Academic literature on the topic 'Transcriptomics Data Sets'
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Journal articles on the topic "Transcriptomics Data Sets"
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Alkhateeb, Abedalrhman, Iman Rezaeian, Siva Singireddy, Dora Cavallo-Medved, Lisa A. Porter, and Luis Rueda. "Transcriptomics Signature from Next-Generation Sequencing Data Reveals New Transcriptomic Biomarkers Related to Prostate Cancer." Cancer Informatics 18 (January 2019): 117693511983552. http://dx.doi.org/10.1177/1176935119835522.
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Prostate cancer is one of the most common types of cancer among Canadian men. Next-generation sequencing using RNA-Seq provides large amounts of data that may reveal novel and informative biomarkers. We introduce a method that uses machine learning techniques to identify transcripts that correlate with prostate cancer development and progression. We have isolated transcripts that have the potential to serve as prognostic indicators and may have tremendous value in guiding treatment decisions. Analysis of normal versus malignant prostate cancer data sets indicates differential expression of the genes HEATR5B, DDC, and GABPB1-AS1 as potential prostate cancer biomarkers. Our study also supports PTGFR, NREP, SCARNA22, DOCK9, FLVCR2, IK2F3, USP13, and CLASP1 as potential biomarkers to predict prostate cancer progression, especially between stage II and subsequent stages of the disease.
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Bauer, Chris, Karol Stec, Alexander Glintschert, Kristina Gruden, Christian Schichor, Michal Or-Guil, Joachim Selbig, and Johannes Schuchhardt. "BioMiner: Paving the Way for Personalized Medicine." Cancer Informatics 14 (January 2015): CIN.S20910. http://dx.doi.org/10.4137/cin.s20910.
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Personalized medicine is promising a revolution for medicine and human biology in the 21st century. The scientific foundation for this revolution is accomplished by analyzing biological high-throughput data sets from genomics, transcriptomics, proteomics, and metabolomics. Currently, access to these data has been limited to either rather simple Web-based tools, which do not grant much insight or analysis by trained specialists, without firsthand involvement of the physician. Here, we present the novel Web-based tool “BioMiner,” which was developed within the scope of an international and interdisciplinary project (SYSTHER†) and gives access to a variety of high-throughput data sets. It provides the user with convenient tools to analyze complex cross-omics data sets and grants enhanced visualization abilities. BioMiner incorporates transcriptomic and cross-omics high-throughput data sets, with a focus on cancer. A public instance of BioMiner along with the database is available at http://systherDB.microdiscovery.de/ , login and password: “systher”; a tutorial detailing the usage of BioMiner can be found in the Supplementary File.
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Elhossiny, Ahmed M., Eileen Carpenter, Padma Kadiyala, Yaqing Zhang, Filip Bednar, Arvind Rao, Timothy Frankel, and Marina Pasca Di Magliano. "Abstract A006: Integrating single cell and spatial transcriptomics define gene signature for pancreatic ductal adenocarcinoma pre-neoplastic lesion." Cancer Research 82, no. 22_Supplement (November 15, 2022): A006. http://dx.doi.org/10.1158/1538-7445.panca22-a006.
