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Auswahl der wissenschaftlichen Literatur zum Thema „Transcriptomic data analysis“
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Zeitschriftenartikel zum Thema "Transcriptomic data analysis"
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Gorbunova, Vera. „COMPARATIVE TRANSCRIPTOMIC OF LONGEVITY“. Innovation in Aging 7, Supplement_1 (01.12.2023): 432. http://dx.doi.org/10.1093/geroni/igad104.1423.
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Abstract Transcriptome analysis provides a nuanced view into the changes that occur in cells and tissues. Transcriptome changes rapidly and reproducibly in response to physiological influences and environmental insults. Recent years have seen an exponential increase in transcriptome data at bulk, single cell and spatial resolution that allows insights into the mechanisms and regulatory pathways of aging and longevity. In this session Drs. Gorbunova (University of Rochester) and Gladyshev (Harvard Medical School) will discuss comparative transcriptomics of longevity across species with diverse lifespans that revealed unique signatures of longevity and the integration of transcriptome and proteome data. Dr. Gladyshev will discuss development of transcriptomic clocks of measuring biological aging. Dr. Artyomov will discuss single-cell resolution approaches to reveal aspects of immune aging in humans, and Dr. Palovics will present the use of transcriptomics to understand rejuvenating effects of heterochronic parabiosis.
2
Dries, Ruben, Jiaji Chen, Natalie del Rossi, Mohammed Muzamil Khan, Adriana Sistig und Guo-Cheng Yuan. „Advances in spatial transcriptomic data analysis“. Genome Research 31, Nr. 10 (Oktober 2021): 1706–18. http://dx.doi.org/10.1101/gr.275224.121.
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Spatial transcriptomics is a rapidly growing field that promises to comprehensively characterize tissue organization and architecture at the single-cell or subcellular resolution. Such information provides a solid foundation for mechanistic understanding of many biological processes in both health and disease that cannot be obtained by using traditional technologies. The development of computational methods plays important roles in extracting biological signals from raw data. Various approaches have been developed to overcome technology-specific limitations such as spatial resolution, gene coverage, sensitivity, and technical biases. Downstream analysis tools formulate spatial organization and cell–cell communications as quantifiable properties, and provide algorithms to derive such properties. Integrative pipelines further assemble multiple tools in one package, allowing biologists to conveniently analyze data from beginning to end. In this review, we summarize the state of the art of spatial transcriptomic data analysis methods and pipelines, and discuss how they operate on different technological platforms.
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Nesterenko, Maksim, und Aleksei Miroliubov. „From head to rootlet: comparative transcriptomic analysis of a rhizocephalan barnacle Peltogaster reticulata (Crustacea: Rhizocephala)“. F1000Research 11 (27.05.2022): 583. http://dx.doi.org/10.12688/f1000research.110492.1.
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Background: Rhizocephalan barnacles stand out in the diverse world of metazoan parasites. The body of a rhizocephalan female is modified beyond revealing any recognizable morphological features, consisting of the interna, the system of rootlets, and the externa, a sac-like reproductive body. Moreover, rhizocephalans have an outstanding ability to control their hosts, literally turning them into “zombies”. Despite all these amazing traits, there is no genomic and transcriptomic data about any Rhizocephala. Methods: We collected transcriptomes from four body parts of an adult female rhizocephalan Peltogaster reticulata: externa and main, growing, and thoracic parts of the interna. We used all prepared data for the de novo assembly of the reference transcriptome. Next, a set of encoded proteins was determined, the expression levels of protein-coding genes in different parts of the parasite body were calculated and lists of enriched bioprocesses were identified. We also in silico identified and analyzed sets of potential excretory / secretory proteins. Finally, we applied phylostratigraphy and evolutionary transcriptomics approaches to our data. Results: The assembled reference transcriptome included transcripts of 12,620 protein-coding genes and was the first for both P. reticulata and Rhizocephala. Based on the results obtained, the spatial heterogeneity of protein-coding genes expression in different regions of P. reticulata adult female body was established. The results of both transcriptomic analysis and histological studies indicated the presence of germ-like cells in the lumen of the interna. The potential molecular basis of the interaction between the nervous system of the host and the parasite's interna was also determined. Given the prolonged expression of development-associated genes, we suggest that rhizocephalans “got stuck in the metamorphosis”, even in their reproductive stage. Conclusions: The results of the first comparative transcriptomic analysis for Rhizocephala not only clarified but also expanded the existing ideas about the biology of this amazing parasites.
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Nesterenko, Maksim, und Aleksei Miroliubov. „From head to rootlet: comparative transcriptomic analysis of a rhizocephalan barnacle Peltogaster reticulata (Crustacea: Rhizocephala)“. F1000Research 11 (09.01.2023): 583. http://dx.doi.org/10.12688/f1000research.110492.2.
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Background: Rhizocephalan barnacles stand out in the diverse world of metazoan parasites. The body of a rhizocephalan female is modified beyond revealing any recognizable morphological features, consisting of the interna, a system of rootlets, and the externa, a sac-like reproductive body. Moreover, rhizocephalans have an outstanding ability to control their hosts, literally turning them into “zombies”. Despite all these amazing traits, there are no genomic or transcriptomic data about any Rhizocephala. Methods: We collected transcriptomes from four body parts of an adult female rhizocephalan Peltogaster reticulata: the externa, and the main, growing, and thoracic parts of the interna. We used all prepared data for the de novo assembly of the reference transcriptome. Next, a set of encoded proteins was determined, the expression levels of protein-coding genes in different parts of the parasite’s body were calculated and lists of enriched bioprocesses were identified. We also in silico identified and analyzed sets of potential excretory / secretory proteins. Finally, we applied phylostratigraphy and evolutionary transcriptomics approaches to our data. Results: The assembled reference transcriptome included transcripts of 12,620 protein-coding genes and was the first for any rhizocephalan. Based on the results obtained, the spatial heterogeneity of protein-coding gene expression in different regions of the adult female body of P. reticulata was established. The results of both transcriptomic analysis and histological studies indicated the presence of germ-like cells in the lumen of the interna. The potential molecular basis of the interaction between the nervous system of the host and the parasite's interna was also determined. Given the prolonged expression of development-associated genes, we suggest that rhizocephalans “got stuck in their metamorphosis”, even at the reproductive stage. Conclusions: The results of the first comparative transcriptomic analysis for Rhizocephala not only clarified but also expanded the existing ideas about the biology of these extraordinary parasites.
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Macrander, Jason, Jyothirmayi Panda, Daniel Janies, Marymegan Daly und Adam M. Reitzel. „Venomix: a simple bioinformatic pipeline for identifying and characterizing toxin gene candidates from transcriptomic data“. PeerJ 6 (31.07.2018): e5361. http://dx.doi.org/10.7717/peerj.5361.
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The advent of next-generation sequencing has resulted in transcriptome-based approaches to investigate functionally significant biological components in a variety of non-model organism. This has resulted in the area of “venomics”: a rapidly growing field using combined transcriptomic and proteomic datasets to characterize toxin diversity in a variety of venomous taxa. Ultimately, the transcriptomic portion of these analyses follows very similar pathways after transcriptome assembly often including candidate toxin identification using BLAST, expression level screening, protein sequence alignment, gene tree reconstruction, and characterization of potential toxin function. Here we describe the Python package Venomix, which streamlines these processes using common bioinformatic tools along with ToxProt, a publicly available annotated database comprised of characterized venom proteins. In this study, we use the Venomix pipeline to characterize candidate venom diversity in four phylogenetically distinct organisms, a cone snail (Conidae; Conus sponsalis), a snake (Viperidae; Echis coloratus), an ant (Formicidae; Tetramorium bicarinatum), and a scorpion (Scorpionidae; Urodacus yaschenkoi). Data on these organisms were sampled from public databases, with each original analysis using different approaches for transcriptome assembly, toxin identification, or gene expression quantification. Venomix recovered numerically more candidate toxin transcripts for three of the four transcriptomes than the original analyses and identified new toxin candidates. In summary, we show that the Venomix package is a useful tool to identify and characterize the diversity of toxin-like transcripts derived from transcriptomic datasets. Venomix is available at: https://bitbucket.org/JasonMacrander/Venomix/.
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Ochsner, Scott A., Christopher M. Watkins, Apollo McOwiti, Xueping Xu, Yolanda F. Darlington, Michael D. Dehart, Austin J. Cooney, David L. Steffen, Lauren B. Becnel und Neil J. McKenna. „Transcriptomine, a web resource for nuclear receptor signaling transcriptomes“. Physiological Genomics 44, Nr. 17 (01.09.2012): 853–63. http://dx.doi.org/10.1152/physiolgenomics.00033.2012.
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The nuclear receptor (NR) superfamily of ligand-regulated transcription factors directs ligand- and tissue-specific transcriptomes in myriad developmental, metabolic, immunological, and reproductive processes. The NR signaling field has generated a wealth of genome-wide expression data points, but due to deficits in their accessibility, annotation, and integration, the full potential of these studies has not yet been realized. We searched public gene expression databases and MEDLINE for global transcriptomic datasets relevant to NRs, their ligands, and coregulators. We carried out extensive, deep reannotation of the datasets using controlled vocabularies for RNA Source and regulating molecule and resolved disparate gene identifiers to official gene symbols to facilitate comparison of fold changes and their significance across multiple datasets. We assembled these data points into a database, Transcriptomine ( http://www.nursa.org/transcriptomine ), that allows for multiple, menu-driven querying strategies of this transcriptomic “superdataset,” including single and multiple genes, Gene Ontology terms, disease terms, and uploaded custom gene lists. Experimental variables such as regulating molecule, RNA Source, as well as fold-change and P value cutoff values can be modified, and full data records can be either browsed or downloaded for downstream analysis. We demonstrate the utility of Transcriptomine as a hypothesis generation and validation tool using in silico and experimental use cases. Our resource empowers users to instantly and routinely mine the collective biology of millions of previously disparate transcriptomic data points. By incorporating future transcriptome-wide datasets in the NR signaling field, we anticipate Transcriptomine developing into a powerful resource for the NR- and other signal transduction research communities.
