Relevant bibliographies by topics / Metagenomics

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Academic literature on the topic 'Metagenomics'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 June 2025

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Journal articles on the topic "Metagenomics"

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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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Nalbantoglu, O. Ufuk. "Information Theoretic Metagenome Assembly Allows the Discovery of Disease Biomarkers in Human Microbiome." Entropy 23, no. 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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New, Felicia N., and Ilana L. Brito. "What Is Metagenomics Teaching Us, and What Is Missed?" Annual Review of Microbiology 74, no. 1 (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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Uritskiy, Gherman, and Jocelyne DiRuggiero. "Applying Genome-Resolved Metagenomics to Deconvolute the Halophilic Microbiome." Genes 10, no. 3 (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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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 (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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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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YE, YUZHEN, and HAIXU TANG. "AN ORFOME ASSEMBLY APPROACH TO METAGENOMICS SEQUENCES ANALYSIS." Journal of Bioinformatics and Computational Biology 07, no. 03 (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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Nam, Nguyen Nhat, Hoang Dang Khoa Do, Kieu The Loan Trinh, and Nae Yoon Lee. "Metagenomics: An Effective Approach for Exploring Microbial Diversity and Functions." Foods 12, no. 11 (2023): 2140. http://dx.doi.org/10.3390/foods12112140.

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Various fields have been identified in the “omics” era, such as genomics, proteomics, transcriptomics, metabolomics, phenomics, and metagenomics. Among these, metagenomics has enabled a significant increase in discoveries related to the microbial world. Newly discovered microbiomes in different ecologies provide meaningful information on the diversity and functions of microorganisms on the Earth. Therefore, the results of metagenomic studies have enabled new microbe-based applications in human health, agriculture, and the food industry, among others. This review summarizes the fundamental procedures on recent advances in bioinformatic tools. It also explores up-to-date applications of metagenomics in human health, food study, plant research, environmental sciences, and other fields. Finally, metagenomics is a powerful tool for studying the microbial world, and it still has numerous applications that are currently hidden and awaiting discovery. Therefore, this review also discusses the future perspectives of metagenomics.
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Simon, Carola, and Rolf Daniel. "Metagenomic Analyses: Past and Future Trends." Applied and Environmental Microbiology 77, no. 4 (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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Alam, Khorshed, Muhammad Nazeer Abbasi, Jinfang Hao, Youming Zhang, and Aiying Li. "Strategies for Natural Products Discovery from Uncultured Microorganisms." Molecules 26, no. 10 (2021): 2977. http://dx.doi.org/10.3390/molecules26102977.

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Microorganisms are highly regarded as a prominent source of natural products that have significant importance in many fields such as medicine, farming, environmental safety, and material production. Due to this, only tiny amounts of microorganisms can be cultivated under standard laboratory conditions, and the bulk of microorganisms in the ecosystems are still unidentified, which restricts our knowledge of uncultured microbial metabolism. However, they could hypothetically provide a large collection of innovative natural products. Culture-independent metagenomics study has the ability to address core questions in the potential of NP production by cloning and analysis of microbial DNA derived directly from environmental samples. Latest advancements in next generation sequencing and genetic engineering tools for genome assembly have broadened the scope of metagenomics to offer perspectives into the life of uncultured microorganisms. In this review, we cover the methods of metagenomic library construction, and heterologous expression for the exploration and development of the environmental metabolome and focus on the function-based metagenomics, sequencing-based metagenomics, and single-cell metagenomics of uncultured microorganisms.
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Dissertations / Theses on the topic "Metagenomics"

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Pope, Phillip Byron. "Metagenomics of Cyanobacterial Blooms." Thesis, Griffith University, 2007. http://hdl.handle.net/10072/368095.