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Abstract Pancreatic Cancer Ductal Adenocarcinoma (PDAC) is one of the deadliest cancers with 5-year survival of 11%. Understanding the intratumor heterogeneity is a pivotal piece to unravel the complexity of PDAC. While single cell RNASeq identifies the heterogeneous cell populations within the tumor tissue, spatially characterizing the transcriptomic profile of neoplastic and pre-neoplastic populations within the tissue remains a challenge, as the spatial dimension is usually lost upon tissue dissociation. We identified an approach to integrate spatial transcriptomics data with single cell RNASeq data. To characterize the cell populations within the tissue we performed single cell RNASeq on disease pathology-free pancreas tissue and primary PDAC samples. We profiled the transcriptomic profile of Acinar, Ductal, Acinar-to-Ductal (ADM), and Pancreatic Intraepithelial Neoplasia (PanINs) regions of interest (ROIs) across the tissue using the Nanostring GeoMx platform. Differential gene expression analysis using linear mixed-effect models of the cell-type specific ROIs defined pan-marker gene sets for each cell type, which were mapped to UMAP projections of single cell RNA sequencing data using AUCell scoring. As expected, the acinar pan-markers gene set derived from the spatial transcriptomics mapped to the manually annotated acinar population in the single cell data. On the other hand, Ductal, ADM, and PanIN pan-marker gene sets were mapped to distinct clusters that previously were not well-defined by single cell sequencing. The analysis coupled with orthogonal validation using RNAScope revealed gene signatures uniquely specific to ADM lesions and PanINs, respectively. Interestingly, our list included known markers as well as novel findings, supporting the validity of the findings. Furthermore, RNA velocity analysis using scVelo revealed a trajectory of cell evolution originating from acinar cells passing through the newly-defined ADM population and ending towards the ductal population derived from tumor samples. Overall, this integration approach of spatial and single cell transcriptomics can further define the characteristics that differentiate neoplastic and pre-neoplastic populations, as well as the potential drivers for tumorigenesis that could be therapeutically targeted. Citation Format: Ahmed M. Elhossiny, Eileen Carpenter, Padma Kadiyala, Yaqing Zhang, Filip Bednar, Arvind Rao, Timothy Frankel, Marina Pasca Di Magliano. Integrating single cell and spatial transcriptomics define gene signature for pancreatic ductal adenocarcinoma pre-neoplastic lesion [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A006.
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Alsagaby, Suliman. "Integration of Proteomics and Transcriptomics Data Sets Identifies Prognostic Markers in Chronic Lymphocytic Leukemia." Majmaah Journal of Health Sciences 7, no. 2 (2019): 1. http://dx.doi.org/10.5455/mjhs.2019.01.002.
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Hamey, Fiona K., and Berthold Göttgens. "Machine learning predicts putative hematopoietic stem cells within large single-cell transcriptomics data sets." Experimental Hematology 78 (October 2019): 11–20. http://dx.doi.org/10.1016/j.exphem.2019.08.009.
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Vahlensieck, Christian, Cora S. Thiel, Jan Adelmann, Beatrice A. Lauber, Jennifer Polzer, and Oliver Ullrich. "Rapid Transient Transcriptional Adaptation to Hypergravity in Jurkat T Cells Revealed by Comparative Analysis of Microarray and RNA-Seq Data." International Journal of Molecular Sciences 22, no. 16 (August 6, 2021): 8451. http://dx.doi.org/10.3390/ijms22168451.
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Cellular responses to micro- and hypergravity are rapid and complex and appear within the first few seconds of exposure. Transcriptomic analyses are a valuable tool to analyze these genome-wide cellular alterations. For a better understanding of the cellular dynamics upon altered gravity exposure, it is important to compare different time points. However, since most of the experiments are designed as endpoint measurements, the combination of cross-experiment meta-studies is inevitable. Microarray and RNA-Seq analyses are two of the main methods to study transcriptomics. In the field of altered gravity research, both methods are frequently used. However, the generation of these data sets is difficult and time-consuming and therefore the number of available data sets in this research field is limited. In this study, we investigated the comparability of microarray and RNA-Seq data and applied the results to a comparison of the transcriptomics dynamics between the hypergravity conditions during two real flight platforms and a centrifuge experiment to identify temporal adaptation processes. We performed a comparative study on an Affymetrix HTA2.0 microarray and a paired-end RNA-Seq data set originating from the same Jurkat T cell RNA samples from a short-term hypergravity experiment. The overall agreeability was high, with better sensitivity of the RNA-Seq analysis. The microarray data set showed weaknesses on the level of single upregulated genes, likely due to its normalization approach. On an aggregated level of biotypes, chromosomal distribution, and gene sets, both technologies performed equally well. The microarray showed better performance on the detection of altered gravity-related splicing events. We found that all initially altered transcripts fully adapted after 15 min to hypergravity and concluded that the altered gene expression response to hypergravity is transient and fully reversible. Based on the combined multiple-platform meta-analysis, we could demonstrate rapid transcriptional adaptation to hypergravity, the differential expression of the ATPase subunits ATP6V1A and ATP6V1D, and the cluster of differentiation (CD) molecules CD1E, CD2AP, CD46, CD47, CD53, CD69, CD96, CD164, and CD226 in hypergravity. We could experimentally demonstrate that it is possible to develop methodological evidence for the meta-analysis of individual data.