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Riquelme-Perez, Miriam, Fernando Perez-Sanz, Jean-François Deleuze, Carole Escartin, Eric Bonnet und Solène Brohard. „DEVEA: an interactive shiny application for Differential Expression analysis, data Visualization and Enrichment Analysis of transcriptomics data“. F1000Research 11 (24.03.2023): 711. http://dx.doi.org/10.12688/f1000research.122949.2.
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We are at a time of considerable growth in transcriptomics studies and subsequent in silico analysis. RNA sequencing (RNA-Seq) is the most widely used approach to analyse the transcriptome and is integrated in many studies. The processing of transcriptomic data typically requires a noteworthy number of steps, statistical knowledge, and coding skills, which are not accessible to all scientists. Despite the development of a plethora of software applications over the past few years to address this concern, there is still room for improvement. Here we present DEVEA, an R shiny application tool developed to perform differential expression analysis, data visualization and enrichment pathway analysis mainly from transcriptomics data, but also from simpler gene lists with or without statistical values. The intuitive and easy-to-manipulate interface facilitates gene expression exploration through numerous interactive figures and tables, and statistical comparisons of expression profile levels between groups. Further meta-analysis such as enrichment analysis is also possible, without the need for prior bioinformatics expertise. DEVEA performs a comprehensive analysis from multiple and flexible data sources representing distinct analytical steps. Consequently, it produces dynamic graphs and tables, to explore the expression levels and statistical results from differential expression analysis. Moreover, it generates a comprehensive pathway analysis to extend biological insights. Finally, a complete and customizable HTML report can be extracted to enable the scientists to explore results beyond the application. DEVEA is freely accessible at https://shiny.imib.es/devea/ and the source code is available on our GitHub repository https://github.com/MiriamRiquelmeP/DEVEA.
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Kriger, Draco, Michael A. Pasquale, Brigitte G. Ampolini und Jonathan R. Chekan. „Mining raw plant transcriptomic data for new cyclopeptide alkaloids“. Beilstein Journal of Organic Chemistry 20 (11.07.2024): 1548–59. http://dx.doi.org/10.3762/bjoc.20.138.
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In recent years, genome and transcriptome mining have dramatically expanded the rate of discovering diverse natural products from bacteria and fungi. In plants, this approach is often more limited due to the lack of available annotated genomes and transcriptomes combined with a less consistent clustering of biosynthetic genes. The recently identified burpitide class of ribosomally synthesized and post-translationally modified peptide (RiPP) natural products offer a valuable opportunity for bioinformatics-guided discovery in plants due to their short biosynthetic pathways and gene encoded substrates. Using a high-throughput approach to assemble and analyze 700 publicly available raw transcriptomic data sets, we uncover the potential distribution of split burpitide precursor peptides in Streptophyta. Metabolomic analysis of target plants confirms our bioinformatic predictions of new cyclopeptide alkaloids from both known and new sources.
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Parmar, Sourabh. „Transcriptomics Analysis using Galaxy and other Online Servers for Rheumatoid Arthritis“. International Journal for Research in Applied Science and Engineering Technology 9, Nr. VII (10.07.2021): 459–66. http://dx.doi.org/10.22214/ijraset.2021.36331.
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Researchers use transcriptomics analyses for biological data mining, interpretation, and presentation. Galaxy-based tools are utilized to analyze various complex disease transcriptomic data to understand the pathogenesis of the disease, which are user-friendly. This work provides simple methods for differential expression analysis and analysis of these results in gene ontology and pathway enrichment tools like David, WebGestalt. This method is very effective in better analysis and understanding the transcriptomic data. Transcriptomics analysis has been made on rheumatoid arthritis sra data. Rheumatoid arthritis (RA) is a systemic autoimmune disease. T cells and autoantibodies mediate the pathogenesis. This article discusses the genes which are differentially expressed between the healthy (n=50) and diseased (n=51) and the functions of those genes in the pathogenesis of RA.
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Li, Youcheng, Leann Lac, Qian Liu und Pingzhao Hu. „ST-CellSeg: Cell segmentation for imaging-based spatial transcriptomics using multi-scale manifold learning“. PLOS Computational Biology 20, Nr. 6 (27.06.2024): e1012254. http://dx.doi.org/10.1371/journal.pcbi.1012254.
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Spatial transcriptomics has gained popularity over the past decade due to its ability to evaluate transcriptome data while preserving spatial information. Cell segmentation is a crucial step in spatial transcriptomic analysis, as it enables the avoidance of unpredictable tissue disentanglement steps. Although high-quality cell segmentation algorithms can aid in the extraction of valuable data, traditional methods are frequently non-spatial, do not account for spatial information efficiently, and perform poorly when confronted with the problem of spatial transcriptome cell segmentation with varying shapes. In this study, we propose ST-CellSeg, an image-based machine learning method for spatial transcriptomics that uses manifold for cell segmentation and is novel in its consideration of multi-scale information. We first construct a fully connected graph which acts as a spatial transcriptomic manifold. Using multi-scale data, we then determine the low-dimensional spatial probability distribution representation for cell segmentation. Using the adjusted Rand index (ARI), normalized mutual information (NMI), and Silhouette coefficient (SC) as model performance measures, the proposed algorithm significantly outperforms baseline models in selected datasets and is efficient in computational complexity.
Dissertationen zum Thema "Transcriptomic data analysis"
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Xu, Huan. „Controlling false positive rate in network analysis of transcriptomic data“. University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin156267322069819.
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2
Kmetzsch, Virgilio. „Multimodal analysis of neuroimaging and transcriptomic data in genetic frontotemporal dementia“. Electronic Thesis or Diss., Sorbonne université, 2022. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2022SORUS279.pdf.
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La démence frontotemporale (DFT) représente le deuxième type de démence le plus fréquent chez les adultes de moins de 65 ans. Il n’existe aucun traitement capable de guérir cette maladie. Dans ce contexte, il est essentiel d’identifier des biomarqueurs capables d’évaluer la progression de la maladie. Cette thèse a deux objectifs. Premièrement, analyser les profils d’expression des microARNs circulants prélevés dans le plasma sanguin de participants, afin d’identifier si l’expression de certains microARNs est corrélée au statut mutationnel et à la progression de la maladie. Deuxièmement, proposer des méthodes pour intégrer des données transversales de type microARN et de neuroimagerie pour estimer la progression de la maladie. Nous avons mené trois études. D’abord, nous avons analysé des échantillons de plasma provenant de porteurs d’une expansion dans le gène C9orf72. Ensuite, nous avons testé toutes les signatures de microARNs identifiées dans la littérature comme biomarqueurs potentiels de la DFT ou de la sclérose latérale amyotrophique (SLA), dans deux cohortes indépendantes. Enfin, dans notre troisième étude, nous avons proposé une nouvelle méthode, utilisant un autoencodeur variationnel multimodal supervisé, qui estime à partir d’échantillons de petite taille un score de progression de la maladie en fonction de données transversales d’expression de microARNs et de neuroimagerie. Les travaux menés dans cette thèse interdisciplinaire ont montré qu’il est possible d’utiliser des biomarqueurs non invasifs, tels que les microARNs circulants et l’imagerie par résonance magnétique, pour évaluer la progression de maladies neurodégénératives rares telles que la DFT et la SLAFrontotemporal dementia (FTD) represents the second most common type of dementia in adults under the age of 65. Currently, there are no treatments that can cure this condition. In this context, it is essential that biomarkers capable of assessing disease progression are identified. This thesis has two objectives. First, to analyze the expression patterns of microRNAs taken from blood samples of patients, asymptomatic individuals who have certain genetic mutations causing FTD, and controls, to identify whether the expressions of some microRNAs correlate with mutation status and disease progression. Second, this work aims at proposing methods for integrating cross-sectional data from microRNAs and neuroimaging to estimate disease progression. We conducted three studies. Initially, we focused on plasma samples from C9orf72 expansion carriers. We identified four microRNAs whose expressions correlated with the clinical status of the participants. Next, we tested all microRNA signatures identified in the literature as potential biomarkers of FTD or amyotrophic lateral sclerosis (ALS), in two groups of individuals. Finally, in our third work, we proposed a new approach, using a supervised multimodal variational autoencoder, that estimates a disease progression score from cross-sectional microRNA expression and neuroimaging datasets with small sample sizes. The work conducted in this interdisciplinary thesis showed that it is possible to use non-invasive biomarkers, such as circulating microRNAs and magnetic resonance imaging, to assess the progression of rare neurodegenerative diseases such as FTD and ALS
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Caterino, Cinzia. „The aging synapse: an integrated proteomic and transcriptomic analysis“. Doctoral thesis, Scuola Normale Superiore, 2019. http://hdl.handle.net/11384/86004.
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An important hallmark of aging is the loss of proteostasis, which can lead to the formation of protein
aggregates and mitochondrial dysfunction in neurons. Although it is well known that protein
synthesis is finely regulated in the brain, especially at synapses, where mRNAs are locally translated in
activity-dependent manner, little is known as to the changes in the synaptic proteome and
transcriptome during aging. Therefore, this work aims to elucidate the relationship between
transcriptome and proteome at soma and synaptic level during aging.