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Cyanobacteria are a diverse and widely distributed group of organisms common in soil and in both marine and freshwater. Under favorable conditions they can reproduce explosively, forming dense concentrations called blooms. Fresh water cyanobacterial blooms in particular are commonly associated with toxin production in drinking water supplies and are increasingly becoming a risk to human health. Beyond toxin production these extremely complex, constantly interacting and changing microbial communities have vast impacts on their surrounding ecosystem. The triggers that initiate bloom formation and/or toxin production remain poorly understood. This stems from the fact that there is still very little known of cyanobacterial bloom population structure and their function in the real environment. A greater understanding of the interactions of different microbial populations and their functions in the blooming process leading to toxin production could come from using metagenomics to investigate the genetic and metabolic diversity of the mixed populations rather than the difficult to culture cyanobacteria. Two distinct cyanobacterial bloom communities existing in contrasting Australian freshwater lakes were selected and high molecular weight DNA extracted. PCR-amplified 16S rRNA genes were subsequently cloned and a total of 75 clones from Lake Samsonvale and 50 clones from Lake Ainsworth were examined. Sequences identified belonged to species from 6 different phyla from the Bacterial domain, including Cyanobacteria, Actinobacteria, Firmicutes, Verrucomicrobium, Bacteroidetes, and á-, â- and ã-Proteobacteria. The majority of the bacterial sequences were most closely related to sequences recovered from other freshwater clones or isolates (80% homology), whilst few were closely related to sequences recovered from soil or marine habitats. In particular 9 % of the total sequences were most closely related to sequences recovered from freshwater lakes that are susceptible to cyanobacterial blooms. A total of 12 novel clusters consisting of 22 sequences were noted spanning all divisions represented in the analysis. Of this, 7 were found to lack any close relatives suggesting that sequences in these clusters may be characteristic for bloom events. Preliminary results also indicate that physio-chemical differences in lake character appear to influence bacterial community composition associated with cyanobacterial blooms. Bloom communities from Lake Samsonvale demonstrated high levels of toxinproducing Cyanobacteria and uncultured Actinobacteria. These findings were used to justify its selection for further metagenomic analysis to gain insights into the genomes of these and other organisms. DNA was fractionated and used to construct a bacterial artificial chromosome library (CBNPD1) of 2,850 clones which had an average insert size of 27 kb. A PCR-based single-gene polyketide synthase library was constructed in tandem and used as an additional assurance that high quality DNA was being extracted and cloned. Phylogenetic analysis of gene sequences recovered from this library demonstrated an abundance of novel bacterial polyketide synthase genes. Sequence-based screening of library CBNPD1 was performed to identify clones of interest and provide a physiological insight within cyanobacterial blooms. A random BAC-end sequence survey generated 67 sequences (40 kb in total) from 36 randomly selected clones. G+C composition ranged from 33.33 to 72.91%. Fifteen sequence tags (22%) were found most similar to sequences affiliated to genera with no available genome. Another 17 sequence tags (25%) were most similar to sequences affiliated to genera with available genomes, however similarities were less than 80%. Sequence tags were also found with affiliation to proteins involved in a wide array of cell metabolism processes including amino acid metabolism (e.g. methionine synthase), carbohydrate metabolism (cellulose), inorganic ion metabolism (nitrite/sulfite reductase), and lipid metabolism (fatty acid hydroxylase). A number of genes involved in cell structures (e.g. flagella), DNA processes, energy production (photosynthetic reaction center L subunit) and defense mechanisms (nucleases) were also affiliated to sequence tags. PCR screening of CBNPD1 was used to detect clones containing 16S rDNA to establish a link between physiological and phylogenetic information of uncharacterized microorganisms in cyanobacterial blooms. Screens from 480 clones identified 2 clones containing a 16S rRNA gene. Clone 545 and 578 contained 16S rDNA affiliated to 2 different phylogenetic genera within the Proteobacteria division, Pseudomonas and Roseateles respectively. From library screens 7 BAC inserts were selected and sequenced to completion comprising 144 kb of a cyanobacterial bloom metagenome and spanning 3 phyla including Proteobacteria, Actinobacteria and Bacteroidetes. 130 genes have been identified and assigned to COG (clusters of orthologous groups of proteins) functional categories. Also identified, were many housekeeping proteins spanning the majority of the COG functional groups as well as physiologically and ecologically important proteins some of which were looked at more in depth. These include a putative phenylacetyl catabolon, a putative RTX toxin, several putative oxidoreductases and several putative bacterial transcriptional regulators that are inferred in controling a wide variety of activities in various biological processes, the most notable being quorum sensing. This culture-independent experimental approach has provided a phylogenetic community snap shot of the cyanobacterial bloom community structure and their physiological functions within the bloom. Moreover it represents an important biodiversity resource which has already been shown to contain novel biomolecular biodiversity.<br>Thesis (PhD Doctorate)<br>Doctor of Philosophy (PhD)<br>School of Biomolecular and Biomedical Sciences<br>Faculty of Science<br>Full Text
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Pozuelo, del Río Marta. "Metagenomics in inflammatory bowel disease." Doctoral thesis, Universitat Autònoma de Barcelona, 2019. http://hdl.handle.net/10803/669437.