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Visentin, Luca, Giorgia Scarpellino, Giorgia Chinigò, Luca Munaron, and Federico Alessandro Ruffinatti. "BioTEA: Containerized Methods of Analysis for Microarray-Based Transcriptomics Data." Biology 11, no. 9 (September 13, 2022): 1346. http://dx.doi.org/10.3390/biology11091346.
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Tens of thousands of gene expression data sets describing a variety of model organisms in many different pathophysiological conditions are currently stored in publicly available databases such as the Gene Expression Omnibus (GEO) and ArrayExpress (AE). As microarray technology is giving way to RNA-seq, it becomes strategic to develop high-level tools of analysis to preserve access to this huge amount of information through the most sophisticated methods of data preparation and processing developed over the years, while ensuring, at the same time, the reproducibility of the results. To meet this need, here we present bioTEA (biological Transcript Expression Analyzer), a novel software tool that combines ease of use with the versatility and power of an R/Bioconductor-based differential expression analysis, starting from raw data retrieval and preparation to gene annotation. BioTEA is an R-coded pipeline, wrapped in a Python-based command line interface and containerized with Docker technology. The user can choose among multiple options—including gene filtering, batch effect handling, sample pairing, statistical test type—to adapt the algorithm flow to the structure of the particular data set. All these options are saved in a single text file, which can be easily shared between different laboratories to deterministically reproduce the results. In addition, a detailed log file provides accurate information about each step of the analysis. Overall, these features make bioTEA an invaluable tool for both bioinformaticians and wet-lab biologists interested in transcriptomics. BioTEA is free and open-source.
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Bisht, Vartika, Katrina Nash, Yuanwei Xu, Prasoon Agarwal, Sofie Bosch, Georgios V. Gkoutos, and Animesh Acharjee. "Integration of the Microbiome, Metabolome and Transcriptomics Data Identified Novel Metabolic Pathway Regulation in Colorectal Cancer." International Journal of Molecular Sciences 22, no. 11 (May 28, 2021): 5763. http://dx.doi.org/10.3390/ijms22115763.
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Integrative multiomics data analysis provides a unique opportunity for the mechanistic understanding of colorectal cancer (CRC) in addition to the identification of potential novel therapeutic targets. In this study, we used public omics data sets to investigate potential associations between microbiome, metabolome, bulk transcriptomics and single cell RNA sequencing datasets. We identified multiple potential interactions, for example 5-aminovalerate interacting with Adlercreutzia; cholesteryl ester interacting with bacterial genera Staphylococcus, Blautia and Roseburia. Using public single cell and bulk RNA sequencing, we identified 17 overlapping genes involved in epithelial cell pathways, with particular significance of the oxidative phosphorylation pathway and the ACAT1 gene that indirectly regulates the esterification of cholesterol. These findings demonstrate that the integration of multiomics data sets from diverse populations can help us in untangling the colorectal cancer pathogenesis as well as postulate the disease pathology mechanisms and therapeutic targets.
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Nehme, Ali, Hassan Dakik, Frédéric Picou, Meyling Cheok, Claude Preudhomme, Hervé Dombret, Juliette Lambert, et al. "Horizontal meta-analysis identifies common deregulated genes across AML subgroups providing a robust prognostic signature." Blood Advances 4, no. 20 (October 27, 2020): 5322–35. http://dx.doi.org/10.1182/bloodadvances.2020002042.
