, in both bacteriome and virome studies. Results: Bacteriome analyses detected 122 cpn60 operational taxonomic units (OTUs). Bacterial profiles clustered into six community state types (CSTs). Trends suggested a relationship between BV-associated CSTs and number of sexual partners (past year), oral sex, use of (hormonal) contraception, abnormal discharge (past 48 hours), lifetime history of trichomoniasis, and number of BV episodes (past two months and year). Virome analyses detected a total of 477 species. Viral profiles clustered into seven groups. Viral patterns were identified within bacteriome CSTs, Nugent scores, viral loads, between Lactobacillus-dominant, Lactobacillus iners-dominant, and heterogeneous profiles, and were associated with a number of descriptive characteristics. Conclusions: The vaginal microbiome is highly diverse and potentially associated with many clinical factors. Our ability to use the microbiome data to subdivide women into clusters, and detect trends between clusters and characteristics will expand our knowledge on the vaginal microbiome as a whole.
Medicine, Faculty of
Graduate
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Mewis, Keith. "Functional metagenomic screening for glycoside hydrolases." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/60223.

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Limitations on the cultivation of a majority of naturally occurring microbes have spurred the rise of culture-independent methods for the investigation of environmental microbial communities, a field known as metagenomics. This thesis addresses both functional and informatic approaches to metagenomics with the aim of improving our knowledge of carbohydrate degradation. A high throughput functional metagenomic screen was developed and applied to over 350,000 fosmid clones to search for glycoside hydrolases (GHs) in metagenomic libraries. Screening yielded 798 fosmid clones capable of hydrolyzing a model sugar compound, and the genes responsible were subcloned and biochemically characterized for pH and temperature stability, and substrate specificity. The combination of functional and in silico methods developed were used in a longitudinal study of the beaver (Castor canadensis) digestive tract, in order to gain insight into the sequential degradation of biomass. A linear model was used to identify enrichment of endo-acting versus exo-acting GH families at five locations throughout the digestive tract. The discovery of high numbers of GH43 family genes on functionally identified fosmids resulted in their combination with all other known GH43 genes in order to create subfamily classifications that provide finer resolution of enzyme activities. This classification system resulted in an improved ability to assign functional characteristics to enzymes identified through informatic studies. Of the 37 subfamilies created, only 22 contained a characterized enzyme. Fosmids identified earlier in this work harboured genes from four uncharacterized GH43 subfamilies, and future characterization efforts will further our understanding of the GH43 family. Altogether, the developed methods provide a framework for future studies of biomass degradation and improve the power of both functional and in silico metagenomics.
Science, Faculty of
Graduate
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5

Bench, Shellie R. "Metagenomic characterization of Chesapeake Bay virioplankton." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file, 78 p, 2007. http://proquest.umi.com/pqdweb?did=1338865971&sid=6&Fmt=2&clientId=8331&RQT=309&VName=PQD.

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Davis, Carina. "Metagenomic approaches to microbial source tracking." Thesis, University of Canterbury. School of Biological Sciences, 2013. http://hdl.handle.net/10092/8194.