Cerebral cortices were isolated from 3 weeks-old mice, 5 months-old and 18 months-old mice and
synaptosomal fraction was extracted by ultracentrifugation on discontinuous sucrose gradient. The
fraction was then analyzed by Data Independent Analysis (DIA) Mass Spectrometry and the resulting
data were analyzed using Spectronaut software. RNA was also extracted and analyzed by ribo-zero
RNA-seq. Data were analyzed and combined with R software.
Proteomic and transcriptomic data analysis revealed that, in young animals, proteins and transcripts
are correlated and synaptic regulation is driven by changes in the soma. During aging, there is a
decoupling between transcripts and proteins and between somatic and synaptic compartments. For
example, there is an increase of ribosomal proteins at synapses that is not mirrored by a concomitant
increase at somatic level. Furthermore, soma-synapse gradient of ribosomal genes changes upon
aging, i.e. ribosomal transcripts are less abundant and ribosomal proteins are more abundant in
synaptic compartment of old mice with respect to younglings.
Mass spectrometry analysis of synaptic protein aggregates revealed that they are particularly rich in
ribosomal proteins and also of some components of lysosomes and proteasome, suggesting that loss
of proteostasis and inefficient degradation leads to aggregation of ribosomes in synaptic
compartment. Strikingly, Desmoplakin, a structural constituent of desmosomes, was also highly
abundant in synaptic aggregates. This study suggests that aging affects both the local translational
machinery and the trafficking of transcripts and proteins.
4
Captier, Nicolas. „Multimodal analysis of radiological, pathological, and transcriptomic data for the prediction of immunotherapy outcome in Non-Small Cell Lung Cancer patients“. Electronic Thesis or Diss., Université Paris sciences et lettres, 2024. http://www.theses.fr/2024UPSLS012.
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La survie globale des patients atteints de cancer du poumon non à petites cellules (CPNPC) métastatique a augmenté grâce à l’utilisation d’immunothérapies anti-PD1/PD-L1. Cependant, la durée de la réponse reste très variable d'un patient à l'autre, et seuls 20 à 30 % des patients sont encore en vie après deux ans. Par conséquent, de nouveaux biomarqueurs permettant de prédire la réponse au traitement et le pronostic des patients sont nécessaires pour guider la décision thérapeutique. Dans le cadre de mon doctorat, nous avons étudié des approches d'apprentissage automatique pour exploiter les données radiologiques, transcriptomiques et pathologiques, en les intégrant dans des modèles multimodaux susceptibles d'améliorer le pouvoir prédictif limité des données de routine clinique.Mon doctorat était au cœur d'un projet multidisciplinaire financé par la Fondation ARC, intitulé "SIGN'IT 2020-Signatures en Immunothérapie". Il réunissait plusieurs équipes de recherche de l'Institut Curie aux côtés d'une équipe de l'Institut du thorax, dirigée par le Professeur Nicolas Girard, en charge de la prise en charge des patients et de la collecte des données. Nous avons constitué une nouvelle cohorte multimodale de 317 patients atteints de CPNPC métastatique traités, en première ligne, par immunothérapie, seule ou associée à une chimiothérapie. Avant le début du traitement, nous avons recueilli des informations cliniques provenant des soins de routine, des examens TEP/TDM au 18F-FDG, des lames pathologiques numérisées provenant du diagnostic initial et des profils RNA-seq provenant de biopsies solides. Les résultats de l'immunothérapie ont été évalués en fonction de la survie globale (OS) et de la survie sans progression (PFS) de chaque patient.En collaboration avec Irène Buvat et Emmanuel Barillot, dont les équipes sont respectivement spécialisées dans l'analyse d'images médicales et de profils tumoraux RNA-seq, nous nous sommes d'abord concentrés sur la conception d'outils informatiques permettant d'extraire des informations pertinentes et interprétables à partir de ces deux modalités de données. Nous avons notamment développé un outil Python pour appliquer l'Analyse en Composantes Indépendantes (ICA) sur les données omiques et stabiliser les résultats à travers de multiples exécutions. Nous avons ensuite exploré le potentiel de l'ICA stabilisée pour extraire des caractéristiques transcriptomiques puissantes et biologiquement pertinentes pour la prédiction des résultats des patients. Pour les images médicales, et en particulier les examens TEP au 18F-FDG, nous avons étudié le potentiel des approches radiomiques pour caractériser la maladie métastatique au niveau du corps entier et concevoir de nouvelles caractéristiques prédictives. Nous avons conçu un outil d'explication Python, basé sur les valeurs de Shapley, pour mettre en évidence la contribution de chaque métastase individuelle à la prédiction des modèles radiomiques.Une part importante de mon doctorat a été consacrée à l'intégration des caractéristiques cliniques, radiomiques et transcriptomiques, ainsi que des caractéristiques pathomiques (avec l'aide de l'équipe de Thomas Walter). Nous avons procédé à une comparaison approfondie des capacités prédictives des différentes combinaisons multimodales en utilisant divers algorithmes d'apprentissage et méthodes d'intégration. Nous avons conçu des stratégies pour surmonter les nombreux défis associés à l'intégration multimodale, y compris la gestion des modalités manquantes pour de nombreux patients, la gestion d'une taille de cohorte modeste par rapport à la haute dimensionnalité des données, ou la garantie d'une comparaison équitable de toutes les combinaisons multimodales possibles. Nous nous sommes particulièrement attachés à mettre en évidence le potentiel des approches multimodales pour améliorer la stratification des risques des patients par rapport aux modèles utilisant uniquement des informations de routine cliniqueOverall survival of patients with metastatic non-small cell lung cancer (NSCLC) has been increasing with the use of anti-PD-1 immune checkpoint inhibitors. However, the duration of response remains highly variable between patients, and only 20-30% of patients are alive at 2 years. Thus, new biomarkers for predicting response to treatment and patient outcomes are still needed to guide therapeutic decision. In my PhD, we investigated machine learning approaches to leverage radiological, transcriptomic, and pathological data, integrating them into powerful multimodal models that might improve the limited predictive power of routine clinical data.My doctoral research stood at the heart of a multidisciplinary project funded by Fondation ARC call «SIGN’IT 2020—Signatures in Immunotherapy». It brought together several research teams of Institut Curie alongside a team from Institut du thorax, led by Professor Nicolas Girard, in charge of patient management and data collection. We built a new multimodal cohort of 317 metastatic NSCLC patients treated with first-line immunotherapy alone or combined with chemotherapy. At baseline, we collected clinical information from routine care, 18F-FDG PET/CT scans, digitized pathological slides from the initial diagnosis, and bulk RNA-seq profiles from solid biopsies. Immunotherapy outcome was monitored with Overall Survival (OS) and Progression-Free Survival (PFS).Together with Irène Buvat and Emmanuel Barillot, whose teams hold significant expertise in the analysis of medical images and RNAseq tumor profiles, respectively, we initially focused on designing computational tools to extract relevant and interpretable information from these two data modalities. We notably developed a Python tool to apply Independent Component Analysis (ICA) on omics data and stabilize the results through multiple runs. We then explored the potential of stabilized ICA to extract powerful and biologically relevant transcriptomic features for the prediction of patient outcome. For medical images, and in particular 18F-FDG PET scans, we investigated the potential of radiomic approaches to characterize the metastatic disease at the whole-body level and design novel predictive features. We designed a Python explanation tool, based on Shapley values, to highlight the contribution of each individual metastasis to the prediction of radiomic models that use as input such whole-body features. A substantial portion of my PhD was devoted to the integration of clinical, radiomic, and transcriptomic features, as well as pathomic features extracted from digitized pathological slides (with the assistance of Thomas Walter’s team). We conducted a thorough comparison of the predictive capabilities of the different multimodal combinations using various state-of-the-art learning algorithms and integration methods. We devised strategies to overcome the many challenges associated to multimodal integration within our dataset, including handling missing modalities for numerous patients, dealing with a modest cohort size in comparison to the high dimensionality of the data, or ensuring a fair comparison of all the possible multimodal combinations. We especially focused on highlighting the potential of multimodal approaches to enhance patient risk stratification with respect to models using only clinical information collected during routine care
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Schmidt, Florian [Verfasser], und Marcel Holger [Akademischer Betreuer] Schulz. „Applications, challenges and new perspectives on the analysis of transcriptional regulation using epigenomic and transcriptomic data / Florian Schmidt ; Betreuer: Marcel Holger Schulz“. Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2019. http://d-nb.info/1196090173/34.
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Schmidt, Florian Verfasser], und Marcel Holger [Akademischer Betreuer] [Schulz. „Applications, challenges and new perspectives on the analysis of transcriptional regulation using epigenomic and transcriptomic data / Florian Schmidt ; Betreuer: Marcel Holger Schulz“. Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2019. http://nbn-resolving.de/urn:nbn:de:bsz:291--ds-287773.
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Czerwińska, Urszula. „Unsupervised deconvolution of bulk omics profiles : methodology and application to characterize the immune landscape in tumors Determining the optimal number of independent components for reproducible transcriptomic data analysis Application of independent component analysis to tumor transcriptomes reveals specific and reproducible immune-related signals A multiscale signalling network map of innate immune response in cancer reveals signatures of cell heterogeneity and functional polarization“. Thesis, Sorbonne Paris Cité, 2018. http://www.theses.fr/2018USPCB075.