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Abstract Advances in transcriptomics have improved our understanding of leukemic development and helped to enhance the stratification of patients. The tendency of transcriptomic studies to combine AML samples, regardless of cytogenetic abnormalities, could lead to bias in differential gene expression analysis because of the differential representation of AML subgroups. Hence, we performed a horizontal meta-analysis that integrated transcriptomic data on AML from multiple studies, to enrich the less frequent cytogenetic subgroups and to uncover common genes involved in the development of AML and response to therapy. A total of 28 Affymetrix microarray data sets containing 3940 AML samples were downloaded from the Gene Expression Omnibus database. After stringent quality control, transcriptomic data on 1534 samples from 11 data sets, covering 10 AML cytogenetically defined subgroups, were retained and merged with the data on 198 healthy bone marrow samples. Differentially expressed genes between each cytogenetic subgroup and normal samples were extracted, enabling the unbiased identification of 330 commonly deregulated genes (CODEGs), which showed enriched profiles of myeloid differentiation, leukemic stem cell status, and relapse. Most of these genes were downregulated, in accordance with DNA hypermethylation. CODEGs were then used to create a prognostic score based on the weighted sum of expression of 22 core genes (CODEG22). The score was validated with microarray data of 5 independent cohorts and by quantitative real time-polymerase chain reaction in a cohort of 142 samples. CODEG22-based stratification of patients, globally and into subpopulations of cytologically healthy and elderly individuals, may complement the European LeukemiaNet classification, for a more accurate prediction of AML outcomes.
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Di Filippo, Marzia, Dario Pescini, Bruno Giovanni Galuzzi, Marcella Bonanomi, Daniela Gaglio, Eleonora Mangano, Clarissa Consolandi, Lilia Alberghina, Marco Vanoni, and Chiara Damiani. "INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation." PLOS Computational Biology 18, no. 2 (February 7, 2022): e1009337. http://dx.doi.org/10.1371/journal.pcbi.1009337.
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Metabolism is directly and indirectly fine-tuned by a complex web of interacting regulatory mechanisms that fall into two major classes. On the one hand, the expression level of the catalyzing enzyme sets the maximal theoretical flux level (i.e., the net rate of the reaction) for each enzyme-controlled reaction. On the other hand, metabolic regulation controls the metabolic flux through the interactions of metabolites (substrates, cofactors, allosteric modulators) with the responsible enzyme. High-throughput data, such as metabolomics and transcriptomics data, if analyzed separately, do not accurately characterize the hierarchical regulation of metabolism outlined above. They must be integrated to disassemble the interdependence between different regulatory layers controlling metabolism. To this aim, we propose INTEGRATE, a computational pipeline that integrates metabolomics and transcriptomics data, using constraint-based stoichiometric metabolic models as a scaffold. We compute differential reaction expression from transcriptomics data and use constraint-based modeling to predict if the differential expression of metabolic enzymes directly originates differences in metabolic fluxes. In parallel, we use metabolomics to predict how differences in substrate availability translate into differences in metabolic fluxes. We discriminate fluxes regulated at the metabolic and/or gene expression level by intersecting these two output datasets. We demonstrate the pipeline using a set of immortalized normal and cancer breast cell lines. In a clinical setting, knowing the regulatory level at which a given metabolic reaction is controlled will be valuable to inform targeted, truly personalized therapies in cancer patients.
Dissertations / Theses on the topic "Transcriptomics Data Sets"
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Coudret, Raphaël. "Stochastic modelling using large data sets : applications in ecology and genetics." Phd thesis, Université Sciences et Technologies - Bordeaux I, 2013. http://tel.archives-ouvertes.fr/tel-00865867.