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Water sources are susceptible to faecal contamination from animal and human pollution sources. Pollution of our waterways has significant implications on human health, especially from a pathogen perspective. Microbial source tracking (MST) is a promising field which aims to identify the sources of faecal contamination, and thereby allowing for the development of effective management strategies to minimise pollution and the impact on human health. Many of the currently used methods rely on the identification of host-specific markers within the 16S ribosomal RNA (rRNA) gene of bacteria by use of amplification techniques such as polymerase chain reaction (PCR). However, these methods can be limited by sensitivity, quantification, geographical differences and issues of cost which can limit how many markers are evaluated. Developments in DNA sequencing technologies over the past decade have led to a number of next generation sequencing (NGS) platforms which have a rapid, high throughput approach, resulting in an exponential decrease in the cost of sequencing. This has enabled the development of sequence-based metagenomics, where entire communities from environmental samples can be analysed based on their genetic material. The ability to barcode allows for analysis of multiple samples at once, reducing the cost of sequencing environmental samples even further. This is a promising technique for MST, which has had little investigation to date. The primary focus of the studies described in this thesis was to evaluate the use of NGS technology through a metagenomic approach. Roche 454 amplicon sequencing was used to sequence a 16S rRNA gene target, amplified from faecal and water samples from various sources in New Zealand. Barcode strategies were incorporated in the amplification design to allow multiple samples to be sequenced simultaneously. A proof-of-concept study initially utilised a small sequence dataset to evaluate a range of analysis tools available. Taxonomic identification and diversity measures were used to evaluate a selection of currently available tools designed for analysing metagenomic data, with the Quantitative Insights Into Microbial Ecology (QIIME) platform decided upon for further studies. A larger study, including 35 faecal samples from 13 difference sources and 10 water samples, resulted in 522,065 raw sequencing reads. Diversity results suggest three phyla, Bacteroidetes, Firmicutes and Proteobacteria, are strongly represented across all faecal sources analysed. Microbial diversity analysis using clustering techniques provided evidence of host source being the largest influence on bacterial diversity, with samples from each source generally clustering together. This technique could not be used to identify sources of contamination sources in water samples as the water samples all clustered separately from the faecal samples. More successful was the use of taxonomic classifications to determine bacteria genera that were potentially specific to one source. Water samples were screened for these genera, with six out of the ten water samples being indicators of either ruminant or human contamination. Faecal and water samples were also analysed for a selection of published 16S rRNA PCR markers, using a computational motif-based search method. Of the twenty motifs screened for, 14 were found to be relatively source-specific for ruminant, human, dog or pig faecal samples, with some cross-reactivity with chicken and possum samples. Using this method, the contamination source for six of the ten water samples was identified, with the remaining four samples found to not have enough sequences to assess with confidence. Both metagenomic strategies produced comparable results which were consistent with previous MST analysis. This project demonstrates the potential application of next generation sequencing technologies to microbial source tracking, suggesting the possibility this approach to replace existing microbial source tracking methods.
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7

Chung, Ryan Kyong-doc. "Deep learning approach to metagenomic binning." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119755.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 39-41).
Understanding the diversity and abundance of microbial populations is paramount to the health of humans and the environment. Estimating the diversity of these populations from whole metagenome shotgun (WMS) sequencing reads is difficult because the size of these datasets and overlapping reads limit what kinds of analysis we can do. Current methods require matching reads to a database of known microbes. These methods are either too slow or lack the sensitivity needed to identify novel species. We propose a convolutional neural network (CNN) based approach to metagenomic binning that embeds reads into a low-dimensional vector space based on taxonomic classification. We show that our method can get the speed and sensitivity necessary taxonomic classification. Our method was able to achieve 13% accuracy on identifying novel genus of bacteria as compared to 7% accuracy of k-mer embedding. At the same time, the speed of our method is within an order of magnitude of that of k-mer embedding, making it viable as a metagenomic analysis tool.
by Ryan Kyong-doc Chung.
M. Eng.
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8

Prost, Vincent. "Sparse unsupervised learning for metagenomic data." Electronic Thesis or Diss., université Paris-Saclay, 2020. http://www.theses.fr/2020UPASL013.