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Les tumeurs sont entourées d'un microenvironnement complexe comprenant des cellules tumorales, des fibroblastes et une diversité de cellules immunitaires. Avec le développement actuel des immunothérapies, la compréhension de la composition du microenvironnement tumoral est d'une importance critique pour effectuer un pronostic sur la progression tumorale et sa réponse au traitement. Cependant, nous manquons d'approches quantitatives fiables et validées pour caractériser le microenvironnement tumoral, facilitant ainsi le choix de la meilleure thérapie. Une partie de ce défi consiste à quantifier la composition cellulaire d'un échantillon tumoral (appelé problème de déconvolution dans ce contexte), en utilisant son profil omique de masse (le profil quantitatif global de certains types de molécules, tels que l'ARNm ou les marqueurs épigénétiques). La plupart des méthodes existantes utilisent des signatures prédéfinies de types cellulaires et ensuite extrapolent cette information à des nouveaux contextes. Cela peut introduire un biais dans la quantification de microenvironnement tumoral dans les situations où le contexte étudié est significativement différent de la référence. Sous certaines conditions, il est possible de séparer des mélanges de signaux complexes, en utilisant des méthodes de séparation de sources et de réduction des dimensions, sans définitions de sources préexistantes. Si une telle approche (déconvolution non supervisée) peut être appliquée à des profils omiques de masse de tumeurs, cela permettrait d'éviter les biais contextuels mentionnés précédemment et fournirait un aperçu des signatures cellulaires spécifiques au contexte. Dans ce travail, j'ai développé une nouvelle méthode appelée DeconICA (Déconvolution de données omiques de masse par l'analyse en composantes immunitaires), basée sur la méthodologie de séparation aveugle de source. DeconICA a pour but l'interprétation et la quantification des signaux biologiques, façonnant les profils omiques d'échantillons tumoraux ou de tissus normaux, en mettant l'accent sur les signaux liés au système immunitaire et la découverte de nouvelles signatures. Afin de rendre mon travail plus accessible, j'ai implémenté la méthode DeconICA en tant que librairie R. En appliquant ce logiciel aux jeux de données de référence, j'ai démontré qu'il est possible de quantifier les cellules immunitaires avec une précision comparable aux méthodes de pointe publiées, sans définir a priori des gènes spécifiques au type cellulaire. DeconICA peut fonctionner avec des techniques de factorisation matricielle telles que l'analyse indépendante des composants (ICA) ou la factorisation matricielle non négative (NMF). Enfin, j'ai appliqué DeconICA à un grand volume de données : plus de 100 jeux de données, contenant au total plus de 28 000 échantillons de 40 types de tumeurs, générés par différentes technologies et traités indépendamment. Cette analyse a démontré que les signaux immunitaires basés sur l'ICA sont reproductibles entre les différents jeux de données. D'autre part, nous avons montré que les trois principaux types de cellules immunitaires, à savoir les lymphocytes T, les lymphocytes B et les cellules myéloïdes, peuvent y être identifiés et quantifiés. Enfin, les métagènes dérivés de l'ICA, c'est-à-dire les valeurs de projection associées à une source, ont été utilisés comme des signatures spécifiques permettant d'étudier les caractéristiques des cellules immunitaires dans différents types de tumeurs. L'analyse a révélé une grande diversité de phénotypes cellulaires identifiés ainsi que la plasticité des cellules immunitaires, qu'elle soit dépendante ou indépendante du type de tumeur. Ces résultats pourraient être utilisés pour identifier des cibles médicamenteuses ou des biomarqueurs pour l'immunothérapie du cancerTumors are engulfed in a complex microenvironment (TME) including tumor cells, fibroblasts, and a diversity of immune cells. Currently, a new generation of cancer therapies based on modulation of the immune system response is in active clinical development with first promising results. Therefore, understanding the composition of TME in each tumor case is critically important to make a prognosis on the tumor progression and its response to treatment. However, we lack reliable and validated quantitative approaches to characterize the TME in order to facilitate the choice of the best existing therapy. One part of this challenge is to be able to quantify the cellular composition of a tumor sample (called deconvolution problem in this context), using its bulk omics profile (global quantitative profiling of certain types of molecules, such as mRNA or epigenetic markers). In recent years, there was a remarkable explosion in the number of methods approaching this problem in several different ways. Most of them use pre-defined molecular signatures of specific cell types and extrapolate this information to previously unseen contexts. This can bias the TME quantification in those situations where the context under study is significantly different from the reference. In theory, under certain assumptions, it is possible to separate complex signal mixtures, using classical and advanced methods of source separation and dimension reduction, without pre-existing source definitions. If such an approach (unsupervised deconvolution) is feasible to apply for bulk omic profiles of tumor samples, then this would make it possible to avoid the above mentioned contextual biases and provide insights into the context-specific signatures of cell types. In this work, I developed a new method called DeconICA (Deconvolution of bulk omics datasets through Immune Component Analysis), based on the blind source separation methodology. DeconICA has an aim to decipher and quantify the biological signals shaping omics profiles of tumor samples or normal tissues. A particular focus of my study was on the immune system-related signals and discovering new signatures of immune cell types. In order to make my work more accessible, I implemented the DeconICA method as an R package named "DeconICA". By applying this software to the standard benchmark datasets, I demonstrated that DeconICA is able to quantify immune cells with accuracy comparable to published state-of-the-art methods but without a priori defining a cell type-specific signature genes. The implementation can work with existing deconvolution methods based on matrix factorization techniques such as Independent Component Analysis (ICA) or Non-Negative Matrix Factorization (NMF). Finally, I applied DeconICA to a big corpus of data containing more than 100 transcriptomic datasets composed of, in total, over 28000 samples of 40 tumor types generated by different technologies and processed independently. This analysis demonstrated that ICA-based immune signals are reproducible between datasets and three major immune cell types: T-cells, B-cells and Myeloid cells can be reliably identified and quantified. Additionally, I used the ICA-derived metagenes as context-specific signatures in order to study the characteristics of immune cells in different tumor types. The analysis revealed a large diversity and plasticity of immune cells dependent and independent on tumor type. Some conclusions of the study can be helpful in identification of new drug targets or biomarkers for immunotherapy of cancer
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Owen, Anne M. „Widescale analysis of transcriptomics data using cloud computing methods“. Thesis, University of Essex, 2016. http://repository.essex.ac.uk/16125/.
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This study explores the handling and analyzing of big data in the field of bioinformatics. The focus has been on improving the analysis of public domain data for Affymetrix GeneChips which are a widely used technology for measuring gene expression. Methods to determine the bias in gene expression due to G-stacks associated with runs of guanine in probes have been explored via the use of a grid and various types of cloud computing. An attempt has been made to find the best way of storing and analyzing big data used in bioinformatics. A grid and various types of cloud computing have been employed. The experience gained in using a grid and different clouds has been reported. In the case of Windows Azure, a public cloud has been employed in a new way to demonstrate the use of the R statistical language for research in bioinformatics. This work has studied the G-stack bias in a broad range of GeneChip data from public repositories. A wide scale survey has been carried out to determine the extent of the Gstack bias in four different chips across three different species. The study commenced with the human GeneChip HG U133A. A second human GeneChip HG U133 Plus2 was then examined, followed by a plant chip, Arabidopsis thaliana, and then a bacterium chip, Pseudomonas aeruginosa. Comparisons have also been made between the use of widely recognised algorithms RMA and PLIER for the normalization stage of extracting gene expression from GeneChip data.
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Hernandez-Ferrer, Carles 1987. „Bioinformatic tools for exposome data analysis : application to human molecular signatures of ultraviolet light effects“. Doctoral thesis, Universitat Pompeu Fabra, 2017. http://hdl.handle.net/10803/572046.
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Las enfermedades complejas se encuentran entre las más comunes y son causadas por una combinación de factores genéticos y ambientales (contaminación ambiental, estilo de vida, etc). Entre las enfermedades complejas que se pueden destacar se encuentran la obesidad, el asma, la hipertensión o la diabetes. Diversos estudios científicos sugieren que el hecho de padecer enfermedades complejas está condicionado a la aparición o acumulación de determinados factores ambientales. Asimismo, se ha descrito que los factores ambientales son unos de los principales contribuyentes a la carga mundial de morbilidad. Todo esto nos lleva a definir el término exposoma como el conjunto de factores ambientales a los que un individuo se ve expuesto desde la concepción hasta la muerte. El estudio de la mecánica subyacente que vincula el exposoma con la salud es un campo de investigación emergente con un fuerte potencial para proporcionar nuevos conocimientos sobre la etiología de las enfermedades.