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There are two main parts in this thesis. The first one concerns valvometry, which is here the study of the distance between both parts of the shell of an oyster, over time. The health status of oysters can be characterized using valvometry in order to obtain insights about the quality of their environment. We consider that a renewal process with four states underlies the behaviour of the studied oysters. Such a hidden process can be retrieved from a valvometric signal by assuming that some probability density function linked with this signal, is bimodal. We then compare several estimators which take this assumption into account, including kernel density estimators.In another chapter, we compare several regression approaches, aiming at analysing transcriptomic data. To understand which explanatory variables have an effect on gene expressions, we apply a multiple testing procedure on these data, through the linear model FAMT. The SIR method may find nonlinear relations in such a context. It is however more commonly used when the response variable is univariate. A multivariate version of SIR was then developed. Procedures to measure gene expressions can be expensive. The sample size n of the corresponding datasets is then often small. That is why we also studied SIR when n is less than the number of explanatory variables p.
Book chapters on the topic "Transcriptomics Data Sets"
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Milone, Diego, Georgina Stegmayer, Matías Gerard, Laura Kamenetzky, Mariana López, and Fernando Carrari. "Analysis and Integration of Biological Data." In Data Mining, 203–30. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2455-9.ch011.
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The volume of information derived from post genomic technologies is rapidly increasing. Due to the amount of involved data, novel computational methods are needed for the analysis and knowledge discovery into the massive data sets produced by these new technologies. Furthermore, data integration is also gaining attention for merging signals from different sources in order to discover unknown relations. This chapter presents a pipeline for biological data integration and discovery of a priori unknown relationships between gene expressions and metabolite accumulations. In this pipeline, two standard clustering methods are compared against a novel neural network approach. The neural model provides a simple visualization interface for identification of coordinated patterns variations, independently of the number of produced clusters. Several quality measurements have been defined for the evaluation of the clustering results obtained on a case study involving transcriptomic and metabolomic profiles from tomato fruits. Moreover, a method is proposed for the evaluation of the biological significance of the clusters found. The neural model has shown a high performance in most of the quality measures, with internal coherence in all the identified clusters and better visualization capabilities.
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Zhang, Wei, Gabriel R. Fries, and Joao Quevedo. "The Use of Bioinformatics and Big Data for the In Silico Study of Psychiatric Disorders." In Convergence Mental Health, edited by Laura M. Hack and Leanne M. Williams, 255–68. Oxford University Press, 2021. http://dx.doi.org/10.1093/med/9780197506271.003.0017.
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Mental and behavioral disorders are becoming the leading cause of disability across the world. Along with the ongoing development of biomedical and computational technologies, more and more data are being constantly produced, including genomic, transcriptomic, metabolomic, proteomic, clinical, and imaging resources. As a consequence, scientists in the psychiatric field are actively changing their research ways from studies focused on individual investigators to large international consortia, which accelerate the data accumulation and increase its size. This chapter discusses the current publicly available data sets on psychiatry disorders and neuroscience, as well as their integrated analysis. The authors also list some studies using novel types of data, which will further extent the potential of big data in the study of psychiatric disorders.
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Verbeke, Lieven, and Steven Van Laere. "Cancer systems biology: From molecular profiles to pathways, signalling networks, and therapeutic vulnerabilities." In Oxford Textbook of Cancer Biology, edited by Francesco Pezzella, Mahvash Tavassoli, and David J. Kerr, 375–93. Oxford University Press, 2019. http://dx.doi.org/10.1093/med/9780198779452.003.0026.
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Cancer systems biology encompasses the application of systems biology approaches to cancer research. Historically, systems biology was first applied in cancer research to enable a pathway-oriented interpretation of gene expression data and this strategy has undoubtedly delivered relevant insights with respect to many aspects of cancer biology. Nowadays, cancer is regarded as a complex system that integrates signals from different levels (i.e. (epi)genomics, transcriptomics, micro-environment) through a network of interconnected proteins to generate a biological response. This holistic approach not only allows the identification of new and relevant signal transduction pathways, but also provides a better understanding of several key properties of cancer cells that can be best understood from a network-level perspective: robustness, evolvability, and plasticity. This chapter provides an overview of several key concepts of systems biology, including reference gene set libraries, network topology, and available strategies to establish biological networks. Next, these concepts are utilized to explain gene set and gene network analysis with particular focus on cancer biology. Finally, the caveats and challenges that are facing cancer systems biology are summarized.
