Academic literature on the topic 'Meta-omics'
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Journal articles on the topic "Meta-omics"
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Rusk, Nicole. "A meta-network of -omics." Nature Methods 5, no. 1 (January 2008): 25. http://dx.doi.org/10.1038/nmeth1165.
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Mackelprang, Rachel, Scott R. Saleska, Carsten Suhr Jacobsen, Janet K. Jansson, and Neslihan Taş. "Permafrost Meta-Omics and Climate Change." Annual Review of Earth and Planetary Sciences 44, no. 1 (June 29, 2016): 439–62. http://dx.doi.org/10.1146/annurev-earth-060614-105126.
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Tsoungos, Anastasios, Violeta Pemaj, Aleksandra Slavko, John Kapolos, Marina Papadelli, and Konstantinos Papadimitriou. "The Rising Role of Omics and Meta-Omics in Table Olive Research." Foods 12, no. 20 (October 15, 2023): 3783. http://dx.doi.org/10.3390/foods12203783.
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Table olives are often the result of fermentation, a process where microorganisms transform raw materials into the final product. The microbial community can significantly impact the organoleptic characteristics and safety of table olives, and it is influenced by various factors, including the processing methods. Traditional culture-dependent techniques capture only a fraction of table olives’ intricate microbiota, prompting a shift toward culture-independent methods to address this knowledge gap. This review explores recent advances in table olive research through omics and meta-omics approaches. Genomic analysis of microorganisms isolated from table olives has revealed multiple genes linked to technological and probiotic attributes. An increasing number of studies concern metagenomics and metabolomics analyses of table olives. The former offers comprehensive insights into microbial diversity and function, while the latter identifies aroma and flavor determinants. Although proteomics and transcriptomics studies remain limited in the field, they have the potential to reveal deeper layers of table olives’ microbiome composition and functionality. Despite the challenges associated with implementing multi-omics approaches, such as the reliance on advanced bioinformatics tools and computational resources, they hold the promise of groundbreaking advances in table olive processing technology.
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Sathyanarayanan, Anita, Rohit Gupta, Erik W. Thompson, Dale R. Nyholt, Denis C. Bauer, and Shivashankar H. Nagaraj. "A comparative study of multi-omics integration tools for cancer driver gene identification and tumour subtyping." Briefings in Bioinformatics 21, no. 6 (November 27, 2019): 1920–36. http://dx.doi.org/10.1093/bib/bbz121.
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Abstract Oncogenesis and cancer can arise as a consequence of a wide range of genomic aberrations including mutations, copy number alterations, expression changes and epigenetic modifications encompassing multiple omics layers. Integrating genomic, transcriptomic, proteomic and epigenomic datasets via multi-omics analysis provides the opportunity to derive a deeper and holistic understanding of the development and progression of cancer. There are two primary approaches to integrating multi-omics data: multi-staged (focused on identifying genes driving cancer) and meta-dimensional (focused on establishing clinically relevant tumour or sample classifications). A number of ready-to-use bioinformatics tools are available to perform both multi-staged and meta-dimensional integration of multi-omics data. In this study, we compared nine different integration tools using real and simulated cancer datasets. The performance of the multi-staged integration tools were assessed at the gene, function and pathway levels, while meta-dimensional integration tools were assessed based on the sample classification performance. Additionally, we discuss the influence of factors such as data representation, sample size, signal and noise on multi-omics data integration. Our results provide current and much needed guidance regarding selection and use of the most appropriate and best performing multi-omics integration tools.
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Mallick, Himel, Ali Rahnavard, Lauren J. McIver, Siyuan Ma, Yancong Zhang, Long H. Nguyen, Timothy L. Tickle, et al. "Multivariable association discovery in population-scale meta-omics studies." PLOS Computational Biology 17, no. 11 (November 16, 2021): e1009442. http://dx.doi.org/10.1371/journal.pcbi.1009442.
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It is challenging to associate features such as human health outcomes, diet, environmental conditions, or other metadata to microbial community measurements, due in part to their quantitative properties. Microbiome multi-omics are typically noisy, sparse (zero-inflated), high-dimensional, extremely non-normal, and often in the form of count or compositional measurements. Here we introduce an optimized combination of novel and established methodology to assess multivariable association of microbial community features with complex metadata in population-scale observational studies. Our approach, MaAsLin 2 (Microbiome Multivariable Associations with Linear Models), uses generalized linear and mixed models to accommodate a wide variety of modern epidemiological studies, including cross-sectional and longitudinal designs, as well as a variety of data types (e.g., counts and relative abundances) with or without covariates and repeated measurements. To construct this method, we conducted a large-scale evaluation of a broad range of scenarios under which straightforward identification of meta-omics associations can be challenging. These simulation studies reveal that MaAsLin 2’s linear model preserves statistical power in the presence of repeated measures and multiple covariates, while accounting for the nuances of meta-omics features and controlling false discovery. We also applied MaAsLin 2 to a microbial multi-omics dataset from the Integrative Human Microbiome (HMP2) project which, in addition to reproducing established results, revealed a unique, integrated landscape of inflammatory bowel diseases (IBD) across multiple time points and omics profiles.
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Adeleke, Bartholomew Saanu, and Olubukola Oluranti Babalola. "Meta-omics of endophytic microbes in agricultural biotechnology." Biocatalysis and Agricultural Biotechnology 42 (July 2022): 102332. http://dx.doi.org/10.1016/j.bcab.2022.102332.