La primera parte de esta tesis se centra en la exposición a la radiación ultravioleta. La exposición a la radiación ultravioleta proviene de fuentes tanto naturales como artificiales. La radiación ultravioleta incluye tres subtipos de radiación según su longitud de onda (UVA 315-400 nm, UVB 315-295 nm y UVC 295-200 nm). Si bien la principal fuente natural de radiación ultravioleta es el Sol, la UVC no llega a la superficie de la Tierra debido a su absorción por la capa estratosférica de ozono. En consecuencia, la exposición a radiación ultravioleta a la que estamos usualmente sometidos consisten en una mezcla de UVA (95 %) y UVB (5 %). Los efectos de la radiación ultravioleta en humanos pueden ser beneficiosos o perjudiciales dependiendo de su cantidad y forma. Los efectos perjudiciales y agudos de la radiación ultravioleta incluyen eritema, oscurecimiento del pigmento, retraso en el bronceado y engrosamiento de la epidermis. Repetidas lesiones en la piel producidas por radiación ultravioleta pueden predisponer, en última instancia, a efectos crónicos de fotoenvejecimiento, inmunosupresión y fotocarcinogénesis. El mayor efecto beneficioso de la radiación ultravioleta es la síntesis cutánea de la vitamina D. La vitamina D es necesaria para mantener el calcio fisiológico y del fósforo para la mineralización ósea y para prevenir el raquitismo, la osteomalacia y la osteoporosis. El paradigma del exposoma es trabajar con múltiples exposiciones a la vez en vez centrarse en una sola exposición. Este enfoque permite tener una visión más parecida a la realidad que vivimos. Luego, la segunda parte se centra en las herramientas para explorar cómo caracterizar y analizar el exposoma y cómo probar sus efectos en múltiples capas biológicas intermedias para proporcionar información sobre los mecanismos moleculares subyacentes que vinculan las exposiciones ambientales a los resultados de salud.Most common diseases are caused by a combination of genetic, environmental and lifestyle factors. These diseases are referred to as complex diseases. Examples of this type of diseases are obesity, asthma, hypertension or diabetes. Several empirical evidence suggest that exposures are necessary determinants of complex disease operating in a causal background of genetic diversity. Moreover, environmental factors have long been implicated as major contributors to the global disease burden. This leads to the formulation of the exposome, that contains any exposure to which an individual is subjected from conception to death. The study of the underlying mechanics that links the exposome with human health is an emerging research field with a strong potential to provide new insights into disease etiology. The first part of this thesis is focused on ultraviolet radiation (UVR) exposure. UVR exposure occurs from both natural and artificial sources. UVR includes three subtypes of radiation according to its wavelength (UVA 315-400 nm, UVB 315-295 nm, and UVC 295-200 nm). While the main natural source of UVR is the Sun, UVC radiation does not reach Earth's surface because of its absorption by the stratospheric ozone layer. Then, exposures to UVR typically consist of a mixture of UVA (95%) and UVB (5%). Effects of UVR on human can be both beneficial and detrimental, depending on the amount and form of UVR. Detrimental and acute effects of UVR include erythema, pigment darkening, delayed tanning and thickening of the epidermis. Repeated UVR-induced injury to the skin, may ultimately predispose one to the chronic effects photoaging, immunosuppression, and photocarcinogenesis. The beneficial effect of UVR is the cutaneous synthesis of vitamin D. Vitamin D is necessary to maintain physiologic calcium and phosphorous for normal bone mineralization and to prevent rickets, osteomalacia, and osteoporosis. But the exposome paradigm is to work with multiple exposures at a time and with one or more health outcomes rather focus in a single exposures analysis. This approach tends to be a more accurate snapshot of the reality that we live in complex environments. Then, the second part is focused on the tools to explore how to characterize and analyze the exposome and how to test its effects in multiple intermediate biological layers to provide insights into the underlying molecular mechanisms linking environmental exposures to health outcomes.
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Daub, Carsten O. „Analysis of integrated transcriptomics and metabolomics data a systems biology approach /“. [S.l. : s.n.], 2004. http://pub.ub.uni-potsdam.de/2004/0025/daub.pdf.
Der volle Inhalt der QuelleBücher zum Thema "Transcriptomic data analysis"
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Wang, Yejun, und Ming-an Sun, Hrsg. Transcriptome Data Analysis. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7710-9.
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Azad, Rajeev K., Hrsg. Transcriptome Data Analysis. New York, NY: Springer US, 2024. http://dx.doi.org/10.1007/978-1-0716-3886-6.
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author, Tuimala Jarno, Somervuo Panu author, Huss Mikael author und Wong Garry author, Hrsg. RNA-seq data analysis: A practical approach. Boca Raton: CRC Press, Taylor & Francis Group, 2015.
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Transcriptome Data Analysis: Methods and Protocols. Humana, 2018.
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Wang, Yejun, und Ming-an Sun. Transcriptome Data Analysis: Methods and Protocols. Springer New York, 2019.
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Gu, Xun. Statistical Analysis of Molecular and Genomic Evolution. Oxford University PressOxford, 2024. http://dx.doi.org/10.1093/oso/9780198816515.001.0001.
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Abstract This book introduces up-to-date methods in statistics and bioinformatics for the study of molecular and genome evolution. It first provides a concise overview of molecular evolutionary analysis and phylogenetic inference. The following chapters cover four research themes: evolution of protein functionality and functional divergence (Chapters 3 and 4); effective gene pleiotropy estimation under the genotype-phenotype mapping model of protein evolution (Chapter 5); evolution of genetic robustness after gene duplication (Chapter 6), and the statistical models of transcriptome evolution (Chapter 7 for phylogenetic transcriptome analysis, Chapter 8 for ancestral transcriptome inference along a phylogeny, and Chapter 9 for Bayesian estimation of selection strength imposed on transcriptome evolution). The book focuses on how the underlying evolutionary mechanisms can be reasonably modeled so that they can be statistically tested by the current high throughput data. Meanwhile, the book avoids the cumbersome description of technical procedures for specific data types such as normalization or bias correction. The author believes that this book will help new-generation researchers to advance this research field as more data of much higher quality are available.
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Tuimala, Jarno, Eija Korpelainen, Panu Somervuo, Mikael Huss und Garry Wong. RNA-Seq Data Analysis: A Practical Approach. Taylor & Francis Group, 2014.
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Tuimala, Jarno, Eija Korpelainen, Panu Somervuo, Mikael Huss und Garry Wong. RNA-Seq Data Analysis: A Practical Approach. Taylor & Francis Group, 2014.
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Tuimala, Jarno, Eija Korpelainen, Panu Somervuo, Mikael Huss und Garry Wong. RNA-Seq Data Analysis: A Practical Approach. Taylor & Francis Group, 2014.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Transcriptomic data analysis"
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Purdom, Elizabeth, und Sach Mukherjee. „Transcriptomic Technologies and Statistical Data Analysis“. In Handbook of Statistical Systems Biology, 133–62. Chichester, UK: John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9781119970606.ch7.
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Cassese, Alberto, Michele Guindani und Marina Vannucci. „iBATCGH: Integrative Bayesian Analysis of Transcriptomic and CGH Data“. In Statistical Analysis for High-Dimensional Data, 105–23. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27099-9_6.
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Marshall, Wallace F. „Use of Transcriptomic Data to Support Organelle Proteomic Analysis“. In Organelle Proteomics, 403–14. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-59745-028-7_27.
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Thomas, Russell S., Longlong Yang, Harvey J. Clewell und Melvin E. Andersen. „Analysis of Transcriptomic Dose-Response Data for Toxicology and Risk Assessment“. In Applications of Toxicogenomics in Safety Evaluation and Risk Assessment, 237–50. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118001042.ch11.
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Kushwaha, Swarnima, Sudeshna Mukherjee, Rajdeep Chowdhury und Shibasish Chowdhury. „Analysis of Transcriptomic Data Generated from Drug-Treated Cancer Cell Line“. In Methods in Molecular Biology, 119–29. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2513-2_10.
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Zhang, Shilu, Sara Knaack und Sushmita Roy. „Enabling Studies of Genome-Scale Regulatory Network Evolution in Large Phylogenies with MRTLE“. In Methods in Molecular Biology, 439–55. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2257-5_24.
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AbstractTranscriptional regulatory networks specify context-specific patterns of genes and play a central role in how species evolve and adapt. Inferring genome-scale regulatory networks in non-model species is the first step for examining patterns of conservation and divergence of regulatory networks. Transcriptomic data obtained under varying environmental stimuli in multiple species are becoming increasingly available, which can be used to infer regulatory networks. However, inference and analysis of multiple gene regulatory networks in a phylogenetic setting remains challenging. We developed an algorithm, Multi-species Regulatory neTwork LEarning (MRTLE), to facilitate such studies of regulatory network evolution. MRTLE is a probabilistic graphical model-based algorithm that uses phylogenetic structure, transcriptomic data for multiple species, and sequence-specific motifs in each species to simultaneously infer genome-scale regulatory networks across multiple species. We applied MRTLE to study regulatory network evolution across six ascomycete yeasts using transcriptomic measurements collected across different stress conditions. MRTLE networks recapitulated experimentally derived interactions in the model organism S. cerevisiae as well as non-model species, and it was more beneficial for network inference than methods that do not use phylogenetic information. We examined the regulatory networks across species and found that regulators associated with significant expression and network changes are involved in stress-related processes. MTRLE and its associated downstream analysis provide a scalable and principled framework to examine evolutionary dynamics of transcriptional regulatory networks across multiple species in a large phylogeny.
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Santos, Thiely Patricia Fabian Dos, Elodia Sánchez-Barrantes, Luiz Filipe Pereira und Andrés Gatica-Arias. „Transcriptomic Data Analysis Using the Galaxy Platform: Coffee (Coffea arabica L.) Flowers as Example“. In Methods in Molecular Biology, 225–43. New York, NY: Springer US, 2024. http://dx.doi.org/10.1007/978-1-0716-3778-4_15.
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Cellerino, Alessandro, und Michele Sanguanini. „RNA-seq raw data processing“. In Transcriptome Analysis, 27–44. Pisa: Scuola Normale Superiore, 2018. http://dx.doi.org/10.1007/978-88-7642-642-1_3.
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Cellerino, Alessandro, und Michele Sanguanini. „A primer on data distributions and their visualisation“. In Transcriptome Analysis, 1–10. Pisa: Scuola Normale Superiore, 2018. http://dx.doi.org/10.1007/978-88-7642-642-1_1.
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Andleeb, Tayyaba, James Milson und Philippa Borrill. „The Wheat Transcriptome and Discovery of Functional Gene Networks“. In Compendium of Plant Genomes, 75–92. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-38294-9_5.