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La microbiota intestinal desempeña un papel crucial en el manteniendo la homeostasis intesitnal. Alteraciones en la composición microbiana, también conocidas como disbisosis, pueden poner en peligro el estado de salud e incrementar el riesgo a padecer una enfermedad. Aunque muchas enfermedades se han asociado a cambios en la microbiota intestinal, todavía se desconoce si dichas alteraciones son la causa o la consecuencia de las patologías. La enfermedad inflamatoria intestinal (EII) es una enfermedad inflamatoria crónica que se caracteriza por periodos de inflamación y constituye un problema de salud dado. La EII presenta dos subtipos: enfermedad de Crohn y colitis ulcerosa, con síntomas similares pero diferentes manifestaciones clínicas. La EII se ha relacionado ampliamente con cambios en la microbiota intestinal. A pesar de los múltiples estudios que existen, no hay un claro consenso en el perfil microbiano asociado a la enfermedad. Las principales discordancias se dan entre las diferencias asociadas a enfermedad de Crohn y la colitis ulcerosa. Algunos investigadores han demostrado que la composición microbiana en colitis ulcerosa es muy similar a la de individuos sanos y ambas difieren de la composición de enfermos de Crohn. En cambio, otros investigadores han visto que las diferencias de colitis ulcerosa y Crohn respecto a sanos son muy similares por lo que consideran ambos subtipos como una única enfermedad (EII). El principal objetivo de esta tesis es determinar la disbiosis en una cohorte de EII española para evaluar hasta qué punto las funciones y composición microbiana difieren entre Crohn y colitis y si los datos de microbioma podrían emplearse como herramientas de diagnóstico. Para ello, analizamos muestras fecales de sanos, enfermos de Crohn y enfermos de colitis usando dos metodologías: secuenciación del gen 16SARNr (o 16S ADNr) y secuenciación por fragmentación del genoma. Como se preveía, observamos la presencia de disbiosis en EII. Además, vimos que las alteraciones en composición microbiana y funciones eran diferentes para Crohn que para colitis, mostrando una mayor disbiosis en Crohn que en enfermos de colitis ulcerosa y con colitis mostrando un patrón muy similar a la microbiota de individuos sanos. Los resultados funcionales encontrados en esta tesis confirman la mayor disbiosis descrita en pacientes de Crohn en comparación con pacientes de colitis ulcerosa en composición microbiana. Estos individuos presentan una mayor cantidad de genes principalmente asociados a metabolismo y enfermedades inmunes que los enfermos de colitis ulcerosa y sanos. A pesar de que los datos de 16S ADNr y secuenciación por fragmentación no detectaron las mismas diferencias entre Crohn y colitis, ambas metodologías permitieron la clasificación de los distintos subtipos de EII con una proporción similar. Más estudios son necesarios para validar los resultados de esta tesis en otras cohortes de pacientes que incluyan Crohn localizado en colon o pacientes recién diagnosticados que no hayan sido sometidos a tratamiento antes de la aplicación de estas metodologías como herramientas diagnósticas en clínica.<br>The gut commensal microbiota is known to play a crucial role in maintaining intestinal homeostasis. Alterations in the microbial community composition, also known as dysbiosis, may put health status in risk and increase susceptibility to diseases. Although several diseases have been related to shifts in the gut microbiome composition, it is still uncertain whether those alterations are the cause or consequence of the disease. Inflammatory bowel disease (IBD) is a chronic inflammatory disease with periods of active and inactive inflammation that constitutes to an important health problem. It is divided in two subtypes: Crohn’s disease (CD) and ulcerative colitis (UC) that present similar symptoms but different clinical manifestations. IBD has been widely associated with an alteration of the gut microbiome composition. Nevertheless, there is no clear consensus on the microbial pattern characteristic of the disorders. Main discordances between studies are related to differences between UC and CD. Some previous publications indicate that UC microbial composition is very similar to healthy and differs from CD whereas others consider both subtypes as a unique entity and find high alterations in UC and CD microbial composition in comparison with the microbiome of healthy individuals. The aim of this thesis was to characterize the dysbiosis in a Spanish IBD cohort to evaluate to which extend the gut microbiome composition and function could be differentiated between CD and UC and whether microbiome data could be used as diagnostic and prognostic tools. For this purpose, we analyzed fecal samples of healthy individuals, CD (affected in the ileum) and UC patients using two different methodologies: 16S rRNA gene (or 16S rDNA) and shotgun (short genomic fragments) sequencing. As expected, we observed the presence of dysbiosis in IBD. Furthermore, we showed that microbial composition and function alterations were different for CD and UC, with greater dysbiosis in CD than in UC and with UC resembling more to a healthy state. Functional findings also confirmed this higher dysbiosis in CD than in UC and revealed genes implicated in metabolism pathways and in immune diseases in higher abundance in CD compared with healthy individuals and UC. Although 16S rDNA and shotgun data did not detect differences in the dysbiosis in CD and UC in a consistent manner, both methodologies allowed the classification of IBD subtypes in a similar proportion. Future studies should validate these results using other patient cohorts such as colonic CD or recently diagnosed patients before the application of these techniques as diagnostic tools in clinical practice.
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Mitsi, Konstantina 1990. "Eukaryotic diversity through the lens of metabarcoding and metagenomics." Doctoral thesis, TDX (Tesis Doctorals en Xarxa), 2021. http://hdl.handle.net/10803/671809.