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Darzi, Youssef, Gwen Falony, Sara Vieira-Silva, and Jeroen Raes. "Towards biome-specific analysis of meta-omics data." ISME Journal 10, no. 5 (December 1, 2015): 1025–28. http://dx.doi.org/10.1038/ismej.2015.188.
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Johnson, David R., Damian E. Helbling, Yujie Men, and Kathrin Fenner. "Can meta-omics help to establish causality between contaminant biotransformations and genes or gene products?" Environmental Science: Water Research & Technology 1, no. 3 (2015): 272–78. http://dx.doi.org/10.1039/c5ew00016e.
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Qin, Xiaofa. "Can inflammatory bowel disease really be solved by the multiple -omics and meta-omics analyses?" Immunology Letters 165, no. 2 (June 2015): 107–8. http://dx.doi.org/10.1016/j.imlet.2015.03.007.
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Cembrowska-Lech, Danuta, Adrianna Krzemińska, Tymoteusz Miller, Anna Nowakowska, Cezary Adamski, Martyna Radaczyńska, Grzegorz Mikiciuk, and Małgorzata Mikiciuk. "An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture." Biology 12, no. 10 (September 30, 2023): 1298. http://dx.doi.org/10.3390/biology12101298.
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This review discusses the transformative potential of integrating multi-omics data and artificial intelligence (AI) in advancing horticultural research, specifically plant phenotyping. The traditional methods of plant phenotyping, while valuable, are limited in their ability to capture the complexity of plant biology. The advent of (meta-)genomics, (meta-)transcriptomics, proteomics, and metabolomics has provided an opportunity for a more comprehensive analysis. AI and machine learning (ML) techniques can effectively handle the complexity and volume of multi-omics data, providing meaningful interpretations and predictions. Reflecting the multidisciplinary nature of this area of research, in this review, readers will find a collection of state-of-the-art solutions that are key to the integration of multi-omics data and AI for phenotyping experiments in horticulture, including experimental design considerations with several technical and non-technical challenges, which are discussed along with potential solutions. The future prospects of this integration include precision horticulture, predictive breeding, improved disease and stress response management, sustainable crop management, and exploration of plant biodiversity. The integration of multi-omics and AI holds immense promise for revolutionizing horticultural research and applications, heralding a new era in plant phenotyping.
Dissertations / Theses on the topic "Meta-omics"
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Zandonà, Alessandro. "Predictive networks for multi meta-omics data integration." Doctoral thesis, Università degli studi di Trento, 2017. https://hdl.handle.net/11572/367893.
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The role of microbiome in disease onset and in equilibrium is being exposed by a wealth of high-throughput omics methods. All key research directions, e.g., the study of gut microbiome dysbiosis in IBD/IBS, indicate the need for bioinformatics methods that can model the complexity of the microbial communities ecology and unravel its disease-associated perturbations. A most promising direction is the “meta-omics†approach, that allows a profiling based on various biological molecules at the metagenomic scale (e.g., metaproteomics, metametabolomics) as well as different “microbial†omes (eukaryotes and viruses) within a system biology approach. This thesis introduces a bioinformatic framework for microbiota datasets that combines predictive profiling, differential network analysis and meta-omics integration. In detail, the framework identifies biomarkers discriminating amongst clinical phenotypes, through machine learning techniques (Random Forest or SVM) based on a complete Data Analysis Protocol derived by two initiatives funded by FDA: the MicroArray Quality Control-II and Sequencing Quality Control projects. The biomarkers are interpreted in terms of biological networks: the framework provides a setup for networks inference, quantification of networks differences based on the glocal Hamming and Ipsen-Mikhailov (HIM) distance and detection of network communities. The differential analysis of networks allows the study of microbiota structural organization as well as the evolving trajectories of microbial communities associated to the dynamics of the target phenotypes. Moreover, the framework combines a novel similarity network fusion method and machine learning to identify biomarkers from the integration of multiple meta-omics data. The framework implementation requires only standard open source computational biology tools, as a combination of R/Bioconductor and Python functions. In particular, full scripts for meta-omics integration are available in a GitHub repository to ease reuse (https://github.com/AleZandona/INF). The pipeline has been validated on original data from three different clinical datasets. First, the predictive profiling and the network differential analysis have been applied on a pediatric Inflammatory Bowel Disease (IBD) cohort (in faecal vs biopsy environments) and controls, in collaboration with a multidisciplinary team at the Ospedale Pediatrico Bambino Gesú (Rome, I). Then, the meta-omics integration has been tested on a paired bacterial and fungal gut microbiota human IBD datasets from the Gastroenterology Department of the Saint Antoine Hospital (Paris, F), thanks to the collaboration with “Commensals and Probiotics-Host Interactions†team at INRA (Jouy-en-Josas, F). Finally, the framework has been validated on a bacterial-fungal gut microbiota dataset from children affected by Rett syndrome. The different nature of datasets used for validation naturally supports the extension of the framework on different omics datasets. Besides, clinical practice can take advantage of our framework, given the reproducibility and robustness of results, ensured by the adopted Data Analysis Protocol, as well as the biological relevance of the findings, confirmed by the clinical collaborators. Specifically, the omics-based dysbiosis profiles and the inferred biological networks can support the current diagnostic tools to reveal disease-associated perturbations at a much prodromal earlier stage of disease and may be used for disease prevention, diagnosis and prognosis.