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Frasca, Jr, Salvatore, Rebecca J. Gast, Andrea L. Bogomolni, and Steven M. Szczepanek. "Diagnosing marine diseases." In Marine Disease Ecology, 213–32. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198821632.003.0011.
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Infectious disease concerns are paramount when considering the health of the oceans and seas of the world. Understanding the ecology of disease in marine environments requires knowledge of diagnostic principles and techniques. Morphologic and molecular approaches exist that allow for the detection of infectious agents from marine life and from the marine environment. However, detection of infection may not be the equivalent of a diagnosis of disease. Disease determination requires recognition of anatomic, biochemical, and molecular features that are characteristic of the disease state and that identify pathogenic organisms. Disease investigations in marine scenarios can be complex and may engage concurrently a wide variety of techniques including microbiological culture and isolation, histotechnological procedures performed on arrays of tissue samples, immunohistochemical methodologies, and nucleic acid-based techniques that make use of genetic, genomic, transcriptomic, and metagenomic data. Effective use of these techniques requires knowledge of their capabilities and limitations so that appropriate selection, proper application, and accurate interpretation can be made.
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van Dam, Pieter-Jan, and Steven Van Laere. "Molecular profiling in cancer research and personalized medicine." In Oxford Textbook of Cancer Biology, edited by Francesco Pezzella, Mahvash Tavassoli, and David J. Kerr, 347–62. Oxford University Press, 2019. http://dx.doi.org/10.1093/med/9780198779452.003.0024.
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Recent efforts by worldwide consortia such as The Cancer Genome Atlas and the International Cancer Genome Consortium have greatly accelerated our knowledge of human cancer biology. Nowadays, complete sets of human tumours that have been characterized at the genomic, epigenomic, transcriptomic, or proteomic level are available to the research community. The generation of these data was made possible thanks to the application of high-throughput molecular profiling techniques such as microarrays and next-generation sequencing. The primary conclusion from current profiling experiments is that human cancer is a complex disease characterized by extreme molecular heterogeneity, both between and within the classical, tissue-defined cancer types. This molecular variety necessitates a paradigm shift in patient management, away from generalized therapy schemes and towards more personalized treatments. This chapter provides an overview of how molecular cancer profiling can assist in facilitating this transition. First, the state-of-the-art of molecular breast cancer profiling is reviewed to provide a general background. Then, the most pertinent high-throughput molecular profiling techniques along with various data mining techniques (i.e. unsupervised clustering, statistical learning) are discussed. Finally, the challenges and perspectives with respect to molecular cancer profiling, also from the perspective of personalized medicine, are summarized.
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von Reumont, Björn M., and Gregory D. Edgecombe. "Crustaceans and Insect Origins." In Evolution and Biogeography, 105–20. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190637842.003.0005.
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Although Crustacea has a long history of being recognized as a formal taxonomic group in arthropod classification, the past 30 years have witnessed repeated challenges to crustacean monophyly. Few unambiguous autapomorphic characters for crustaceans have been proposed by morphologists, and many diagnostic characters can be interpreted as symplesiomorphies of Mandibulata. More serious challenges arise from molecular phylogenetics: irrespective of the scope of taxonomic and/or character sampling or analytical methods, a pancrustacean clade in which “Crustacea” is paraphyletic with respect to Hexapoda is retrieved. However, most traditional single to multigene studies infer phylogenies that display considerable mutual conflict. Although hexapod monophyly is robust and its deep branchings have recently been recovered using large-scale transcriptomic datasets, its crustacean sister group has been contentious. To some extent, a conclusive result is still hindered by uneven taxonomic coverage, with some key groups still being undersampled in phylogenomic studies. Nonetheless, phylogenomic analyses provide some robust results: notably, Hexapoda is part of a pancrustacean clade named Allotriocarida, which includes Cephalocarida and Branchiopoda as a grade or each other’s sister group, and Remipedia as the closest relatives to Hexapoda. Neuroanatomical support for a rival malacostracan-remipede-hexapod clade is incongruent with molecular datasets, which instead group Malacostraca, Copepoda, and Cirripedia as a clade. However, cirripedes resolve either as a sister group to copepods or to malacostracans, and this instability casts doubt on the typical pattern in molecular analyses that position malacostracans unexpectedly deep within the crustacean lineage. Pancrustacean phylogeny requires critical interpretation of phylogenomic data to reveal conflict in the data and ambiguous signals within the selected set of orthologous genes.