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Les avancées technologiques dans le séquençage ADN haut débit ont permis à la métagénomique de considérablement se développer lors de la dernière décennie. Le séquencage des espèces directement dans leur milieu naturel a ouvert de nouveaux horizons dans de nombreux domaines de recherche. La réduction des coûts associée à l'augmentation du débit fait que de plus en plus d'études sont lancées actuellement.Dans cette thèse nous considérons deux problèmes ardus en métagénomique, à savoir le clustering de lectures brutes et l'inférence de réseaux microbiens. Pour résoudre ces problèmes, nous proposons de mettre en oeuvre des méthodes d'apprentissage non supervisées utilisant le principe de parcimonie, ce qui prend la forme concrète de problèmes d'optimisation avec une pénalisation de norme l1.Dans la première partie de la thèse, on considère le problème intermédiaire du clustering des séquences ADN dans des partitions biologiquement pertinentes (binning). La plupart des méthodes computationelles n'effectuent le binning qu'après une étape d'assemblage qui est génératrice d'erreurs (avec la création de contigs chimériques) et de pertes d'information. C'est pourquoi nous nous penchons sur le problème du binning sans assemblage préalable. Nous exploitons le signal de co-abondance des espèces au travers des échantillons mesuré via le comptage des k-mers (sous-séquences de taille k) longs. L'utilisation du Local Sensitive Hashing (LSH) permet de contenir, au coût d'une approximation, l'explosion combinatoire des k-mers possibles dans un espace de cardinal fixé. La première contribution de la thèse est de proposer l'application d'une factorisation en matrices non-négatives creuses (sparse NMF) sur la matrice de comptage des k-mers afin de conjointement extraire une information de variation d'abondance et d'effectuer le clustering des k-mers. Nous montrons d'abord le bien fondé de l'approche au niveau théorique. Puis, nous explorons dans l'état de l'art les méthodes de sparse NMF les mieux adaptées à notre problème. Les méthodes d'apprentissage de dictionnaire en ligne ont particulièrement retenu notre attention de par leur capacité à passer à l'échelle pour des jeux de données comportant un très grand nombre de points. La validation des méthodes de binning en métagénomique sur des données réelles étant difficile à cause de l'absence de vérité terrain, nous avons créé et utilisé plusieurs jeux de données synthétiques pour l'évaluation des différentes méthodes. Nous montrons que l'application de la sparse NMF améliore les méthodes de l'état de l'art pour le binning sur ces jeux de données. Des expérience sur des données métagénomiques réelles issus de 1135 échantillons de microbiotes intestinaux d'individus sains ont également été menées afin de montrer la pertinence de l'approche.Dans la seconde partie de la thèse, on considère les données métagénomiques après le profilage taxonomique, c'est à dire des donnés multivariées représentant les niveaux d'abondance des taxons au sein des échantillons. Les microbes vivant en communautés structurées par des interactions écologiques, il est important de pouvoir identifier ces interactions. Nous nous penchons donc sur le problème de l'inférence de réseau d'interactions microbiennes à partir des profils taxonomiques. Ce problème est souvent abordé dans le cadre théorique des modèles graphiques gaussiens (GGM), pour lequel il existe des algorithmes d'inférence puissants tel que le graphical lasso. Mais les méthodes statistiques existantes sont très limitées par l'aspect extrêmement creux des profils taxonomiques que l'on rencontre en métagénomique, notamment par la grande proportion de zéros dits biologiques (i.e. liés à l'absence réelle de taxons). Nous proposons un model log normal avec inflation de zéro visant à traiter ces zéros biologiques et nous montrons un gain de performance par rapport aux méthodes de l'état de l'art pour l'inférence de réseau d'interactions microbiennes
The development of massively parallel sequencing technologies enables to sequence DNA at high-throughput and low cost, fueling the rise of metagenomics which is the study of complex microbial communities sequenced in their natural environment.Metagenomic problems are usually computationally difficult and are further complicated by the massive amount of data involved.In this thesis we consider two different metagenomics problems: 1. raw reads binning and 2. microbial network inference from taxonomic abundance profiles. We address them using unsupervised machine learning methods leveraging the parsimony principle, typically involving l1 penalized log-likelihood maximization.The assembly of genomes from raw metagenomic datasets is a challenging task akin to assembling a mixture of large puzzles composed of billions or trillions of pieces (DNA sequences). In the first part of this thesis, we consider the related task of clustering sequences into biologically meaningful partitions (binning). Most of the existing computational tools perform binning after read assembly as a pre-processing, which is error-prone (yielding artifacts like chimeric contigs) and discards vast amounts of information in the form of unassembled reads (up to 50% for highly diverse metagenomes). This motivated us to try to address the raw read binning (without prior assembly) problem. We exploit the co-abundance of species across samples as discriminative signal. Abundance is usually measured via the number of occurrences of long k-mers (subsequences of size k). The use of Local Sensitive Hashing (LSH) allows us to contain, at the cost of some approximation, the combinatorial explosion of long k-mers indexing. The first contribution of this thesis is to propose a sparse Non-Negative Matrix factorization (NMF) of the samples x k-mers count matrix in order to extract abundance variation signals. We first show that using sparse NMF is well-grounded since data is a sparse linear mixture of non-negative components. Sparse NMF exploiting online dictionary learning algorithms retained our attention, including its decent behavior on largely asymmetric data matrices. The validation of metagenomic binning being difficult on real datasets, because of the absence of ground truth, we created and used several benchmarks for the different methods evaluated on. We illustrated that sparse NMF improves state of the art binning methods on those datasets. Experiments conducted on a real metagenomic cohort of 1135 human gut microbiota showed the relevance of the approach.In the second part of the thesis, we consider metagenomic data after taxonomic profiling: multivariate data representing abundances of taxa across samples. It is known that microbes live in communities structured by ecological interaction between the members of the community. We focus on the problem of the inference of microbial interaction networks from taxonomic profiles. This problem is frequently cast into the paradigm of Gaussian graphical models (GGMs) for which efficient structure inference algorithms are available, like the graphical lasso. Unfortunately, GGMs or variants thereof can not properly account for the extremely sparse patterns occurring in real-world metagenomic taxonomic profiles. In particular, structural zeros corresponding to true absences of biological signals fail to be properly handled by most statistical methods. We present in this part a zero-inflated log-normal graphical model specifically aimed at handling such "biological" zeros, and demonstrate significant performance gains over state-of-the-art statistical methods for the inference of microbial association networks, with most notable gains obtained when analyzing taxonomic profiles displaying sparsity levels on par with real-world metagenomic datasets
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Schuch, Viviane [UNESP]. "Construção de biblioteca metagenômica para prospecção de genes envolvidos na biossíntese de antibióticos." Universidade Estadual Paulista (UNESP), 2007. http://hdl.handle.net/11449/94940.