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Zandonà, Alessandro. "Predictive networks for multi meta-omics data integration." Doctoral thesis, University of Trento, 2017. http://eprints-phd.biblio.unitn.it/2547/1/zandona2017_phdthesis.pdf.
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The role of microbiome in disease onset and in equilibrium is being exposed by a wealth of high-throughput omics methods. All key research directions, e.g., the study of gut microbiome dysbiosis in IBD/IBS, indicate the need for bioinformatics methods that can model the complexity of the microbial communities ecology and unravel its disease-associated perturbations. A most promising direction is the “meta-omics” approach, that allows a profiling based on various biological molecules at the metagenomic scale (e.g., metaproteomics, metametabolomics) as well as different “microbial” omes (eukaryotes and viruses) within a system biology approach. This thesis introduces a bioinformatic framework for microbiota datasets that combines predictive profiling, differential network analysis and meta-omics integration. In detail, the framework identifies biomarkers discriminating amongst clinical phenotypes, through machine learning techniques (Random Forest or SVM) based on a complete Data Analysis Protocol derived by two initiatives funded by FDA: the MicroArray Quality Control-II and Sequencing Quality Control projects. The biomarkers are interpreted in terms of biological networks: the framework provides a setup for networks inference, quantification of networks differences based on the glocal Hamming and Ipsen-Mikhailov (HIM) distance and detection of network communities. The differential analysis of networks allows the study of microbiota structural organization as well as the evolving trajectories of microbial communities associated to the dynamics of the target phenotypes. Moreover, the framework combines a novel similarity network fusion method and machine learning to identify biomarkers from the integration of multiple meta-omics data. The framework implementation requires only standard open source computational biology tools, as a combination of R/Bioconductor and Python functions. In particular, full scripts for meta-omics integration are available in a GitHub repository to ease reuse (https://github.com/AleZandona/INF). The pipeline has been validated on original data from three different clinical datasets. First, the predictive profiling and the network differential analysis have been applied on a pediatric Inflammatory Bowel Disease (IBD) cohort (in faecal vs biopsy environments) and controls, in collaboration with a multidisciplinary team at the Ospedale Pediatrico Bambino Gesú (Rome, I). Then, the meta-omics integration has been tested on a paired bacterial and fungal gut microbiota human IBD datasets from the Gastroenterology Department of the Saint Antoine Hospital (Paris, F), thanks to the collaboration with “Commensals and Probiotics-Host Interactions” team at INRA (Jouy-en-Josas, F). Finally, the framework has been validated on a bacterial-fungal gut microbiota dataset from children affected by Rett syndrome. The different nature of datasets used for validation naturally supports the extension of the framework on different omics datasets. Besides, clinical practice can take advantage of our framework, given the reproducibility and robustness of results, ensured by the adopted Data Analysis Protocol, as well as the biological relevance of the findings, confirmed by the clinical collaborators. Specifically, the omics-based dysbiosis profiles and the inferred biological networks can support the current diagnostic tools to reveal disease-associated perturbations at a much prodromal earlier stage of disease and may be used for disease prevention, diagnosis and prognosis.
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Roy, Alexandra-Sophie [Verfasser]. "Response of Thalassiosira oceanica and natural microbial communities to ocean acidification : a meta-omics comparison from unialgal cultures to mesocosms / Alexandra-Sophie Roy." Kiel : Universitätsbibliothek Kiel, 2017. http://d-nb.info/1138979929/34.
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Da, Silva Ophélie. "Structure de l'écosystème planctonique : apport des données à haut débit de séquençage et d'imagerie." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS183.
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Les organismes planctoniques, acteurs clés des écosystèmes, soutiennent les réseaux trophiques et ont un rôle majeur dans les cycles biogéochimiques et la régulation du climat. Tandis que la répartition spatio-temporelle de la diversité planctonique peut être étudiée à plusieurs niveaux, du gène jusqu’à l’écosystème, comprendre les mécanismes qui sous-tendent cette organisation est un défi. En effet, la structure de la diversité résulte de différents processus évolutifs et écologiques qui peuvent agir simultanément sur le vivant. Depuis le début du XXIème siècle, le milieu océanique fait l'objet d’une surveillance croissante. De nombreuses plateformes d’observation ont été déployées permettant l’acquisition de très nombreuses données couvrant de multiples caractéristiques environnementales. En parallèle, les technologies d’étude du vivant se sont développées, conduisant à un échantillonnage sans précédent des organismes planctoniques. En particulier, les données à haut débit de séquençage et d’imagerie permettent de fournir des informations moléculaires, taxonomiques et fonctionnelles à l’échelle des communautés. L’objectif de cette thèse était d’explorer la structure des écosystèmes planctoniques à l’aide des données à haut débit de séquençage et d’imagerie. Le couplage avec les données environnementales pourrait contribuer à une meilleure compréhension de la répartition spatiale de la diversité planctonique, des espèces jusqu’au communautés. Dans une première partie, la diversité génétique de protistes a été étudiée à l’échelle de l’espèce. L’hypothèse était que les données métagénomiques pourraient permettre d’accéder à l’organisation de cette diversité mal caractérisée pour les protistes, ainsi qu’aux mécanismes qui la sous-tendent. Dans une deuxième partie, le lien entre diversité génétique et diversité fonctionnelle a été exploré. La transparence a été ciblée. Ce trait fonctionnel est peu exploré à l’échelle des communautés et les bases moléculaires sont mal identifiées. Une approche permettant de faire émerger ce trait des données d’imagerie a été utilisée, ayant conduit à l’exploration de sa biogéographie et ses bases moléculaires. Dans