Relevant bibliographies by topics / Meta-transcriptomics

Academic literature on the topic 'Meta-transcriptomics'

Author: Grafiati
Published: 10 December 2022
Last updated: 20 February 2023

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Journal articles on the topic "Meta-transcriptomics"

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Caldas, José, and Susana Vinga. "Global Meta-Analysis of Transcriptomics Studies." PLoS ONE 9, no. 2 (February 26, 2014): e89318. http://dx.doi.org/10.1371/journal.pone.0089318.

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Cobbin, Joanna CA, Justine Charon, Erin Harvey, Edward C. Holmes, and Jackie E. Mahar. "Current challenges to virus discovery by meta-transcriptomics." Current Opinion in Virology 51 (December 2021): 48–55. http://dx.doi.org/10.1016/j.coviro.2021.09.007.

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Shi, Mang, Yong-Zhen Zhang, and Edward C. Holmes. "Meta-transcriptomics and the evolutionary biology of RNA viruses." Virus Research 243 (January 2018): 83–90. http://dx.doi.org/10.1016/j.virusres.2017.10.016.

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Fung, Wing Tung, Joseph T. Wu, Wai Man Mandy Chan, Henry H. Chan, and Herbert Pang. "Pathway‐based meta‐analysis for partially paired transcriptomics analysis." Research Synthesis Methods 11, no. 1 (November 10, 2019): 123–33. http://dx.doi.org/10.1002/jrsm.1381.

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Wittekindt, Nicola E., Abinash Padhi, Stephan C. Schuster, Ji Qi, Fangqing Zhao, Lynn P. Tomsho, Lindsay R. Kasson, Michael Packard, Paul Cross, and Mary Poss. "Nodeomics: Pathogen Detection in Vertebrate Lymph Nodes Using Meta-Transcriptomics." PLoS ONE 5, no. 10 (October 18, 2010): e13432. http://dx.doi.org/10.1371/journal.pone.0013432.

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Gust, Kurt A., Fares Z. Najar, Tanwir Habib, Guilherme R. Lotufo, Alan M. Piggot, Bruce W. Fouke, Jennifer G. Laird, et al. "Coral-zooxanthellae meta-transcriptomics reveals integrated response to pollutant stress." BMC Genomics 15, no. 1 (2014): 591. http://dx.doi.org/10.1186/1471-2164-15-591.

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Brown, Laurence A., and Stuart N. Peirson. "Improving Reproducibility and Candidate Selection in Transcriptomics Using Meta-analysis." Journal of Experimental Neuroscience 12 (January 2018): 117906951875629. http://dx.doi.org/10.1177/1179069518756296.

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Chialva, Matteo, Stefano Ghignone, Mara Novero, Wael N. Hozzein, Luisa Lanfranco, and Paola Bonfante. "Tomato RNA-seq Data Mining Reveals the Taxonomic and Functional Diversity of Root-Associated Microbiota." Microorganisms 8, no. 1 (December 24, 2019): 38. http://dx.doi.org/10.3390/microorganisms8010038.

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Next-generation approaches have enabled researchers to deeply study the plant microbiota and to reveal how microbiota associated with plant roots has key effects on plant nutrition, disease resistance, and plant development. Although early “omics” experiments focused mainly on the species composition of microbial communities, new “meta-omics” approaches such as meta-transcriptomics provide hints about the functions of the microbes when interacting with their plant host. Here, we used an RNA-seq dataset previously generated for tomato (Solanum lycopersicum) plants growing on different native soils to test the hypothesis that host-targeted transcriptomics can detect the taxonomic and functional diversity of root microbiota. Even though the sequencing throughput for the microbial populations was limited, we were able to reconstruct the microbial communities and obtain an overview of their functional diversity. Comparisons of the host transcriptome and the meta-transcriptome suggested that the composition and the metabolic activities of the microbiota shape plant responses at the molecular level. Despite the limitations, mining available next-generation sequencing datasets can provide unexpected results and potential benefits for microbiota research.
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Delhomme, Nicolas, Görel Sundström, Neda Zamani, Henrik Lantz, Yao-Cheng Lin, Torgeir R. Hvidsten, Marc P. Höppner, et al. "Serendipitous Meta-Transcriptomics: The Fungal Community of Norway Spruce (Picea abies)." PLOS ONE 10, no. 9 (September 28, 2015): e0139080. http://dx.doi.org/10.1371/journal.pone.0139080.