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Los organismos eucariotas abarcan una diversidad inmensa de formas, tamaños y estilos de vida. Sin embargo, abordar la verdadera amplitud de esa diversidad es una tarea laboriosa. Durante las últimas decadas, los estudios de biodiversidad han experimentado un progreso importante gracias a la incorporación de técnicas basadas en la secuenciación del ADN. Esta tesis es una composición de tres proyectos independientes donde estas técnicas se aplican con el objetivo de ampliar nuestro conocimiento de la biodiversidad eucariota. En el primer proyecto, obtenemos el genoma nuclear y el genoma mitocondrial de un parásito a partir de un metagenoma eucariota. Definimos la posición filogenética de este nuevo organismo que se posiciona junto con Filasterea, un grupo de organismos unicelulares que está estrechamente relacionado con los animales y es clave para estudiar la transición a la multicelularidad animal. El análisis del contenido génico muestra que el nuevo organismo posee un genoma reducido en comparación con los otros Filasterea. A pesar de eso, su genoma codifica un flagelo completo y muchas proteínas que están relacionadas con la multicelularidad en los animales. En el segundo proyecto, buscamos diversidad molecular no descrita dentro de los platelmintos, uno de los filos animales más diversos e importantes desde un punto de vista biomédico. Con este fin, analizamos datos globales de metabarcoding del gen 18S del ADN ribosomal procedentes de hábitats marinos y de agua dulce. Nuestros resultados muestran que gran parte de la diversidad molecular de los platelmintos sigue sin estar documentada e identifican los habitats de agua dulce como puntos donde buscar nuevas especies de platelmintos. Por último, en el tercer proyecto, investigamos la novedad a nivel molecular, la composición taxonómica y la estructura de la comunidad eucariota del lago Sanabria, un lago oligotrófico. Secuenciamos la región hipervariable V4 del gen 18S del ADN ribosmal y demostramos cómo la elección de los métodos analíticos (ASVs o OTUs) afectan los resultados y las conclusiones sacadas por un estudio de biodiversida.. En conjunto, nuestros resultados amplían nuestra perspectiva de la diversidad eucariota y mejoran nuestra comprensión de la distribución, la ecología, la novedad molecular y los rasgos genómicos de los eucariotas.<br>Eukaryotes encompass an unprecedented diversity of forms, sizes and lifestyles. However, tackling the real breadth of that diversity is a challenging task. In the last decades, the assessment of biodiversity has seen substantial progress due to the incorporation of culture-independent techniques based on Next Generation DNA Sequencing. This thesis is a composition of three independent projects that implement these techniques aiming to expand our understanding of the extant eukaryotic biodiversity. In the first project, we obtain the nuclear and mitochondrial genomes of Txikispora philomaios, an uncultured unicellular parasite, using a metagenomic approach. We define the phylogenetic position of T.philomaios that branches within Filasterea, a group of unicellular eukaryotes that is closely related to animals and is key to elucidate the transition to animal multicellularity. Despite T.philomaios possessing a reduced genome in comparison to other Filasterea, it has a complete flagellar toolkit and its genome encodes many proteins that are related to multicellularity in animals. In the second project, we seek undescribed molecular diversity inside the phylum Platyhelminthes, one of the most diverse and biomedically important animal phyla. To this end, we analyze global metabarcoding data of the 18S rDNA gene from marine and freshwater habitats. Our results show that a large part of the molecular diversity of Platyhelminthes remains undocumented and identify freshwater environments as potential reservoirs for novel species of flatworms. Finally, in the third project, we investigate the molecular novelty, the taxonomic composition and the structure of the eukaryotic community in Sanabria Lake by sequencing the V4 hypervariable region of the 18S rDNA gene. We show to which extent the choice of the analytical methods (ASVs or OTUs) affects the final results and conclusions of a biodiversity survey. Altogether, our results broaden our perspective of eukaryotic diversity and enhance our understanding of the distribution, the ecology, the molecular novelty and the genomic traits of eukaryotes
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Robidart, Julie Christine. "Metagenomics of the Riftia pachyptila symbiont." Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2006. http://wwwlib.umi.com/cr/ucsd/fullcit?p3237569.