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AbstractGene expression patterns have been a widely applied source of information to start understanding gene function in multiple plant species. In wheat, the advent of increasingly accurate and complete gene annotations now enables transcriptomic studies to be carried out on a routine basis and studies by groups around the world have compared gene expression changes under an array of environmental and developmental stages. However, associating data from differentially expressed genes to understanding the biological role of these genes and their applications for breeding is a major challenge. Recently, the first steps to apply network-based approaches to characterise gene expression have been taken in wheat and these networks have enabled the prediction of gene functions in wheat but only for a handful of traits. Combining advanced analysis methods with better sequencing technology will increase our capacity to place gene expression in wheat in the context of functions of genes that influence agronomically important traits.
Konferenzberichte zum Thema "Transcriptomic data analysis"
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Khokhar, Maham, Burcu Bakir-Gungor und Malik Yousef. „Enhancing the Efficiency of the Grouping-Scoring-Modeling Framework with Statistical Pre-Scoring Component for Transcriptomic Data Analysis“. In 16th International Conference on Bioinformatics Models, Methods and Algorithms, 479–88. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013192600003911.
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Shuai, Jin, Li Yaoyu und Peng Jiawu. „Building Machine Learning Models on Limited Transcriptomic RNA-Seq Data“. In 2024 10th International Conference on Big Data and Information Analytics (BigDIA), 358–63. IEEE, 2024. https://doi.org/10.1109/bigdia63733.2024.10808560.
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Fang, Donghai, Fangfang Zhu und Wenwen Min. „Multi-Slice Spatial Transcriptomics Data Integration Analysis with STG3Net“. In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 509–14. IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822331.
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Langston, Michael A., Andy D. Perkins, Arnold M. Saxton, Jon A. Scharff und Brynn H. Voy. „Innovative computational methods for transcriptomic data analysis“. In the 2006 ACM symposium. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1141277.1141319.
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„Ontology analysis of big transcriptomic data and differential gene expression“. In Биоинформатика регуляции и структуры геномов / системная биология. ИЦиГ СО РАН, 2024. http://dx.doi.org/10.18699/bgrs2024-1.4-24.
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Mazur, O. E., I. A. Kutyrev, T. V. Sidorova, L. V. Sukhanova, N. B. Terenina und S. O. Movsesyan. „TRANSCRIPTOME ANALYSIS OF THE SPLEEN OF THE BAIKAL CISCO (LAKE BAIKAL, EASTERN SIBERIA)“. In THEORY AND PRACTICE OF PARASITIC DISEASE CONTROL. VNIIP – FSC VIEV, 2024. http://dx.doi.org/10.31016/978-5-6050437-8-2.2024.25.251-255.
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For the first time, new data have been presented on the spleen transcriptome of the Baikal cisco Coregonus migratorius Georgi, 1775 (Salmoniformes: Coregonidae), infected with parasites of different systematic groups. Transcriptomic libraries were sequenced on an Illumina NextSeq550 sequencer using the NextSeq® 550 High Output Kit v2. The de-novo transcriptome was assembled. Conserved domains and their associated Gene Ontology annotations were predicted with Blast2Go. The annotation results of the obtained transcripts found that transcripts were distributed in the spleen into the following categories: molecular functions, biological processes, and cellular components. Among the molecular functions, transcripts of enzyme binding (25.8%), transferase activity (24.7%), hydrolase activity (24.4%), catalytic activity affecting proteins (22.2%), and DNA binding (21%) predominated. Biological processes were dominated by transcripts of cellular processes (46.7%), metabolic processes (38.6%), biological regulation (38.2%), and regulation components of biological processes (36.6%). The category of cellular GO components identified terms, namely cytoplasmic vesicles (25.9%), cytoplasmic membranes (23.1%), nucleoplasm (22.1%), cytoskeletal part (18.6%), cytosol and cellular compounds (17%) in a greater extent.
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Podpecan, Vid, Dragana Miljkovic, Marko Petek, Tjasa Stare, Kristina Gruden, Igor Mozetic und Nada Lavrac. „Integrating semantic transcriptomic data analysis and knowledge extraction from biological literature“. In 2013 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2013. http://dx.doi.org/10.1109/bibm.2013.6732540.
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„792 BGRS/SB-2022 Phylostratigraphic analysis of human cancers transcriptomic data“. In Bioinformatics of Genome Regulation and Structure/Systems Biology (BGRS/SB-2022) :. Institute of Cytology and Genetics, the Siberian Branch of the Russian Academy of Sciences, 2022. http://dx.doi.org/10.18699/sbb-2022-457.
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Lavorato-Rocha, André M., Beatriz de Melo Maia, Iara S. Rodrigues, Fabio A. Marchi, Gabriel R. Fernandes, Glauco Baiocchi, Fernando A. Soares, Silvia R. Rogatto, Yukie Sato-Kuwabara und Rafael M. Rocha. „Abstract 3427: Uncovering vulvar cancer: Integrated analysis of genomic and transcriptomic data“. In Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA. American Association for Cancer Research, 2014. http://dx.doi.org/10.1158/1538-7445.am2014-3427.
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Jackson, N., S. Sajuthi, C. Rios, M. T. Montgomery, J. L. Everman, A. C. Y. Mak, C. Eng et al. „Machine Learning Analysis of Airway Transcriptomic Data Identifies Distinct Childhood Asthma Endotypes“. 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.a1151.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Transcriptomic data analysis"
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Westwood, James H., Yaakov Tadmor und Hanan Eizenberg. Identifying the genes involved in host root perception by root parasitic weeds: Genetic and transcriptomic analysis of Orobanche hybrids differing in signal response specificity. United States Department of Agriculture, Januar 2013. http://dx.doi.org/10.32747/2013.7598145.bard.
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Seeds of the root parasitic plants of the genus Orobanchegerminate specifically in response to host-derived germination signals, which enables parasites to detect and attack preferred hosts. The best characterized class of germination stimulants is the strigolactones (SL), although some species respond to sesquiterpene lactones such as dehydrocostuslactone (DCL). Despite great progress in characterizing the SL signaling system in plants, the mechanism(s) by which parasite species detect specific compounds remains poorly understood. The goal of our project was to identify and characterize the genes responsible for stimulant specificity in O. cernuaand O. cumana. These two species are closely related, but differ in host range, with O. cernuaparasitizingSolanaceous crops such as tomato (and responding to SLs), and O. cumanaspecifically parasitizing sunflower (and responding to DCL). We used a genetic approach based on O. cernuax O. cumanahybrids to associate germination response with genes. We found that these parasite species each have multiple copies of KAI2d genes, which function in SL perception. In O. cernua, the OrceKAI2d2 responds to SL stimulants and is most consistently associated with hybrid lines that respond to SLs. For O. cumana, an apparently linked block of KAI2d genes was associated with response to DCL in hybrid lines, but we found no strong evidence that any of the OrcuKAI2d genes specifically recognize the DCL stimulant. Remarkably, one O. cumanagene, OrcuKAI2d5, responds to certain SLs in a genetic complementation assay, even though hybrid lines containing this gene show fidelity to DCL. In summary, we have identified the SL receptor in O. cernua, but the DCL receptor in O. cumanaremains unknown. Our data point to involvement of additional genes and yet greater levels of complexity regulating germination specificity in Orobanche. BARD Report - Project 4616 Page 2 of 8
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Aharoni, Asaph, Zhangjun Fei, Efraim Lewinsohn, Arthur Schaffer und Yaakov Tadmor. System Approach to Understanding the Metabolic Diversity in Melon. United States Department of Agriculture, Juli 2013. http://dx.doi.org/10.32747/2013.7593400.bard.
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Fruit quality is determined by numerous genetic factors that affect taste, aroma, color, texture, nutritional value and shelf life. To unravel the genetic components involved in the metabolic pathways behind these traits, the major goal of the project was to identify novel genes that are involved in, or that regulate, these pathways using correlation analysis between genotype, metabolite and gene expression data. The original and specific research objectives were: (1) Collection of replicated fruit from a population of 96 RI lines derived from parents distinguished by great diversity in fruit development and quality phenotypes, (2) Phenotypic and metabolic profiling of mature fruit from all 96 RI lines and their parents, (3) 454 pyrosequencing of cDNA representing mRNA of mature fruit from each line to facilitate gene expression analysis based on relative EST abundance, (4) Development of a database modeled after an existing database developed for tomato introgression lines (ILs) to facilitate online data analysis by members of this project and by researchers around the world. The main functions of the database will be to store and present metabolite and gene expression data so that correlations can be drawn between variation in target traits or metabolites across the RI population members and variation in gene expression to identify candidate genes which may impact phenotypic and chemical traits of interest, (5) Selection of RI lines for segregation and/or hybridization (crosses) analysis to ascertain whether or not genes associated with traits through gene expression/metabolite correlation analysis are indeed contributors to said traits. The overall research strategy was to utilize an available recombinant inbred population of melon (Cucumis melo L.) derived from phenotypically diverse parents and for which over 800 molecular markers have been mapped for the association of metabolic trait and gene expression QTLs. Transcriptomic data were obtained by high throughput sequencing using the Illumina platform instead of the originally planned 454 platform. The change was due to the fast advancement and proven advantages of the Illumina platform, as explained in the first annual scientific report. Metabolic data were collected using both targeted (sugars, organic acids, carotenoids) and non-targeted metabolomics analysis methodologies. Genes whose expression patterns were associated with variation of particular metabolites or fruit quality traits represent candidates for the molecular mechanisms that underlie them. Candidate genes that may encode enzymes catalyzingbiosynthetic steps in the production of volatile compounds of interest, downstream catabolic processes of aromatic amino acids and regulatory genes were selected and are in the process of functional analyses. Several of these are genes represent unanticipated effectors of compound accumulation that could not be identified using traditional approaches. According to the original plan, the Cucurbit Genomics Network (http://www.icugi.org/), developed through an earlier BARD project (IS-3333-02), was expanded to serve as a public portal for the extensive metabolomics and transcriptomic data resulting from the current project. Importantly, this database was also expanded to include genomic and metabolomic resources of all the cucurbit crops, including genomes of cucumber and watermelon, EST collections, genetic maps, metabolite data and additional information. In addition, the database provides tools enabling researchers to identify genes, the expression patterns of which correlate with traits of interest. The project has significantly expanded the existing EST resource for melon and provides new molecular tools for marker-assisted selection. This information will be opened to the public by the end of 2013, upon the first publication describing the transcriptomic and metabolomics resources developed through the project. In addition, well-characterized RI lines are available to enable targeted breeding for genes of interest. Segregation of the RI lines for specific metabolites of interest has been shown, demonstrating the utility in these lines and our new molecular and metabolic data as a basis for selection targeting specific flavor, quality, nutritional and/or defensive compounds. To summarize, all the specific goals of the project have been achieved and in many cases exceeded. Large scale trascriptomic and metabolomic resources have been developed for melon and will soon become available to the community. The usefulness of these has been validated. A number of novel genes involved in fruit ripening have been selected and are currently being functionally analyzed. We thus fully addressed our obligations to the project. In our view, however, the potential value of the project outcomes as ultimately manifested may be far greater than originally anticipated. The resources developed and expanded under this project, and the tools created for using them will enable us, and others, to continue to employ resulting data and discoveries in future studies with benefits both in basic and applied agricultural - scientific research.