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Porter, Ashleigh F., Mang Shi, John-Sebastian Eden, Yong-Zhen Zhang, and Edward C. Holmes. "Diversity and Evolution of Novel Invertebrate DNA Viruses Revealed by Meta-Transcriptomics." Viruses 11, no. 12 (November 25, 2019): 1092. http://dx.doi.org/10.3390/v11121092.

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DNA viruses comprise a wide array of genome structures and infect diverse host species. To date, most studies of DNA viruses have focused on those with the strongest disease associations. Accordingly, there has been a marked lack of sampling of DNA viruses from invertebrates. Bulk RNA sequencing has resulted in the discovery of a myriad of novel RNA viruses, and herein we used this methodology to identify actively transcribing DNA viruses in meta-transcriptomic libraries of diverse invertebrate species. Our analysis revealed high levels of phylogenetic diversity in DNA viruses, including 13 species from the Parvoviridae, Circoviridae, and Genomoviridae families of single-stranded DNA virus families, and six double-stranded DNA virus species from the Nudiviridae, Polyomaviridae, and Herpesviridae, for which few invertebrate viruses have been identified to date. By incorporating the sequence of a “blank” experimental control we also highlight the importance of reagent contamination in metagenomic studies. In sum, this work expands our knowledge of the diversity and evolution of DNA viruses and illustrates the utility of meta-transcriptomic data in identifying organisms with DNA genomes.
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Dissertations / Theses on the topic "Meta-transcriptomics"

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Harvey, Erin Elizabeth Hunter. "Using Meta-Transcriptomics to Reveal the Diversity, Ecology and Evolution of Animal Viruses." Thesis, The University of Sydney, 2020. https://hdl.handle.net/2123/21806.

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Although viruses are ubiquitous, infecting all biological entities including viruses themselves, we know relatively little of viral diversity beyond those implicated in diseases affecting humans and domestic plants and animals. Recent advances in metagenomic sequencing technologies, particularly bulk RNA shotgun sequencing ('meta-transcriptomics'), have enabled a dramatic increase in our understanding of virus diversity and evolution. However, although likely central to disease emergence, the total viral diversity (i.e. virome) of many vertebrates and their eukaryotic parasites remain uncharacterised. In this thesis I use meta-transcriptomic based virus discovery to characterise both the viromes of species threatened by anthropocentric activities and their parasitic invertebrates. In total, I describe the discovery of 102 novel viruses in samples collected from four vertebrate species - eastern Australian Humpback whales, koalas and three species of Antarctic penguin. These viruses were often highly diverse from those previously characterised, providing a broadened perspective on virome diversity in these animals. In addition, due to their role as vectors of disease in humans and wildlife, ticks and fleas were collected during population studies of two species of eastern Australian Bandicoot as well as from Antarctic penguin nesting sites. Meta-transcriptomic analyses of these parasites identified 46 novel viruses, often highly divergent from those viruses previously characterised. Australia and Antarctica have unique fauna that have evolved in isolation for millions of years. It may therefore be assumed that the viruses infecting these species would be equally as divergent. Both Australian and Antarctic tick viruses clustered phylogenetically with other tick-associated viruses, suggesting that they co-evolved with their tick hosts as they have diversified.
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Giotti, Bruno. "Derivation of the human cell cycle transcriptional signature." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28840.

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Duplication of the genome and successful mitotic cell division requires the coordinated activity of hundreds of proteins. Many are known, but a complete list of the components of the cell cycle machinery is still lacking. This thesis describes a series of data driven analyses to assemble a comprehensive list of genes induced during the human cell cycle. To start with, a meta-analysis of previous transcriptomics studies revealed a larger number of cell cycle genes consistently expressed across multiple human cell types than previously reported. Following this observation, the cell cycle transcriptome was further investigated with the generation of a new time-course microarray dataset on normal human dermal fibroblasts (NHDF) undergoing synchronised cell division. Network cluster analysis of these data identified transcripts whose expression was associated with different stages of cell cycle progression. Co-expression of these transcripts was then analysed using a complementary dataset that included genome-wide promoter expression of a wide range of human primary cells. This resulted in the identification of a core set of 545 cell cycle genes, mainly associated with G1/S to M phases, which showed a high degree of co-expression across all cell types. Expression of 75% of these genes was also found conserved in mouse, as revealed by the analysis of a new microarray experiment generated from mouse fibroblasts. Gene Ontology and motif enrichment analysis validated the list with significant enrichments for terms and transcription factor biding sites linked with cell cycle biology. Toward a better interpretation of these 545 genes, a meticulous manual annotation exercise was carried out. Unsurprisingly, the majority of these genes were known to be involved in S and M phases-associated processes, however 50 genes were functionally uncharacterised. A subset of 36 of these were then taken forward for subcellular localisation assays. These studies were performed by transfection of human embryonic kidney cells (HEK293T) with GFP-tagged cDNA clones leading to the finding of four uncharacterised proteins co-localising with the centrosome, a crucial organelle for normal cell cycle progression. This thesis represents an attempt in documenting the genes actively transcribed and therefore likely involved in the processes associated with cell cycle, hence providing a comprehensive catalogue of its key components. In so doing, I have also identified a significant number of new genes likely to contribute to this central process vital in health and disease.
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Chang, Wei-Shan. "Metagenomic Applications in Virus Discovery, Ecology, and the Surveillance of Australian Wildlife." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/25948.