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Made available in DSpace on 2014-06-11T19:27:23Z (GMT). No. of bitstreams: 0 Previous issue date: 2007-02-28Bitstream added on 2014-06-13T20:47:49Z : No. of bitstreams: 1 schuch_v_me_jabo.pdf: 3089029 bytes, checksum: 0835ef08e49e97cfdf7ad571bdfc3671 (MD5)
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
Metabólitos secundários são compostos bioativos, com grande importância para a indústria farmacêutica e agropecuária, produzidos por certos grupos de microrganismos e plantas. Os policetídeos, que são sintetizados por complexos enzimáticos denominados policetídeos sintases (PKSs), desatacam-se entre os metabólitos secundários conhecidos e compõe a estrutura química básica de vários antibióticos. Todos os genes envolvidos na biossíntese de um policetídeo se encontram agrupados fisicamente no cromossomo, e contém genes que são altamente conservados, comumente chamados d~ pks mínima. Os métodos tradicionais para pesquisa de novas drogas, que envolvem o cultivo de microrganismos isolados do solo, não são mais tão promissores, devido à alta taxa de redescoberta de antibióticos já conhecidos, que chega a 99,9%, e à pequena parcela de microrganismos do solo que são cultiváveis pelas técnicas padrões de cultivo, cerca de 1 %. A Metagenômica é uma abordagem promissora que permite acessar o genoma desses organismos incultiváveis, pois consiste na extração de DNA diretamente do ambiente e construção de uma biblioteca com este genoma misto. Neste trabalho descrevemos a construção de uma biblioteca feita com DNA de alto peso molecular isolado diretamente de solo coletado sob arboreto de eucaliptos no Estado de São Paulo, Brasil. A biblioteca possui 9.320 clones e foi construída em vetor cosmídeo, com insertos de tamanho variando entre 30 e 45kb...
Secondary metabolites are bioactive compounds with great importance in the pharmaceutical and agriculture industries, procuced by a few groups of microrganisms and plants. The polyketides that are synthetized by enzimatic complexes, denominated polyketides synthases, outstand among the secondary known metabolites, which are part of the main structure of many antibiotics. Ali genes involved in the biosynthesis of antibiotics are found as clusters in the chromossome. The traditional methods for the research of new drugs that are made from microrganisms cultures isolated from the soil are not so promissing, due to the high rate of rediscorevy of already known species, reaching 99.9%. The other small piece of microrganisms are culturable by standards culture methods, reaching 1 % maximum. Metagenomics is a promissing approach that allows the access to genom of these organisms that are not culturable, as it is carried out by DNA extraction directly from the environment and construction of a mixed genomic library. In this work, we describe the construction of a library made from high molecular weight DNA isolated directly form the soi! undemeath a pinus forest in the State of São Paulo, Brazil. The library shows 9.320 dones and it was constructed in a cosmideo vector, with insert size ranging from 30 to 45 kb. Digestion with difterent restriction enzymes of cosmidial DNA randomly chosen allowed to visualize evident difterences in the restriction fragments among the clones, as does the possibility to determine the average insert size. The initial evaluation of the presence of genes involved in the biosynthesis of antibiotics synthesized by the enzymatic system PKS of kind I, was accomplished by the PCR amplification of clones from the library using specific primers. We studied 4.320 clones and the results suggest a great variety of these genes. The PCR products obtained were sequenced for the determination of identity of the amplified gene.
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Morfopoulou, S. "Bayesian mixture models for metagenomic community profiling." Thesis, University College London (University of London), 2015. http://discovery.ucl.ac.uk/1473450/.