la dernière partie, le haut potentiel de complémentarité entre jeux de données de séquençage, d’imagerie et environnementaux a été exploré, afin de mettre en lumière la structure multi-échelle de l’écosystème planctonique et d’identifier sa structure globale. Enfin, l’ensemble des résultats a été discuté pour mettre en évidence les apports que peuvent fournir ces données à la compréhension des écosystèmes planctoniques, ainsi que les limites auxquelles elles peuvent faire facePlanktonic organisms are key actors in oceanic ecosystems, which support trophic networks and play a major role in biogeochemical cycles and climate regulation. While the spatio-temporal distribution of planktonic diversity can be investigated at several levels, from the gene to the ecosystem, identifying the underlying mechanisms is challenging. Indeed, the structure of diversity results from different evolutionary and ecological processes that can act simultaneously. Since the beginning of the 21st century, the oceanic environment has been increasingly monitored. Numerous observation platforms have been deployed, leading to the acquisition of a large amount of data for multiple environmental characteristics. At the same time, technologies for studying living organisms have been developed. Thus, an unprecedented sampling of planktonic organisms has taken place. In particular, high-throughput sequencing and imaging data provide molecular, taxonomic and functional information at several biological levels. The objective of this thesis was to explore the structure of planktonic ecosystems using high-throughput sequencing and imaging data. Coupling with environmental data could contribute to a better understanding of the spatial distribution of planktonic diversity, from species to communities. In the first part, the genetic diversity of protists was studied at the species level. The hypothesis was that metagenomics could provide access to the poorly characterized spatial organization of the intraspecific protist genetic diversity, as well as to the mechanisms underlying it. In a second part, the link between genetic diversity and functional diversity was explored. Transparency was targeted. This functional trait is little explored at the community level and its molecular basis is poorly identified. A data-driven approach allowed this trait to emerge from imaging data, leading to the exploration of its biogeography and molecular basis. In the last part, the high potential of complementarity between sequencing, imaging and environmental datasets was explored, in order to highlight the multi-scale structure of the planktonic ecosystem and to identify its global structure. Finally, all the results were discussed to highlight the contributions that these data can provide to the understanding of planktonic ecosystems, as well as the limitations they can face
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Benoiston, Anne-Sophie. "Méta-omique et méta-données environnementales : vers une nouvelle compréhension de la pompe à carbone biologique." Electronic Thesis or Diss., Sorbonne université, 2019. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2019SORUS182.pdf.
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La pompe à carbone biologique comprend la production primaire de matière organique dans la zone euphotique, son export vers les profondeurs et sa reminéralisation. Les acteurs les plus fréquemment cités sont les diatomées en raison de leur contribution à la production primaire et à l’export de carbone et les copépodes pour la production de pelotes fécales. Cependant, la pompe biologique est le résultat d'interactions complexes entre organismes plutôt que de leurs actions indépendantes. En outre, bien qu'il ait été montré que la distribution de taille et la composition minérale du phytoplancton en surface ont une influence significative sur l'intensité de l'export de carbone, on ne sait pas si les données méta-omiques peuvent prédire efficacement les processus de la pompe à carbone biologique. Dans cette thèse, je propose d’abord de revisiter l’étude de la pompe à carbone biologique dans l’océan oligotrophe en définissant des états biogéochimiques de l’océan sur la base de la contribution relative de la production primaire, de l’export de carbone et de l’atténuation du flux dans les stations d’échantillonnage Tara Océans. L'analyse des états en termes de composition et d'interactions microbiennes inférées à partir de données de métabarcoding a révélé que les associations plutôt que la composition microbienne semblent caractériser les états de la pompe à carbone biologique. Ensuite, en utilisant les données méta-omiques et environnementales des expéditions Tara Oceans, je propose pour la première fois de prédire ces états biogéochimiques à partir d'abondances biologiques dérivées d'ADN environnemental, dans l'objectif de fournir une liste de biomarqueursThe biological carbon pump encompasses a series of processes including the primary production of organic matter in the surface ocean, its export to deeper waters and its remineralization. The common highlighted actors are diatoms because of their contribution to primary production and carbon export and copepods for their production of fecal pellets. However, the biological pump is the result of complex interactions among organisms rather than their independent actions. Besides, although size distribution and mineral composition of phytoplankton in surface was shown to significantly influence the strength of carbon export, it is unknown whether meta-omic data can efficiently predict the processes of the biological carbon pump. In this thesis, I first propose to revisit the study of the biological carbon pump in the oligotrophic ocean by defining biogeochemical states of the ocean based on the relative contribution of primary production, carbon export and flux attenuation in Tara Oceans sampling stations. The analysis of the states in terms of microbial composition and interactions inferred from metabarcoding data revealed that variation in associations rather than lineages presence seems to drive the states of the biological carbon pump. Then, by using meta-omics and environmental parameters from the Tara Oceans expeditions, I propose the first study trying to predict biogeochemical states from biological abundances derived from environmental DNA, with the goal of providing a list of biomarkers
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Costa, João Carlos Sequeira. "Development of an automated pipeline for meta-omics data analysis." Master's thesis, 2017. http://hdl.handle.net/1822/56113.
