Academic literature on the topic 'Meta-transcriptomics'
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Journal articles on the topic "Meta-transcriptomics"
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.
Full textCobbin, 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.
Full textShi, 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.
Full textFung, 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.
Full textWittekindt, 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.
Full textGust, 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.
Full textBrown, 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.
Full textChialva, 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.
Full textDelhomme, 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.
Full textPorter, 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.
Full textDissertations / Theses on the topic "Meta-transcriptomics"
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.
Full textGiotti, Bruno. "Derivation of the human cell cycle transcriptional signature." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28840.
Full textChang, 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.
Full textTsai, 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.
Full text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.
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.
Full textPuthiyedth, Nisha. "A novel feature selection approach for data integration analysis: applications to transcriptomics study." Thesis, 2016. http://hdl.handle.net/1959.13/1322449.
Full textMeta-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.
Book chapters on the topic "Meta-transcriptomics"
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.
Full textMacklaim, 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.
Full textFan, 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.
Full textNehme, 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.
Full textHassan, 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.
Full textConference papers on the topic "Meta-transcriptomics"
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.
Full textBuiga, 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.
Full textFaner 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.
Full textGrigoryev, 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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