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Metagenomics can be defined as the study of DNA sequences from environmental or community samples. This is a rapidly progressing field and application ideas that seemed outlandish a few years ago are now routine and familiar. Metagenomics’ scope is broad and includes the analysis of a diverse set of samples such as environmental or clinical samples. Human tissues are in essence metagenomic samples due to the presence of microorganisms, such as bacteria, viruses and fungi in both healthy and diseased individuals. Deep sequencing of clinical samples is now an established tool for pathogen detection, with direct medical applications. The large amount of data generated produces an opportunity to detect species even at very low levels, provided that computational tools can effectively profile the relevant metagenomic communities. Data interpretation is complicated by the fact that short sequencing reads can match multiple organisms and by the lack of completeness of existing databases, particularly for viruses. The research presented in this thesis focuses on using Bayesian Mixture Model techniques to produce taxonomic profiles for metagenomic data. A novel Bayesian mixture model framework for resolving complex metagenomic mixtures is introduced, called metaMix. The use of parallel Monte Carlo Markov chains (MCMC) for the exploration of the species space enables the identification of the set of species most likely to contribute to the mixture. The improved accuracy of metaMix compared to relevant methods is demonstrated, particularly for profiling complex communities consisting of several related species. metaMix was designed specifically for the analysis of deep transcriptome sequencing datasets, with a focus on viral pathogen detection. However, the principles are generally applicable to all types of metagenomic mixtures. metaMix is implemented as a user friendly R package, freely available on CRAN: http://cran.r-project.org/web/packages/metaMix.
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Books on the topic "Metagenomic"

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Singh, Shailza, ed. Metagenomic Systems Biology. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8562-3.

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Mitra, Suparna, ed. Metagenomic Data Analysis. New York, NY: Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-3072-3.

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Nelson, Karen E. Metagenomics of the human body. New York: Springer, 2011.

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Streit, Wolfgang R., and Rolf Daniel, eds. Metagenomics. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6691-2.

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Streit, Wolfgang R., and Rolf Daniel, eds. Metagenomics. Totowa, NJ: Humana Press, 2010. http://dx.doi.org/10.1007/978-1-60761-823-2.

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Streit, Wolfgang R., and Rolf Daniel, eds. Metagenomics. New York, NY: Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-2795-2.

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Gojobori, Takashi, Tokio Wada, Takanori Kobayashi, and Katsuhiko Mineta, eds. Marine Metagenomics. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8134-8.

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Pantaleo, Vitantonio, and Michela Chiumenti, eds. Viral Metagenomics. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7683-6.

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Pantaleo, Vitantonio, and Laura Miozzi, eds. Viral Metagenomics. New York, NY: Springer US, 2024. http://dx.doi.org/10.1007/978-1-0716-3515-5.

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Nelson, Karen E., ed. Encyclopedia of Metagenomics. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6418-1.

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Book chapters on the topic "Metagenomic"

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Wang, Zhang, Jie-Liang Liang, Li-Nan Huang, Alessio Mengoni, and Wen-Sheng Shu. "Metagenomic Assembly: Reconstructing Genomes from Metagenomes." In Methods in Molecular Biology, 139–52. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1099-2_9.