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Metagenomic next-generation sequencing (mNGS), particularly total RNA sequencing (“meta-transcriptomics”), has led to a revolution in virus discovery, veterinary diagnostics and virus evolution. Wildlife naturally harbour a diverse assemblage of infectious microorganisms and these can be a source of novel, and often poorly studied, diseases of humans and other animals. Many mortality and morbidity events in wildlife are long-standing, neglected and unsolved. These include wobbly possum disease, black and white bird disease, clenched claw syndrome and bearded dragon respiratory disease. To provide a new understanding of these diseases and identify pathogens in diseased wildlife with unknown aetiology across different taxa, I developed and applied a meta-transcriptomic-based pipeline that was used in combination with retrospective clinical metadata, histopathology, phylogeny, and molecular assays. Accordingly, novel viruses were identified from a wide range of virus families, including the Circoviridae, Chaphamaparvoviridae, Flaviviridae, Astroviridae, Picornaviridae, Paramixoviridae, Adenoviridae, and Polyomaviridae, greatly extending our knowledge of virus diversity in wildlife, including marsupials, birds, and reptiles from both the wild and captive environments. Similarly, through exploiting meta-transcriptomic approaches and mining the Sequence Read Archive, I discovered four novel hepatitis delta-like viruses from fish, amphibians and termites, thereby rejecting the concept that hepatitis delta viruses are only associated with humans. In sum, my work highlights a successful combination of metagenomics with traditional tools to transform veterinary clinical diagnostics and disease surveillance. In doing so, it sheds light on the enormous diversity of viruses, elucidating their origins and evolutionary history, and allowing the discovery of pathogens from wildlife biodiversity diseases within a One Health perspective.
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Tsai, Chen-Hui, and 蔡辰輝. "Discovery of Metastasis Regulatory Genes Based on the ParallelEvolution with Meta-Analysis of Transcriptomics Approach." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/mh25am.

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碩士
臺北醫學大學
醫學資訊研究所
102
Cancer death proceeded to the leading cause of death and metastasis accounts for 90% of cancer deaths. Finding the set of metastatic biomarkers is prerequisite for increasing effective screening and prognostic prediction. Based on the theory that tumorigenesis and metastasis are evolved in parallel, we obtained 428 Metastasis Promoting Genes (MPG) and 548 Metastasis Suppressor Genes (MSG) by meta-data analysis from metastatic tumors, benign tumors, ectopic ovarian endometrium, eutopic endometrium microarray data. Furthermore,using the microarray data of the normal tissues, we obtained 176 MPG highly expression and 248 MSG low expression in leukocyte-related tissues. To validate those candidate genes, we will carry out the functional assay including in vitro migration assay and in vitro invasion assay. In the future, those Metastatic Related Genes that could effectively differentiate the potential tendency for tumor to metastasize are potential drug targets to facilitate the therapeutic lead compound development.
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Kaever, Alexander. "Development of a statistical framework for mass spectrometry data analysis in untargeted Metabolomics studies." Thesis, 2014. http://hdl.handle.net/11858/00-1735-0000-0023-995A-3.

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Puthiyedth, Nisha. "A novel feature selection approach for data integration analysis: applications to transcriptomics study." Thesis, 2016. http://hdl.handle.net/1959.13/1322449.