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Ron, Eliora, und Eugene Eugene Nester. Global functional genomics of plant cell transformation by agrobacterium. United States Department of Agriculture, März 2009. http://dx.doi.org/10.32747/2009.7695860.bard.
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The aim of this study was to carry out a global functional genomics analysis of plant cell transformation by Agrobacterium in order to define and characterize the physiology of Agrobacterium in the acidic environment of a wounded plant. We planed to study the proteome and transcriptome of Agrobacterium in response to a change in pH, from 7.2 to 5.5 and identify genes and circuits directly involved in this change. Bacteria-plant interactions involve a large number of global regulatory systems, which are essential for protection against new stressful conditions. The interaction of bacteria with their hosts has been previously studied by genetic-physiological methods. We wanted to make use of the new capabilities to study these interactions on a global scale, using transcription analysis (transcriptomics, microarrays) and proteomics (2D gel electrophoresis and mass spectrometry). The results provided extensive data on the functional genomics under conditions that partially mimic plant infection and – in addition - revealed some surprising and significant data. Thus, we identified the genes whose expression is modulated when Agrobacterium is grown under the acidic conditions found in the rhizosphere (pH 5.5), an essential environmental factor in Agrobacterium – plant interactions essential for induction of the virulence program by plant signal molecules. Among the 45 genes whose expression was significantly elevated, of special interest is the two-component chromosomally encoded system, ChvG/I which is involved in regulating acid inducible genes. A second exciting system under acid and ChvG/Icontrol is a secretion system for proteins, T6SS, encoded by 14 genes which appears to be important for Rhizobium leguminosarum nodule formation and nitrogen fixation and for virulence of Agrobacterium. The proteome analysis revealed that gamma aminobutyric acid (GABA), a metabolite secreted by wounded plants, induces the synthesis of an Agrobacterium lactonase which degrades the quorum sensing signal, N-acyl homoserine lactone (AHL), resulting in attenuation of virulence. In addition, through a transcriptomic analysis of Agrobacterium growing at the pH of the rhizosphere (pH=5.5), we demonstrated that salicylic acid (SA) a well-studied plant signal molecule important in plant defense, attenuates Agrobacterium virulence in two distinct ways - by down regulating the synthesis of the virulence (vir) genes required for the processing and transfer of the T-DNA and by inducing the same lactonase, which in turn degrades the AHL. Thus, GABA and SA with different molecular structures, induce the expression of these same genes. The identification of genes whose expression is modulated by conditions that mimic plant infection, as well as the identification of regulatory molecules that help control the early stages of infection, advance our understanding of this complex bacterial-plant interaction and has immediate potential applications to modify it. We expect that the data generated by our research will be used to develop novel strategies for the control of crown gall disease. Moreover, these results will also provide the basis for future biotechnological approaches that will use genetic manipulations to improve bacterial-plant interactions, leading to more efficient DNA transfer to recalcitrant plants and robust symbiosis. These advances will, in turn, contribute to plant protection by introducing genes for resistance against other bacteria, pests and environmental stress.
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Katzir, Nurit, James Giovannoni, Marla Binzel, Efraim Lewinsohn, Joseph Burger und Arthur Schaffer. Genomic Approach to the Improvement of Fruit Quality in Melon (Cucumis melo) and Related Cucurbit Crops II: Functional Genomics. United States Department of Agriculture, Januar 2010. http://dx.doi.org/10.32747/2010.7592123.bard.
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Background: Genomics tools for enhancement of melon research, with an emphasis on fruit, were developed through a previous BARD project of the PIs (IS -333-02). These included the first public melon EST collection, a database to relay this information to the research community and a publicly available microarray. The current project (IS-3877- 06) aimed to apply these tools for identification of important genes for improvement of melon (Cucumis melo) fruit quality. Specifically, the research plans included expression analysis using the microarray and functional analyses of selected genes. The original project objectives, as they appeared in the approved project, were: Objective 1: Utilization of a public melon microarray developed under the existing project to characterize melon transcriptome activity during the ripening of normal melon fruit (cv. Galia) in order to provide a basis for both a general view of melon transcriptome activity during ripening and for comparison with existing transcriptome data of developing tomato and pepper fruit. Objective 2: Utilization of the same public melon microarray to characterize melon transcriptome activity in lines available in the collection of the Israeli group, focusing on sugar, organic acids and aroma metabolism, so as to identify potentially useful candidates for functional analysis and possible manipulation, through comparison with the general fruit development profile resulting from (1) above. Objective 3: Expansion of our existing melon EST database to include publicly available gene expression data and query tools, as the US group has done with tomato. Objective 4: Selection of 6-8 candidate genes for functional analysis and development of DNA constructs for repression or over-expression. Objective 5: Creation of transgenic melon lines, or transgenic heterologous systems (e.g. E. coli or tomato), to assess putative functions and potential as tools for molecular enhancement of melon fruit quality, using the candidate gene constructs from (4).
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Lers, Amnon, Majid R. Foolad und Haya Friedman. genetic basis for postharvest chilling tolerance in tomato fruit. United States Department of Agriculture, Januar 2014. http://dx.doi.org/10.32747/2014.7600014.bard.
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ABSTRACT Postharvest losses of fresh produce are estimated globally to be around 30%. Reducing these losses is considered a major solution to ensure global food security. Storage at low temperatures is an efficient practice to prolong postharvest performance of crops with minimal negative impact on produce quality or human health and the environment. However, many fresh produce commodities are susceptible to chilling temperatures, and the application of cold storage is limited as it would cause physiological chilling injury (CI) leading to reduced produce quality. Further, the primary CI becomes a preferred site for pathogens leading to decay and massive produce losses. Thus, chilling sensitive crops should be stored at higher minimal temperatures, which curtails their marketing life and in some cases necessitates the use of other storage strategies. Development of new knowledge about the biological basis for chilling tolerance in fruits and vegetables should allow development of both new varieties more tolerant to cold, and more efficient postharvest storage treatments and storage conditions. In order to improve the agricultural performance of modern crop varieties, including tomato, there is great potential in introgression of marker-defined genomic regions from wild species onto the background of elite breeding lines. To exploit this potential for improving tomato fruit chilling tolerance during postharvest storage, we have used in this research a recombinant inbred line (RIL) population derived from a cross between the red-fruited tomato wild species SolanumpimpinellifoliumL. accession LA2093 and an advanced Solanum lycopersicumL. tomato breeding line NCEBR-1, developed in the laboratory of the US co-PI. The original specific objectives were: 1) Screening of RIL population resulting from the cross NCEBR1 X LA2093 for fruit chilling response during postharvest storage and estimation of its heritability; 2) Perform a transcriptopmic and bioinformatics analysis for the two parental lines following exposure to chilling storage. During the course of the project, we learned that we could measure greater differences in chilling responses among specific RILs compared to that observed between the two parental lines, and thus we decided not to perform transcriptomic analysis and instead invest our efforts more on characterization of the RILs. Performing the transcriptomic analysis for several RILs, which significantly differ in their chilling tolerance/sensitivity, at a later stage could result with more significant insights. The RIL population, (172 lines), was used in field experiment in which fruits were examined for chilling sensitivity by determining CI severity. Following the field experiments, including 4 harvest days and CI measurements, two extreme tails of the response distribution, each consisting of 11 RILs exhibiting either high sensitivity or tolerance to chilling stress, were identified and were further examined for chilling response in greenhouse experiments. Across the RILs, we found significant (P < 0.01) correlation between field and greenhouse grown plants in fruit CI. Two groups of 5 RILs, whose fruits exhibited reproducible chilling tolerant/sensitive phenotypes in both field and greenhouse experiments, were selected for further analyses. Numerous genetic, physiological, biochemical and molecular variations were investigated in response to postharvest chilling stress in the selected RILs. We confirmed the differential response of the parental lines of the RIL population to chilling stress, and examined the extent of variation in the RIL population in response to chilling treatment. We determined parameters which would be useful for further characterization of chilling response in the RIL population. These included chlorophyll fluorescence Fv/Fm, water loss, total non-enzymatic potential of antioxidant activity, ascorbate and proline content, and expression of LeCBF1 gene, known to be associated with cold acclimation. These parameters could be used in continuation studies for the identification and genetic mapping of loci contributing to chilling tolerance in this population, and identifying genetic markers associated with chilling tolerance in tomato. Once genetic markers associated with chilling tolerance are identified, the trait could be transferred to different genetic background via marker-assisted selection (MAS) and breeding. The collaborative research established in this program has resulted in new information and insights in this area of research and the collaboration will be continued to obtain further insights into the genetic, molecular biology and physiology of postharvest chilling tolerance in tomato fruit. The US Co-PI, developed the RIL population that was used for screening and measurement of the relevant chilling stress responses and conducted statistical analyses of the data. Because we were not able to grow the RIL population under field conditions in two successive generations, we could not estimate heritability of response to chilling temperatures. However, we plan to continue the research, grow the RIL progeny in the field again, and determine heritability of chilling tolerance in a near future. The IS and US investigators interacted regularly and plan to continue and expand on this study, since combing the expertise of the Co-PI in genetics and breeding with that of the PI in postharvest physiology and molecular biology will have great impact on this line of research, given the significant findings of this one-year feasibility project.