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Huson, Daniel H. "MEtaGenome ANalyzer (MEGAN): Metagenomic Expert Resource." In Encyclopedia of Metagenomics, 383–89. Boston, MA: Springer US, 2015. http://dx.doi.org/10.1007/978-1-4899-7478-5_4.

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Huson, Daniel H. "MEtaGenome ANalyzer (MEGAN): Metagenomic Expert Resource." In Encyclopedia of Metagenomics, 1–8. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6418-1_4-1.

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Raffaetà, Roberta. "The Microbial Ecosystem at the Crossroads Between Disciplines." In Metagenomic Futures, 183–200. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003222965-9.

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Raffaetà, Roberta. "Conclusion." In Metagenomic Futures, 201–9. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003222965-10.

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Raffaetà, Roberta. "Microbes and Health." In Metagenomic Futures, 37–64. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003222965-3.

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Raffaetà, Roberta. "What Are Microbes?" In Metagenomic Futures, 19–36. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003222965-2.

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Raffaetà, Roberta. "“Overselling the Microbiome”." In Metagenomic Futures, 141–65. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003222965-7.

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Raffaetà, Roberta. "The Microbiome, Genetics and Postgenomics." In Metagenomic Futures, 166–82. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003222965-8.

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Raffaetà, Roberta. "The Ethics and Politics of the Pragmatic Approach." In Metagenomic Futures, 124–40. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003222965-6.

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Conference papers on the topic "Metagenomic"

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Lipovac, Josipa, and Krešimir Križanović. "Using De Novo Metagenome Assembly for Improved Metagenomic Classification." In 2023 46th MIPRO ICT and Electronics Convention (MIPRO). IEEE, 2023. http://dx.doi.org/10.23919/mipro57284.2023.10159902.

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"PREDICTED RELATIVE METABOLOMIC TURNOVER - Predicting Changes in the Environmental Metabolome from the Metagenome." In Metagenomic Sequence Data Analysis. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003314803370345.

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"ANNOTATING UniProt METAGENOMIC AND ENVIRONMENTAL SEQUENCES IN UniMES." In Metagenomic Sequence Data Analysis. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003350803670368.

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"AUTOMATIC ANNOTATION OF BACTERIAL COMMUNITY SEQUENCES AND APPLICATION TO INFECTIONS DIAGNOSTIC." In Metagenomic Sequence Data Analysis. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003333703460353.

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"INFRASTRUCTURE FOR METAGENOME DATA MANAGEMENT AND ANALYSIS." In Metagenomic Sequence Data Analysis. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003333803570362.

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"PROPOSAL FOR OPEN DISCUSSION - Informatics Challenges for Next Generation Sequencing Metagenomics Experiments." In Metagenomic Sequence Data Analysis. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003334203630366.

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Mathias, Marina Barrionuevo, Fernando Gatti, Gustavo Bruniera, Vitor Paes, Gisele Sampaio Silva, Pedro Braga-Neto, Alcino Barbosa, Augusto Penalva, and Livia Almeida Dutra. "Neurobrucellosis mimicking primary vasculitis of the central nervous system: we should perform a metagenomic analysis of the cerebrospinal fluid prior to brain biopsy." In XIII Congresso Paulista de Neurologia. Zeppelini Editorial e Comunicação, 2021. http://dx.doi.org/10.5327/1516-3180.422.