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Dissertação de mestrado em Computer ScienceKnowing what lies around us has been a goal for many decades now, and the new advances in sequencing technologies and in meta-omics approaches have permitted to start answering some of the main questions of microbiology - what is there, and what is it doing? The exponential growth of omics studies has been answered by the development of some bioinformatic tools capable of handling Metagenomics (MG) analysis, with a scarce few integrating such analysis with Metatranscriptomics (MT) or Metaproteomics (MP) studies. Furthermore, the existing tools for meta-omics analysis are usually not user friendly, usually limited to command-line usage. Because of the variety in meta-omics approaches, a standard workflow is not possible, but some routines exist, which may be implemented in a single tool, thereby facilitating the work of laboratory professionals. In the framework of this master thesis, a pipeline for integrative MG and MT data analysis was developed. This pipeline aims to retrieve comprehensive comparative gene/transcript expression results obtained from different biological samples. The user can access the data at the end of each step and summaries containing several parameters of evaluation of the previous step, and final graphical representations, like Krona plots and Differential Expression (DE) heatmaps. Several quality reports are also generated. The pipeline was constructed with tools tested and validated for meta-omics data analysis. Selected tools include FastQC, Trimmomatic and SortMeRNA for preprocessing, MetaSPAdes and Megahit for assembly, MetaQUAST and Bowtie2 for reporting on the quality of the assembly, FragGeneScan and DIAMOND for annotation and DeSEQ2 for DE analysis. Firstly, the tools were tested separately and then integrated in several python wrappers to construct the software Meta-Omics Software for Community Analysis (MOSCA). MOSCA performs preprocessing of MG and MT reads, assembly of the reads, annotation of the assembled contigs, and a final data analysis. Real datasets were used to test the capabilities of the tool. Since different types of files can be obtained along the workflow, it is possible to perform further analyses to obtain additional information and/or additional data representations, such as metabolic pathway mapping.
O objectivo da microbiologia, e em particular daqueles que se dedicam ao estudo de comunidades microbianas, é descobrir o que compõe as comunidades, e a função de cada microrganismo no seio da comunidade. Graças aos avanços nas técnicas de sequenciação, em particular no desenvolvimento de tecnologias de Next Generation Sequencing, surgiram abordagens de meta-ómicas que têm vindo a ajudar a responder a estas questões. Várias ferramentas foram desenvolvidas para lidar com estas questões, nomeadamente lidando com dados de Metagenómica (MG), e algumas poucas integrando esse tipo de análise com estudos de Metatranscriptómica (MT) e Metaproteómica (MP). Além da escassez de ferramentas bioinformáticas, as que já existem não costumam ser facilmente manipuláveis por utilizadores com pouca experiencia em informática, e estão frequentemente limitadas a uso por linha de comando. Um formato geral para uma ferramenta de análise meta-ómica não é possível devido à grande variedade de aplicações. No entanto, certas aplicações possuem certas rotinas, que são passíveis de serem implementadas numa ferramenta, facilitando assim o trabalho dos profissionais de laboratório. Nesta tese, uma pipeline integrada para análise de dados de MG e MT foi desenvolvida, pretendendo determinar a expressão de genes/transcriptos entre diferentes amostras biológicas. O utilizador tem disponíveis os resultados de cada passo, sumários com vários parâmetros para avaliação do procedimento, e representações gráficas como gráficos Krona e heatmaps de expressão diferencial. Vários relatórios sobre a qualidade dos resultados obtidos também são gerados. A ferramenta foi construída baseada em ferramentas e procedimentos testados e validados com análise de dados de meta-ómica. Essas ferramentas são FastQC, Trimmomatic e SortMeRNA para pré-processamento, Megahit e MetaSPAdes para assemblagem, MetaQUAST e Bowtie2 para controlo da qualidade dos contigs obtidos na assemblagem, FragGeneScan e DIAMOND para anotação e DeSEQ2 para análise de expressão diferencial. As ferramentas foram testadas uma a uma, e depois integradas em diferentes wrappers de python para compôr a Meta-Omics Software for Community Analysis (MOSCA). A MOSCA executa pré-processamento de reads de MG e MT, assemblagem das reads, anotação dos contigs assemblados, e uma análise de dados final Foram usados dados reais para testar as capacidades da MOSCA. Como podem ser obtidos diferentes tipos de ficheiros ao longo da execução da MOSCA, é possível levar a cabo análises posteriores para obter informação adicional e/ou representações de dados adicionais, como mapeamento de vias metabólicas.
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Annavajhala, Medini. "Meta-omics-derived structure, function, and activity of mixed microbial communities driving biological nutrient removal and recovery." Thesis, 2017. https://doi.org/10.7916/D864023F.