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Thesis (Ph. D.)--University of California, San Diego, 2006.<br>Title from first page of PDF file (viewed December 13, 2006). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references.
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Mwaigwisya, Solomon. "Culture-independent metagenomics characterisation of infection." Thesis, University of East Anglia, 2018. https://ueaeprints.uea.ac.uk/69635/.

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Next-generation sequencing (NGS) technologies are revolutionising our ability to study and characterise microorganisms and investigate infectious diseases. The potential of metagenomics sequencing for use as a single, all-inclusive diagnostic test for comprehensive detection of pathogens, resistance genes and virulence markers directly from clinical samples has been discussed at length in the literature in recent years. However, implementation has been slow as there are several challenges associated with applying metagenomics sequencing to clinical microbiology. These include the large number of human cells, the often low proportion of pathogen cells/DNA and, in some cases, the high background of normal microbiological flora present in clinical samples. Here we report rapid, culture-independent metagenomics workflows that overcome these challenges. Metagenomics pipelines were developed and evaluated in three model samples: i) blood, for the diagnosis of sepsis, ii) urine, for the diagnosis of urinary tract infections, and iii) stool, for the diagnosis of Clostridioides difficile infection. Developed workflows comprised of rapid depletion of unwanted cells/DNA (human and normal flora (in stool)), genomic DNA extraction from remaining microorganisms, whole genome amplification (in blood), rapid nanopore library preparation and real-time metagenomics analysis. These pipelines enabled comprehensive detection of pathogens and resistance genes in clinical blood samples within eight hours and in clinical urine samples within four hours. The C. difficile pipeline could enrich for and sequence the pathogen directly from stool within 24 hours. However, further optimisation of this pipeline is required to increase genome coverage before it can be utilised for typing C. difficile directly from stool. The rapid host depletion and metagenomics sequencing pipelines developed here demonstrate that this technology can provide clinicians with the necessary information to tailor antibiotic therapy for the specific infecting pathogen before second dose of empiric therapy is administered (usually eight-hour intervals), thereby improving patient outcomes and antibiotic stewardship.
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Ditzler, Gregory, J. Calvin Morrison, Yemin Lan, and Gail L. Rosen. "Fizzy: feature subset selection for metagenomics." BioMed Central, 2015. http://hdl.handle.net/10150/610268.

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BACKGROUND: Some of the current software tools for comparative metagenomics provide ecologists with the ability to investigate and explore bacterial communities using α- & β-diversity. Feature subset selection - a sub-field of machine learning - can also provide a unique insight into the differences between metagenomic or 16S phenotypes. In particular, feature subset selection methods can obtain the operational taxonomic units (OTUs), or functional features, that have a high-level of influence on the condition being studied. For example, in a previous study we have used information-theoretic feature selection to understand the differences between protein family abundances that best discriminate between age groups in the human gut microbiome. RESULTS: We have developed a new Python command line tool, which is compatible with the widely adopted BIOM format, for microbial ecologists that implements information-theoretic subset selection methods for biological data formats. We demonstrate the software tools capabilities on publicly available datasets. CONCLUSIONS: We have made the software implementation of Fizzy available to the public under the GNU GPL license. The standalone implementation can be found at http://github.com/EESI/Fizzy.
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Abdul, Wahab Ahmad Hakeem. "Statistical Discovery of Biomarkers in Metagenomics." Thesis, The University of Arizona, 2015. http://hdl.handle.net/10150/566996.

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Metagenomics holds unyielding potential in uncovering relationships within microbial communities that have yet to be discovered, particularly because the field circumvents the need to isolate and culture microbes from their natural environmental settings. A common research objective is to detect biomarkers, microbes are associated with changes in a status. For instance, determining such microbes across conditions such as healthy and diseased groups for instance allows researchers to identify pathogens and probiotics. This is often achieved via analysis of differential abundance of microbes. The problem is that differential abundance analysis looks at each microbe individually without considering the possible associations the microbes may have with each other. This is not favorable, since microbes rarely act individually but within intricate communities involving other microbes. An alternative would be variable selection techniques such as Lasso or Elastic Net which considers all the microbes simultaneously and conducts selection. However, Lasso often selects only a representative feature of a correlated cluster of features and the Elastic Net may incorrectly select unimportant features too frequently and erratically due to high levels of sparsity and variation in the data.\par In this research paper, the proposed method AdaLassop is an augmented variable selection technique that overcomes the misgivings of Lasso and Elastic Net. It provides researchers with a holistic model that takes into account the effects of selected biomarkers in presence of other important biomarkers. For AdaLassop, variable selection on sparse ultra-high dimensional data is implemented using the Adaptive Lasso with p-values extracted from Zero Inflated Negative Binomial Regressions as augmented weights. Comprehensive simulations involving varying correlation structures indicate that AdaLassop has optimal performance in the presence multicollinearity. This is especially apparent as sample size grows. Application of Adalassop on a Metagenome-wide study of diabetic patients reveals both pathogens and probiotics that have been researched in the medical field.
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Dao, Quang Minh. "High performance processing of metagenomics data." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS203.