Reports on the topic "Transcriptomics Data Sets"
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Cohen, Yuval, Christopher A. Cullis, and Uri Lavi. Molecular Analyses of Soma-clonal Variation in Date Palm and Banana for Early Identification and Control of Off-types Generation. United States Department of Agriculture, October 2010. http://dx.doi.org/10.32747/2010.7592124.bard.
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Date palm (Phoenix dactylifera L.) is the major fruit tree grown in arid areas in the Middle East and North Africa. In the last century, dates were introduced to new regions including the USA. Date palms are traditionally propagated through offshoots. Expansion of modern date palm groves led to the development of Tissue Culture propagation methods that generate a large number of homogenous plants, have no seasonal effect on plant source and provide tools to fight the expansion of date pests and diseases. The disadvantage of this procedure is the occurrence of off-type trees which differ from the original cultivar. In the present project we focused on two of the most common date palm off-types: (1) trees with reduced fruit setting, in which most of the flowers turn into three-carpel parthenocarpic fruits. In a severe form, multi-carpel flowers and fruitlets (with up to six or eight carpels instead of the normal three-carpel flowers) are also formed. (2) dwarf trees, having fewer and shorter leaves, very short trunk and are not bearing fruits at their expected age, compared to the normal trees. Similar off-types occur in other crop species propagated by tissue culture, like banana (mainly dwarf plants) or oil palm (with a common 'Mantled' phenotype with reduced fruit setting and occurrence of supernumerary carpels). Some off-types can only be detected several years after planting in the fields. Therefore, efficient methods for prevention of the generation of off-types, as well as methods for their detection and early removal, are required for date palms, as well as for other tissue culture propagated crops. This research is aimed at the understanding of the mechanisms by which off-types are generated, and developing markers for their early identification. Several molecular and genomic approaches were applied. Using Methylation Sensitive AFLP and bisulfite sequencing, we detected changes in DNA methylation patterns occurring in off-types. We isolated and compared the sequence and expression of candidate genes, genes related to vegetative growth and dwarfism and genes related to flower development. While no sequence variation were detected, changes in gene expression, associated with the severity of the "fruit set" phenotype were detected in two genes - PdDEF (Ortholog of rice SPW1, and AP3 B type MADS box gene), and PdDIF (a defensin gene, highly homologous to the oil palm gene EGAD). We applied transcriptomic analyses, using high throughput sequencing, to identify genes differentially expressed in the "palm heart" (the apical meristem and the region of embryonic leaves) of dwarf vs. normal trees. Among the differentially expressed genes we identified genes related to hormonal biosynthesis, perception and regulation, genes related to cell expansion, and genes related to DNA methylation. Using Representation Difference Analyses, we detected changes in the genomes of off-type trees, mainly chloroplast-derived sequences that were incorporated in the nuclear genome and sequences of transposable elements. Sequences previously identified as differing between normal and off-type trees of oil palms or banana, successfully identified variation among date palm off-types, suggesting that these represent highly labile regions of monocot genomes. The data indicate that the date palm genome, similarly to genomes of other monocot crops as oil palm and banana, is quite unstable when cells pass through a cycle of tissue culture and regeneration. Changes in DNA sequences, translocation of DNA fragments and alteration of methylation patterns occur. Consequently, patterns of gene expression are changed, resulting in abnormal phenotypes. The data can be useful for future development of tools for early identification of off-type as well as for better understanding the phenomenon of somaclonal variation during propagation in vitro.