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Improved process design and operation of systems engineered for the biological removal and recovery of carbon, nitrogen, and phosphorus from waste streams requires an understanding of the mixed microbial communities employed. While traditional microbiology techniques have been used to characterize the metabolic capability and activity of some organisms responsible for nutrient cycling, the metabolism of novel organisms and dynamics of complex microbial communities have been insufficiently revealed. The development and increased commercial availability of next-generation sequencing technology over the last 5-7 years has led to immense data-gathering capabilities from biological systems at the DNA ((meta)genomics), RNA ((meta)transcriptomics), and protein ((meta)proteomics) levels. However, the application of next-generation sequencing and bioinformatics to engineered biological processes remains rare, and major gaps still exist in the reference databases and metabolic understanding of single organisms (genomics) and mixed communities (metagenomics) driving biological nutrient removal and recovery in wastewater and food waste. This dissertation therefore had several major objectives: (1) Improving understanding of microbial conversion of food waste to volatile fatty acids; (2) Surveying pilot- and full-scale global biological nitrogen removal communities; (3) Application of mainstream deammonification; and (4) Adding to the sparse genomic reference database related to enhanced biological phosphorus removal (EBPR). The model of acidogenesis and acetogenesis from food waste was significantly expanded, and used to link shifts in microbial community structure and functional potential, caused by varying reactor operating conditions, to the production and speciation of volatile fatty acids for a variety of endpoint uses. Unexpected trends in the microbial ecology and functional potential of global full-scale systems were also uncovered, indicating opportunity for further enhancement of nitrogen removal through microbial community selection as a response to increasingly stringent nitrogen discharge permit levels. At the lab-scale, energy- and cost-saving anaerobic ammonia oxidation (anammox) was successfully applied as an alternative to conventional biological nitrogen removal under suboptimal mainstream wastewater conditions without constant bioaugmentation. Lastly, the annotation of PAO and GAO metagenomes from highly enriched cultures for which long-term morphological, physiological, and performance data were available allowed for increased confidence in the resulting genetic insights into the anaerobic metabolism and denitrification capabilities of these organisms. A systems biology approach to the analysis of engineered bioprocesses provided insights on microbial community structure and functional capabilities which were previously unavailable and unattainable. Ultimately, the work reported here will lead to better diagnoses of underlying issues in problematic bioreactors and smarter design of new wastewater and food waste treatment options.
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Park, Mee Rye. "Elucidating Microbial Community Structure, Function and Activity in Engineered Biological Nitrogen Removal Processes using Meta-omics Approaches." Thesis, 2017. https://doi.org/10.7916/D8BR94RN.
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Biological nitrogen removal (BNR) has been applied for more than a century in the interests of preserving and enhancing public health and the environment. But only during the last few decades has the development of molecular techniques using biomolecules such as nucleic acids (DNA and RNA) and proteins allowed the accurate description and characterization of the phylogenetic and functional diversity of microbial communities. Moreover, thanks to recent advances in genomics and next-generation sequencing technologies, microbial community analyses have initiated a new era of microbial ecology. Notwithstanding the fact that the efficiency and robustness of a wastewater treatment mainly depend on the composition and activity of BNR communities, research on the structural and functional microbial ecology of the engineered BNR process remains rare with respect to next-generation sequencing and bioinformatics.
This dissertation aims to bridge high-priority knowledge gaps in determining and applying knowledge of microbial structure (who is there and how many?) and function (what are they doing? what else can they do?) to the practice of BNR processes, and to opening up the ‘black-box’ of energy and resource efficient engineered BNR processes using a systems biology approach. Specific objectives were to (1) selectively enrich Nitrospira spp. from a mixed environmental microbial consortium (such as activated sludge) in a continuously operated bioreactor and characterize the microbial ecology during the course of enrichment, determine key kinetic parameters of enriched Nitrospira spp., (2) examine the inhibitory effects of nitrogenous intermediates (such as hydroxylamine, presented herein) on the physiological and molecular responses of Nitrospira spp. in terms of both catabolism and anabolism, (3) characterize bacterial community composition and their dynamics by 16S rRNA gene amplicon sequencing under varying reactor operational conditions from full-scale WWTPs and identify process parameters that most significantly correlate with those dynamics, (4) interpret metagenomic (DNA-based) and metatranscriptomic (RNA-based) derived structure, metabolic function and activity of the full-scale BNR microbial communities, and (5) describe gene expression in the same full-scale BNR communities in response to alternating anoxic-aerobic conditions using a metatranscriptomic approach.
First, planktonic Nitrospira spp. were successfully enriched from activated sludge in a sequencing batch reactor by maintaining sustained limiting extant nitrite and dissolved oxygen concentrations for a half year. The determined parameters collectively reflected not just higher affinities of this enrichment for nitrite and oxygen, respectively, but also a higher biomass yield and energy transfer efficiency relative to other NOB such as Nitrobacter spp. Used in combination, these kinetic and thermodynamic parameters can help toward the development and application of energy-efficient biological nutrient removal processes through effective Nitrospira out-selection.
Second, using quantitative activity measurements (respirometrc rates) with functional gene expression profiles, this study demonstrated that N-intermediates such as hydroxylamine (NH¬2OH) can strongly inhibit the activity and expression of key anabolic (energy synthesis) and catabolic (biomass synthesis) pathways of Nitrospira spp. A strategy that relies upon the transient accumulation and consumption of such intermediates (such as transient aeration) could provide the platform for successful suppression of Nitrospira spp. in the next generation of energy efficient engineered BNR processes.