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Avec l'avènement de la technologie de séquençage de la prochaine génération, une quantité sans cesse croissante de données génomiques est produite à mesure que le coût du séquençage diminue. Cela a permis au domaine de la métagénomique de se développer rapidement. Par conséquent, la communauté bioinformatique est confrontée à des goulots d'étranglement informatiques sans précédent pour traiter les énormes ensembles de données métagénomiques. Les pipelines traditionnels de métagénomique se composent de plusieurs étapes, utilisant différentes plates-formes de calcul distribuées et parallèles pour améliorer leurs performances. Cependant, l'évolutivité de ces outils n'est pas efficace. Ils affichent de lourds frais généraux d'exécution lors du prétraitement de grandes quantités de données et ne sont pas en mesure de passer automatiquement à l'échelle supérieure pour collecter davantage de ressources informatiques. De plus, l'absence de modularité intégrée rend également leur maintenance et leur évolutivité difficiles. Ici, nous avons conçu QMSpy, une nouvelle plate-forme tout-en-un à la fois évolutive et modulaire. Dès le début, les lectures brutes de séquençage sont stockées sur stockage distribué et transformées en objets distribués, qui sont prétraités (rognés, nettoyés, filtrés, etc.), mis en correspondance avec le catalogue du génome de référence et comptés pour générer des tables d'abondance. QMSpy a été construit sur un cluster de calcul haute performance, utilisant le framework PySpark - un logiciel adaptatif qui supporte Python on Spark et étend le modèle Hadoop MapReduce. QMSpy a été testé avec des ensembles de données simulées et réelles. Dans ce pipeline, nous avons intégré des outils bioinformatiques bien connus tels que Bowtie2, Trimmomatic, Bwa, HiSat, Minimap, etc. pour traiter le séquençage des données. Notre approche prend en charge la création de workflows personnalisables en utilisant une enveloppe d'outils pour distribuer des logiciels externes dans des modules exécutables à déployer sur le cluster Spark et à exécuter en parallèle. De plus, QMSpy peut être déployé sur presque toutes les plates-formes de services informatiques à haute performance populaires telles que Google Cloud, Amazon Web Services, Microsoft Azure ou Docker et s'intégrer de manière flexible dans l'environnement d'entreprise et organisationnel tel que Hortonwork Data Platform, Salesforce, Teradata etc. En comparant QMSpy avec des ensembles de données réelles et simulées, nous avons identifié certains des facteurs les plus importants qui influencent l'exactitude du processus de quantification. Enfin, QMSpy avec ses caractéristiques telles que l'évolutivité et la modularité permettent aux bioinformaticiens de proposer de nouveaux algorithmes qui améliorent la quantification génétique, taxonomique et fonctionnelle des écosystèmes microbiens. Et nous croyons que cette ressource sera d'une grande valeur pour le domaine de la gestion de la quantitative metagenomics<br>The assessment and characterization of the gut microbiome has become a focus of research in the area of human autoimmune diseases. Many diseases such as obesity, inflammatory bowel (IBD), lean or beses twins, colorectal cancers and so on (Qin et al. 2010; Turnbaugh et al. 2009) have already been found to be associated with changes in the human microbiome. To investigate these relationships, quantitative metagenomics (QM) studies based on sequencing data could be performed. Understanding the role of the microbiome in human health and how it can be modulated is becoming increasingly relevant for precision medicine and for the medical management of chronic diseases. Results from such QM studies which report the organisms present in the samples and profile their abundances, will be used for continuous analyses. The terms microbiome and microbiota are used indistinctly to describe the community of microorganisms that live in a given environment. The development of high-throughput DNA sequencing technologies has boosted microbiome research through the study of microbial genomes allowing a more precise quantification of microbial and functional abundance. However, microbiome data analysis is challenging because it involves high-dimensional structured multivariate sparse data and because of its compositional structure of microbiome data. The data preprocessing is typically implemented as a pipeline (workflow) with third-party software that each process input files and produces output files. The pipelines are often deep, with ten or more tools, which could be very diverse from different languages such as R, Python, Perl etc. and integrated into different frameworks (Leipzig 2017) such as Galaxy, Apache Taverna, Toil etc. The challenges with existing approaches is that they are not always efficient with very large datasets in terms of scalability for individual tools in a metagenomics pipeline and their execution speed also has not met the expectations of the bioinformaticians. To date, more and more data are captured or generated from many different research areas such as Physics, Climatology, Sociology, Remote sensing or Management as well as bioinformatics. Indeed, Big Data Analytics (BDA) describes the unprecedented growth of data generated and collected from all kinds of data sources as mentioned above. This growth could be in the volume of data, in the speed of data moving in/out or in the speed of analyzing data which depends on high-performance computing (HPC) technologies. In the past few decades since the invention of the computer, HPC has contributed significantly to our quality of life - driving scientific innovation, enhancing engineering design and consumer goods manufacturing, as well as strengthening national and international security. This has been recognised and emphasised by both government and industry, with major ongoing investments in areas encompassing weather forecasting, scientific research and development as well as drug design and healthcare outcomes. In many ways, those two worlds (HPC and big data) are slowly, but surely converging. They are the keys to overcome limitations of bioinformatics analysis in general and quantitative metagenomics analysis in particular. Within the scope of this thesis, we contributed a novel bioinformatics framework and pipeline called QMSpy which helped bioinformaticians overcome limitations related to HPC and big data domains in the context of quantitative metagenomics. QMSpy tackles two challenges introduced by large scale NGS data: (i) sequencing data alignment - a computation intensive task and (ii) quantify metagenomics objects - a memory intensive task. By leveraging the powerful distributed computing engine (Apache Spark), in combination with the workflow management of big data processing (Hortonwork Data Platform), QMSpy allows us not only to bypass [...]
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Iwaloye, Opeoluwa Favour. "Metagenomics and Metatranscriptomics of Lake Erie Ice." Bowling Green State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1626157167377268.