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Context Primary angiitis of the central nervous system (PACNS) is characterized by the inflammation of small and medium CNS arteries; the clinical manifestations include headache, cognitive impairment and focal neurological deficits. The gold standard test for diagnosis is brain biopsy. Neurobrucellosis is an infection associated with cattle farming, which leads to neurological and psychiatric symptoms. We report a case of neurobrucellosis mimicking PACNS. Case report Male, 32 years old, with fever, headache, dizziness and cognitive impairments for 30 days. History of stroke 2 years before, with mild sequelae right hemiparesis; investigation showed suspected intracranial dissection. On physical examination, he had apathy, preserved strength, reduced reflexes with plantar flexor responses. General laboratory tests, autoantibodies and serology were normal. Brain MRI showed deep left nucleocapsular gliosis and cerebral angiography revealed stenosis of the ICA and MCA. CSF showed 42 cells/ mm³, glucose 46 mg/dL, protein 82 mg/dL. Blood PCR was negative for Brucella. Immunophenotyping of the CSF and PET-CT excluded neoplasia. Brain biopsy was inconclusive for vasculitis. Metagenomic analysis of the CSF detected 78% of Brucella genetic material. Serum agglutination test was 1:40 for brucella. Conclusions PACNS is diagnosed by exclusion. The patient filled criteria for possible PACNS, image compatible with vascular stenosis, but inconclusive brain biopsy. Brucellosis is an endemic disease in underdeveloped countries that can present as CNS vasculitis. Metagenomic analysis allows the detection of different pathogens using a single method. The case illustrates the use of metagenomics in rare diseases characterized by vasculitis, with change in clinical outcomes and conduct.
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Pop, Mihai. "Invited: Challenges in metagenomic assembly." In 2011 IEEE 1st International Conference on Computational Advances in Bio and Medical Sciences (ICCABS). IEEE, 2011. http://dx.doi.org/10.1109/iccabs.2011.5729950.

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Lux, Markus, Alexander Sczyrba, and Barbara Hammer. "Automatic discovery of metagenomic structure." In 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, 2015. http://dx.doi.org/10.1109/ijcnn.2015.7280500.

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Ditzler, Gregory, Robi Polikar, and Gail Rosen. "Determining significance in metagenomic samples." In 2012 38th Annual Northeast Bioengineering Conference (NEBEC). IEEE, 2012. http://dx.doi.org/10.1109/nebc.2012.6207004.

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Reports on the topic "Metagenomic"

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Brigmon, R., C. Turick, and C. Burckhalter. Metagenomic Analysis of Three Samples from the MCU Process. Office of Scientific and Technical Information (OSTI), April 2019. http://dx.doi.org/10.2172/1508736.

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Lai, Qiang, Tao Cheng, Wentao Yang, Tianyong Han, and Shuyun Xu. The diagnostic value of metagenomic next-generation sequencing in sepsis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, June 2022. http://dx.doi.org/10.37766/inplasy2022.6.0008.

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David Kirchman. Metagenomic analysis of uncultured Cytophaga and beta-1,4 glycanases in marine consortia. Office of Scientific and Technical Information (OSTI), December 2005. http://dx.doi.org/10.2172/861432.

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Beckstrom-Sternberg, Stephen. Bioinformatic Tools for Metagenomic Analysis of Pathogen Backgrounds and Human Microbial Communities. Fort Belvoir, VA: Defense Technical Information Center, May 2010. http://dx.doi.org/10.21236/ada581677.

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D'haeseleer, P. FY08 LDRD Final Report Probabilistic Inference of Metabolic Pathways from Metagenomic Sequence Data. Office of Scientific and Technical Information (OSTI), March 2009. http://dx.doi.org/10.2172/948980.

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McLoughlin, K. Technical Report: Benchmarking for Quasispecies Abundance Inference with Confidence Intervals from Metagenomic Sequence Data. Office of Scientific and Technical Information (OSTI), January 2016. http://dx.doi.org/10.2172/1237578.

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McLoughlin, K. Technical Report on Modeling for Quasispecies Abundance Inference with Confidence Intervals from Metagenomic Sequence Data. Office of Scientific and Technical Information (OSTI), January 2016. http://dx.doi.org/10.2172/1237573.

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McLoughlin, Kevin. Technical Report: Algorithm and Implementation for Quasispecies Abundance Inference with Confidence Intervals from Metagenomic Sequence Data. Office of Scientific and Technical Information (OSTI), January 2016. http://dx.doi.org/10.2172/1237568.

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Liao, Jiadan. Comparison of metagenomic next-generation sequencing technology and GeneXpert MTB/RIF assay in tuberculosis:a meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, April 2023. http://dx.doi.org/10.37766/inplasy2023.4.0111.

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Guo, Qiang, Xiulin Ye, Xiaoxing Ge, Xiaoji Su, and Shihai Zhang. Metagenomic Next Generation Sequencing for the Diagnosis pathogeny of Respiratory Infection : A Systematic Review and Meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, August 2021. http://dx.doi.org/10.37766/inplasy2021.8.0036.

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