Third, 16S rRNA gene amplicon sequencing revealed that microbial community structure and their dynamics significantly varied depending on seven differing wastewater treatment processes. The findings showed that five process parameters of wastewater influenced the dynamics of BNR communities; water temperature was correlated most strongly to the variance of bacterial communities, followed by effluent NH3, effluent NO3-, removed N, and effluent NO2-. The results provided insights into the underlying ecological pattern of community compositions and dynamics in full-scale WWTPs; and correlation with process parameters brought about distinct communities that enable different microbial activities. However, one of the greatest challenges was to elucidate the relationship between microbial structure and their “active” functions, which are related to reactor performance (This challenge continued into fourth study chapter summarized below).
Fourth, continuing from the previous study, combined metagenomics and metatranscriptomics revealed far superior richness of information of not just microbial structure, but also potential (through metagenomics) and expressed function (through metatranscriptimics) within the complex activated sludge processes. Via independent analysis of whole-DNA and whole-RNA, the entire microbial community and its in situ active members, involved in nitrificaiton and denitrification, were compared. Active nitrifiers and denitrifiers obtained by RNA analysis exhibited relatively high abundances in DNA-derived communities. Further gene expression annotation on nitrogen removal revealed that the expressions of denitrification-related genes except nos were increased under anoxic conditions relative to aerobic conditions, while the expressions of nitrifying genes were decreased. Our findings led to an improved understanding of metabolic activities and roles of BNR microbial communities, and offer the first metatranscriptional insights on engineered nutrient removal in anoxic conditions relative to aerobic conditions in full-scale wastewater systems.
In sum, next-generation sequencing as well as traditional molecular techniques shed light on microbial diversity and different functional genes in varying engineered BNR systems. Furthermore, this dissertation provides a wealth of knowledge on systematic explorations of the linkage between structure and function of BNR communities, and offers engineering applications to BNR processes including energy and resource efficient engineered systems. It is expected that the implementation and further expansion of this work will improve the design and operation of engineered BNR processes, eventually producing benefits for the global population and the environment.
Books on the topic "Meta-omics"
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Park, Mee Rye. Elucidating Microbial Community Structure, Function and Activity in Engineered Biological Nitrogen Removal Processes using Meta-omics Approaches. [New York, N.Y.?]: [publisher not identified], 2017.
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Unlocking the Potential of Carbonaceous Resource Recovery from the Arrested Anaerobic Digestion of Food Waste: Engineering Design and Meta-omics Analysis. [New York, N.Y.?]: [publisher not identified], 2022.
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Kirchman, David L. Genomes and meta-omics for microbes. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198789406.003.0005.
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The sequencing of entire genomes of microbes grown in pure cultures is now routine. The sequence data from cultivated microbes have provided insights into these microbes and their uncultivated relatives. Sequencing studies have found that bacterial genomes range from 0.18 Mb (intracellular symbiont) to 13 Mb (a soil bacterium), whereas genomes of eukaryotes are much bigger. Genomes from eukaryotes and prokaryotes are organized quite differently. While bacteria and their small genomes often grow faster than eukaryotes, there is no correlation between genome size and growth rates among the bacteria examined so far. Genomic studies have also highlighted the importance of genes exchanged (“horizontal gene transfer”) between organisms, seemingly unrelated, as defined by rRNA gene sequences. Microbial ecologists use metagenomics to sequence all microbes in a community. This approach has revealed unsuspected physiological processes in microbes, such as the occurrence of a light-driven proton pump, rhodopsin, in bacteria (dubbed proteorhodopsin). Genomes from single cells isolated by flow cytometry have also provided insights about the ecophysiology of both bacteria and protists. Oligotrophic bacteria have streamlined genomes, which are usually small but with a high fraction of genomic material devoted to protein-encoding genes, and few transcriptional control mechanisms. The study of all transcripts from a natural community, metatranscriptomics, has been informative about the response of eukaryotes as well as bacteria to changing environmental conditions.
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Tseng, George, Debashis Ghosh, and Xianghong Jasmine Zhou. Integrating Omics Data. Cambridge University Press, 2015.
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Integrating Omics Data. Cambridge University Press, 2015.
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Tseng, George C., Debashis Ghosh, and Xianghong Jasmine Zhou. Integrating Omics Data. Cambridge University Press, 2015.
Find full textBook chapters on the topic "Meta-omics"
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Srivastava, Nidhi, and Indira P. Sarethy. "Rhizosphere Fingerprints: Novel Biomolecules Via Meta-Omics Technology." In Omics Science for Rhizosphere Biology, 171–88. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0889-6_10.
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Alisoltani, Arghavan, Akebe Luther King Abia, and Linda Bester. "Shared Microbiome in Different Ecosystems: A Meta-Omics Perspective." In Microbial Genomics in Sustainable Agroecosystems, 1–20. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9860-6_1.
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Ponnusamy, Mohanraj, Chinnan Velmurugan Karthikeyan, and Babu Ramanathan. "Meta-omics in Detection of Silkworm Gut Microbiome Diversity." In Microbial Genomics in Sustainable Agroecosystems, 359–70. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9860-6_17.
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Wani, Atif Khurshid, Daljeet Singh Dhanjal, Nahid Akhtar, Chirag Chopra, Abhineet Goyal, and Reena Singh. "Role of Genomics, Metagenomics, and Other Meta-Omics Approaches for Expunging the Environmental Contaminants by Bioremediation." In Omics for Environmental Engineering and Microbiology Systems, 19–51. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003247883-2.