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Bulancea, Lindvall Oscar. "Quantum Methods for Sequence Alignment and Metagenomics." Thesis, KTH, Tillämpad fysik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-256349.

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Books on the topic "Metagenomics"

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Streit, Wolfgang R., and Rolf Daniel, eds. Metagenomics. 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. 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. 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. 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. 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. 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. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6418-1.

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De Mandal, Surajit, Amrita Kumari Panda, Nachimuthu Senthil Kumar, Satpal Singh Bisht, and Fengliang Jin. Metagenomics and Microbial Ecology. CRC Press, 2021. http://dx.doi.org/10.1201/9781003042570.

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Nelson, Karen E., ed. Metagenomics of the Human Body. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-7089-3.

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Charles, Trevor C., Mark R. Liles, and Angela Sessitsch, eds. Functional Metagenomics: Tools and Applications. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61510-3.

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Book chapters on the topic "Metagenomics"

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Gilbert, Jack A., Bonnie Laverock, Ben Temperton, Simon Thomas, Martin Muhling, and Margaret Hughes. "Metagenomics." In Methods in Molecular Biology. Humana Press, 2011. http://dx.doi.org/10.1007/978-1-61779-089-8_12.

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O’Malley, Maureen A. "Metagenomics." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_74.

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Srikanth, Perumalla, and Sivakumar Durairaj. "Metagenomics." In Microbial Community Studies in Industrial Wastewater Treatment. CRC Press, 2022. http://dx.doi.org/10.1201/9781003354147-1.

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Nag, Aditi, Bhavuk Gupta, and Sudipti Arora. "Metagenomics." In Microbial Community Studies in Industrial Wastewater Treatment. CRC Press, 2022. http://dx.doi.org/10.1201/9781003354147-13.

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Liebl, Wolfgang. "Metagenomics." In Encyclopedia of Geobiology. Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-1-4020-9212-1_133.

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Pal, Aruna. "Metagenomics." In Springer Protocols Handbooks. Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1818-9_15.

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Ociepa, Tomasz. "Metagenomics." In Microbial Genetics. CRC Press, 2024. http://dx.doi.org/10.1201/9781003328933-6.

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Brady, Silja, and Rolf Daniel. "Glacier Metagenomics." In Encyclopedia of Metagenomics. Springer US, 2015. http://dx.doi.org/10.1007/978-1-4899-7475-4_38.

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Gilbert, Jack. "Ocean Metagenomics." In Encyclopedia of Metagenomics. Springer US, 2015. http://dx.doi.org/10.1007/978-1-4899-7475-4_40.