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Research Doctorate - Doctor of Philosophy (PhD)
Meta-analysis has become a popular method for identifying novel biomarkers in the field of medical research. Meta-analysis has been widely applied to genome-wide association and transcriptomic studies due to the availability of datasets in the public domain. Joint analysis of multiple datasets has become a common technique for increasing statistical power in detecting biomarkers reported in smaller studies. The approach generally followed relies on the fact that as the total number of samples increases, greater power to detect associations of interest is anticipated. Integrating available information from different datasets to generate a combined result seems reasonable and promising. Consequently, there is a need for computationally based integration methods that evaluate multiple independent datasets investigating a common theme or disorder. This raises a variety of issues in the analysis of such data and leads to more complications than are seen with standard meta-analysis, including diverse experimental platforms and complex data structures. I illustrate these ideas using microarray datasets from multiple studies and propose an integrative methodology to combine datasets generated using different platforms. Having combined the data, the main challenge is to choose a subset of features that represent the combined dataset in a particular aspect. While the approach is well established in biostatistics, the introduction of new combinatorial optimisation models to address this issue has not been explored in depth. In 2004, a new feature selection approach based on a combinatorial optimisation method was proposed, entitled the (α,β)-k Feature Set problem approach. The main advantage of this approach over ranking methods for selecting individual features is that the features are evaluated as groups instead of on the basis of their individual performance. The (α,β)-k Feature Set problem approach has been defined having first in mind a single uniform dataset, and conceived in this ways, it is not readily applicable to the case of integrated datasets. An extended version of this approach handles integrated datasets in a consistent manner and selects features that differentiate sample pairs across datasets. The application of an (α,β)-k Feature Set problem -based approach for meta-analysis thus helps to identify the best set of features from a combined dataset, allowing researchers to reveal the genetic pathways that contribute to the development of a disease. I propose an extended version of the (α,β)-k Feature Set problem approach that aims to find a set of genes whose expression level may be used to identify a joint core subset of genes that putatively play an important role in two conditions: prostate cancer and Alzheimer's disease. The results of the current study suggest that the proposed method is an efficient meta-analysis method that is capable of identifying biologically relevant genes that other methods fail to identify. As the amount of data increases, this novel method can be applied to find additional genes and pathways that are significant in these diseases, which may provide new insights into the disease mechanism and contribute towards understanding, prevention and cures.
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Book chapters on the topic "Meta-transcriptomics"

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Fan, Teresa Whei-Mei. "Metabolomics-Edited Transcriptomics Analysis (META)." In Methods in Pharmacology and Toxicology, 439–80. Totowa, NJ: Humana Press, 2012. http://dx.doi.org/10.1007/978-1-61779-618-0_14.

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Macklaim, Jean M., and Gregory B. Gloor. "From RNA-seq to Biological Inference: Using Compositional Data Analysis in Meta-Transcriptomics." In Methods in Molecular Biology, 193–213. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-8728-3_13.

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Fan, T. W. M. "Metabolomics-Edited Transcriptomics Analysis (Meta)." In Comprehensive Toxicology, 685–706. Elsevier, 2010. http://dx.doi.org/10.1016/b978-0-08-046884-6.00239-6.

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Nehme, Ali, Frédéric Mazurier, and Kazem Zibara. "Comprehensive Workflow for Integrative Transcriptomics Meta-Analysis." In Leveraging Biomedical and Healthcare Data, 1–16. Elsevier, 2019. http://dx.doi.org/10.1016/b978-0-12-809556-0.00001-0.

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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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Conference papers on the topic "Meta-transcriptomics"

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He, Daniel, Sabina A. Guler, Casey P. Shannon, Christopher J. Ryerson, and Scott J. Tebbutt. "Systematic review and meta-analysis of interstitial lung disease transcriptomics." In ERS Lung Science Conference 2022 abstracts. European Respiratory Society, 2022. http://dx.doi.org/10.1183/23120541.lsc-2022.84.

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Buiga, Petronela, Jamie Soul, and Jean-Marc Schwartz. "A meta-analysis portal for human breast cancer transcriptomics data: BreastCancerVis." In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2018. http://dx.doi.org/10.1109/bibm.2018.8621315.

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Faner Canet, Maria Rosa, Jarrett Morrow, Guillaume Noell, Alejandra Lopez-Giraldo, Tamara Cruz, Ruth Tal-Singer, Bruce E. Miller, et al. "LATE-BREAKING ABSTRACT: Network-based meta-analysis of lung, sputum and blood transcriptomics in COPD." In ERS International Congress 2016 abstracts. European Respiratory Society, 2016. http://dx.doi.org/10.1183/13993003.congress-2016.oa1776.

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Grigoryev, D. N., K. M. Hernandez, G. A. Rupp, Z. Zhang, and R. L. Grossman. "Meta-Analysis of Transcriptomics of Systemic Sclerosis Related Pulmonary Arterial Hypertension: Search for New Molecular Targets." In American Thoracic Society 2020 International Conference, May 15-20, 2020 - Philadelphia, PA. American Thoracic Society, 2020. http://dx.doi.org/10.1164/ajrccm-conference.2020.201.1_meetingabstracts.a6354.

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