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Hassan, Muhammad Jawad, Muhammad Faheem, and Sabba Mehmood. "Emerging OMICS and Genetic Disease." In Omics Technologies for Clinical Diagnosis and Gene Therapy: Medical Applications in Human Genetics, 93–113. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/9789815079517122010010.
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Multiomics also described as integrative omics is an analytical approach that combines data from multiple ‘omics’ approaches including genomics, transcriptomics, proteomics, metabolomics, epigenomics, metagenomics and Meta transcriptomics to answer the complex biological processes involved in rare genetic disorders. This omics approach is particularly helpful since it identifies biomarkers of disease progression and treatment progress by collective characterization and quantification of pools of biological molecules within and among the various types of cells to better understand and categorize the Mendelian and non- Mendelian forms of rare diseases. As compared to studies of a single omics type, multi-omics offers the opportunity to understand the flow of information that underlies the disease. A range of omics software and databases, for example WikiPathways, MixOmics, MONGKIE, GalaxyP, GalaxyM, CrossPlatform Commander, and iCluster are used for multi-omics data exploration and integration in rare disease analysis. Recent advances in the field of genetics and translational research have opened new treatment avenues for patients. The innovation in the next generation sequencing and RNA sequencing has improved the ability from diagnostics to detection of molecular alterations like gene mutations in specific disease types. In this chapter, we provide an overview of such omics technologies and focus on methods for their integration across multiple omics layers. The scrupulous understanding of rare genetic disorders and their treatment at the molecular level led to the concept of a personalized approach, which is one of the most significant advancements in modern research which enable researchers to better comprehend the flow of knowledge which underpins genetic diseases.
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Tripathi, Lokesh Kumar, and Tapan Kumar Nailwal. "Metagenomics: Applications of functional and structural approaches and meta-omics." In Recent Advancements in Microbial Diversity, 471–505. Elsevier, 2020. http://dx.doi.org/10.1016/b978-0-12-821265-3.00020-7.
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Tilgam, Jyotsana, Deepanshu Jayaswal, Mushineni Ashajyothi, Jyoti Prakash Singh, Adarsh Kumar, and Hillol Chakdar. "Meta-omics approaches for understanding and exploring soil microbial communities for sustainable agriculture." In Applications of Metagenomics, 3–22. Elsevier, 2024. http://dx.doi.org/10.1016/b978-0-323-98394-5.00013-4.
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Margret, Arockiya Anita, S. Aishwarya, A. Arun, and R. Jasmine. "Interface of ‘meta-omics’ in gut biome remediation to unravel the complications of environmental pollutants." In Metagenomics to Bioremediation, 183–206. Elsevier, 2023. http://dx.doi.org/10.1016/b978-0-323-96113-4.00024-x.
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Samanta, Brajogopal, and Pattigundla Swathi. "Macroalgal Epiphytic Microbiome: A Potential Source of Novel Drugs." In Marine Ecology: Current and Future Developments, 184–205. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815051995123030008.
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In the marine rocky intertidal ecosystem, macroalgae (seaweeds) serve ecosystem engineers that create, modify, or maintain the physical habitat for their own and other species. Intriguingly, most marine macroalgal species evolved with microbial colonization and biofilm formation on their surface. The macroalgae (basibiont) and associated epiphytic microbiota (epibiont) act as a functional unit known as a “macroalgal holobiont,” characterized by its complex chemical interactions. In this non-trophic association, the epiphytic microbial biofilm forms a protective layer essential in host defense against foulers, consumers, or pathogens. In addition, antimicrobial activity is widespread among these epiphytic microbes. However, due to their thinness and often negligible biomass, the chemo-ecological impact of this epiphytic microbiome is severely underestimated. This chapter aims to review the antimicrobial potential of the “macroalgal epiphytic microbiome” and introduce the application of “meta-omics” approaches for further exhaustive exploitations of this unique microbiome for future drug discovery.
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Islam, Ekramul. "Meta-Omics Studies of Microbial Communities in Hollow Fiber Membrane Biofilm Reactors Treating Contaminants in Water Resources: Recent Advances." In Wastewater Treatment, 457–70. Elsevier, 2021. http://dx.doi.org/10.1016/b978-0-12-821881-5.00022-2.
Full textConference papers on the topic "Meta-omics"
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Bernier-Latmani, Rizlan, Karen Viacava, Jiangtao Qiao, Karin Lederballe Meibom, Andrew Janowczyk, Suresh Poudel, Robert Hettich, Him Shrestha, Nicolas Jacquemin, and Matthew Reid. "Identification of active arsenic-methylating organisms in anaerobic soil enrichment cultures using meta-omics." In Goldschmidt2021. France: European Association of Geochemistry, 2021. http://dx.doi.org/10.7185/gold2021.7580.
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Gui, H., D. Hu, P. Sleiman, S. Xiao, M. Yang, S. Hochstadt, D. Dynkowski, et al. "Whole-Genome Sequencing Based Meta-Analysis of Asthma Exacerbations from the Asthma Translational Genomic Collaborative (ATGC) of the Trans-Omics for Precision Medicine (TOPMed) Program." In American Thoracic Society 2021 International Conference, May 14-19, 2021 - San Diego, CA. American Thoracic Society, 2021. http://dx.doi.org/10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a1382.
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