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López, Manuel Fernández, Hugo C. Ramirez-Saad, Francisco Martínez-Abarca, J. Félix Aguirre-Garrido, and Nicolas Toro. "Rhizosphere Metagenomics." In Encyclopedia of Metagenomics. Springer US, 2015. http://dx.doi.org/10.1007/978-1-4899-7475-4_611.

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Conference papers on the topic "Metagenomics"

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D’Ugo, Emilio, Roberto Giuseppetti, Fabio Magurano, et al. "Integration of Satellite Imagery and Metagenomics to Improve Water Quality Assessment." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10642700.

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Cavalcante, João Vitor F., Iara Dantas de Souza, Diego A. A. Morais, and Rodrigo J. S. Dalmolin. "EURYALE: A versatile Nextflow pipeline for taxonomic classification and functional annotation of metagenomics data." In 2024 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2024. http://dx.doi.org/10.1109/cibcb58642.2024.10702116.

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Struble, R. D., and R. Villacreses. "Pulmonary Toxoplasmosis Diagnosed With Metagenomics." In American Thoracic Society 2023 International Conference, May 19-24, 2023 - Washington, DC. American Thoracic Society, 2023. http://dx.doi.org/10.1164/ajrccm-conference.2023.207.1_meetingabstracts.a5633.

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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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Havre, S. L., B. J. Webb-Robertson, A. Shah, C. Posse, B. Gopalan, and F. J. Brockma. "Bioinformatic insights from metagenomics through visualization." In 2005 IEEE Computational Systems Bioinformatics Conference (CSB'05). IEEE, 2005. http://dx.doi.org/10.1109/csb.2005.19.

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Walsh, Paul, Cintia Palu, Brian Kelly, et al. "A metagenomics analysis of rumen microbiome." In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2017. http://dx.doi.org/10.1109/bibm.2017.8217980.

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Hassoun, Soha, Yasser El-Manzalawy, Georg Gerber, David Koslicki, and Gail Rosen. "Workshop on Microbiomics, Metagenomics, and Metabolomics." In BCB '19: 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. ACM, 2019. http://dx.doi.org/10.1145/3307339.3343860.

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Rosenboom, Ilona, Ajith Thavarasa, Hollian Richardson, et al. "Sputum metagenomics of people with bronchiectasis." In ERS Congress 2024 abstracts. European Respiratory Society, 2024. http://dx.doi.org/10.1183/13993003.congress-2024.pa2386.

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Wilkening, Jared, Andreas Wilke, Narayan Desai, and Folker Meyer. "Using clouds for metagenomics: A case study." In 2009 IEEE International Conference on Cluster Computing and Workshops. IEEE, 2009. http://dx.doi.org/10.1109/clustr.2009.5289187.

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Hassoun, Soha, and Curtis Huttenhower. "A Workshop on Microbiomics, Metagenomics, and Metabolomics." In BCB '17: 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. ACM, 2017. http://dx.doi.org/10.1145/3107411.3108172.

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Reports on the topic "Metagenomics"

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Zhou, C., and J. Smith. Viral Metagenomics: MetaView Software. Office of Scientific and Technical Information (OSTI), 2007. http://dx.doi.org/10.2172/923102.

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Hemme, Chris, Ye Deng, Qichao Tu, et al. Comparative Metagenomics of Freshwater Microbial Communities. Office of Scientific and Technical Information (OSTI), 2010. http://dx.doi.org/10.2172/985937.

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Matthew L. Bochman, Matthew L. Bochman. Mixed culture metagenomics of the microbes making sour beer. Experiment, 2019. http://dx.doi.org/10.18258/13495.

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Daily, Jeffrey A. Scalable Parallel Methods for Analyzing Metagenomics Data at Extreme Scale. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1186981.

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Wu, Cathy H., and Lynette Hirschman. BioCreative Workshops for DOE Genome Sciences: Text Mining for Metagenomics. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1330427.

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House, Geoffrey Lehman, Laverne A. Gallegos-Graves, and Patrick Sam Guy Chain. Overview of the Soil Metagenomics and Carbon Cycling SFA Fungal Collection. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1483488.

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Davenport, Karen Walston. Short papers on current state of sequencing, metagenomics, and RNAseq for diagnostics. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1503174.

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Cindy, Shi. Development of Microarrays-Based Metagenomics Technology for Monitoring Sulfate-Reducing Bacteria in Subsurface Environments. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1194725.

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Tiedje, James M. Metagenomics-enabled understanding of the functions and activities of microbial communities at ERSP field research. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1167554.

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Zhou, Jizhong, and Liyou Wu. From Structure to Functions: Metagenomics-Enabled Predictive Understanding of Soil Microbial Feedbacks to Climate Change. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1574023.

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