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Ghanim, Murad, Joe Cicero, Judith K. Brown und Henryk Czosnek. Dissection of Whitefly-geminivirus Interactions at the Transcriptomic, Proteomic and Cellular Levels. United States Department of Agriculture, Februar 2010. http://dx.doi.org/10.32747/2010.7592654.bard.
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Our project focuses on gene expression and proteomics of the whitefly Bemisia tabaci (Gennadius) species complex in relation to the internal anatomy and localization of expressed genes and virions in the whitefly vector, which poses a major constraint to vegetable and fiber production in Israel and the USA. While many biological parameters are known for begomovirus transmission, nothing is known about vector proteins involved in the specific interactions between begomoviruses and their whitefly vectors. Identifying such proteins is expected to lead to the design of novel control methods that interfere with whitefly-mediated begomovirus transmission. The project objectives were to: 1) Perform gene expression analyses using microarrays to study the response of whiteflies (B, Q and A biotypes) to the acquisition of begomoviruses (Tomato yellow leaf curl (TYLCV) and Squash leaf curl (SLCV). 2) Construct a whitefly proteome from whole whiteflies and dissected organs after begomovirus acquisition. 3) Validate gene expression by q-RTPCR and sub-cellular localization of candidate ESTs identified in microarray and proteomic analyses. 4) Verify functionality of candidate ESTs using an RNAi approach, and to link these datasets to overall functional whitefly anatomical studies. During the first and second years biological experiments with TYLCV and SLCV acquisition and transmission were completed to verify the suitable parameters for sample collection for microarray experiments. The parameters were generally found to be similar to previously published results by our groups and others. Samples from whole whiteflies and midguts of the B, A and Q biotypes that acquired TYLCV and SLCV were collected in both the US and Israel and hybridized to B. tabaci microarray. The data we analyzed, candidate genes that respond to both viruses in the three tested biotypes were identified and their expression that included quantitative real-time PCR and co-localization was verified for HSP70 by the Israeli group. In addition, experiments were undertaken to employ in situ hybridization to localize several candidate genes (in progress) using an oligonucleotide probe to the primary endosymbiont as a positive control. A proteome and corresponding transcriptome to enable more effective protein identification of adult whiteflies was constructed by the US group. Further validation of the transmission route of begomoviruses, mainly SLCV and the involvement of the digestive and salivary systems was investigated (Cicero and Brown). Due to time and budget constraints the RNAi-mediated silencing objective to verify gene function was not accomplished as anticipated. HSP70, a strong candidate protein that showed over-expression after TYLCV and SLCV acquisition and retention by B. tabaci, and co-localization with TYLCV in the midgut, was further studies. Besides this protein, our joint research resulted in the identification of many intriguing candidate genes and proteins that will be followed up by additional experiments during our future research. To identify these proteins it was necessary to increase the number and breadth of whitefly ESTs substantially and so whitefly cDNAs from various libraries made during the project were sequenced (Sanger, 454). As a result, the proteome annotation (ID) was far more successful than in the initial attempt to identify proteins using Uniprot or translated insect ESTs from public databases. The extent of homology shared by insects in different orders was surprisingly low, underscoring the imperative need for genome and transcriptome sequencing of homopteran insects. Having increased the number of EST from the original usable 5500 generated several years ago to >600,000 (this project+NCBI data mining), we have identified about one fifth of the whitefly proteome using these new resources. Also we have created a database that links all identified whitefly proteins to the PAVEdb-ESTs in the database, resulting in a useful dataset to which additional ESTS will be added. We are optimistic about the prospect of linking the proteome ID results to the transcriptome database to enable our own and other labs the opportunity to functionally annotate not only genes and proteins involved in our area of interest (whitefly mediated transmission) but for the plethora of other functionalities that will emerge from mining and functionally annotating other key genes and gene families in whitefly metabolism, development, among others. This joint grant has resulted in the identification of numerous candidate proteins involved in begomovirus transmission by B. tabaci. A next major step will be to capitalize on validated genes/proteins to develop approaches to interfere with the virus transmission.
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Gur, Amit, Edward Buckler, Joseph Burger, Yaakov Tadmor und Iftach Klapp. Characterization of genetic variation and yield heterosis in Cucumis melo. United States Department of Agriculture, Januar 2016. http://dx.doi.org/10.32747/2016.7600047.bard.
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Project objectives: 1) Characterization of variation for yield heterosis in melon using Half-Diallele (HDA) design. 2) Development and implementation of image-based yield phenotyping in melon. 3) Characterization of genetic, epigenetic and transcriptional variation across 25 founder lines and selected hybrids. The epigentic part of this objective was modified during the course of the project: instead of characterization of chromatin structure in a single melon line through genome-wide mapping of nucleosomes using MNase-seq approach, we took advantage of rapid advancements in single-molecule sequencing and shifted the focus to Nanoporelong-read sequencing of all 25 founder lines. This analysis provides invaluable information on genome-wide structural variation across our diversity 4) Integrated analyses and development of prediction models Agricultural heterosis relates to hybrids that outperform their inbred parents for yield. First generation (F1) hybrids are produced in many crop species and it is estimated that heterosis increases yield by 15-30% globally. Melon (Cucumismelo) is an economically important species of The Cucurbitaceae family and is among the most important fleshy fruits for fresh consumption Worldwide. The major goal of this project was to explore the patterns and magnitude of yield heterosis in melon and link it to whole genome sequence variation. A core subset of 25 diverse lines was selected from the Newe-Yaar melon diversity panel for whole-genome re-sequencing (WGS) and test-crosses, to produce structured half-diallele design of 300 F1 hybrids (MelHDA25). Yield variation was measured in replicated yield trials at the whole-plant and at the rootstock levels (through a common-scion grafted experiments), across the F1s and parental lines. As part of this project we also developed an algorithmic pipeline for detection and yield estimation of melons from aerial-images, towards future implementation of such high throughput, cost-effective method for remote yield evaluation in open-field melons. We found extensive, highly heritable root-derived yield variation across the diallele population that was characterized by prominent best-parent heterosis (BPH), where hybrids rootstocks outperformed their parents by 38% and 56 % under optimal irrigation and drought- stress, respectively. Through integration of the genotypic data (~4,000,000 SNPs) and yield analyses we show that root-derived hybrids yield is independent of parental genetic distance. However, we mapped novel root-derived yield QTLs through genome-wide association (GWA) analysis and a multi-QTLs model explained more than 45% of the hybrids yield variation, providing a potential route for marker-assisted hybrid rootstock breeding. Four selected hybrid rootstocks are further studied under multiple scion varieties and their validated positive effect on yield performance is now leading to ongoing evaluation of their commercial potential. On the genomic level, this project resulted in 3 layers of data: 1) whole-genome short-read Illumina sequencing (30X) of the 25 founder lines provided us with 25 genome alignments and high-density melon HapMap that is already shown to be an effective resource for QTL annotation and candidate gene analysis in melon. 2) fast advancements in long-read single-molecule sequencing allowed us to shift focus towards this technology and generate ~50X Nanoporesequencing of the 25 founders which in combination with the short-read data now enable de novo assembly of the 25 genomes that will soon lead to construction of the first melon pan-genome. 3) Transcriptomic (3' RNA-Seq) analysis of several selected hybrids and their parents provide preliminary information on differentially expressed genes that can be further used to explain the root-derived yield variation. Taken together, this project expanded our view on yield heterosis in melon with novel specific insights on root-derived yield heterosis. To our knowledge, thus far this is the largest systematic genetic analysis of rootstock effects on yield heterosis in cucurbits or any other crop plant, and our results are now translated into potential breeding applications. The genomic resources that were developed as part of this project are putting melon in the forefront of genomic research and will continue to be useful tool for the cucurbits community in years to come.
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Jander, Georg, und Daniel Chamovitz. Investigation of growth regulation by maize benzoxazinoid breakdown products. United States Department of Agriculture, Januar 2015. http://dx.doi.org/10.32747/2015.7600031.bard.
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Introduction Previous research had suggested that benzoxazinoids, a class of defensive metabolites found in maize, wheat, rye, and wild barley, are not only direct insect deterrents, but also influence other areas of plant metabolism. In particular, the benzoxazinoid 2,4-dihydroxy-7-methoxy-2H-1,4-benzoxa- zin-3(4H)- one (DIMBOA) was implicated in: (i) altering plant growth by interfering with auxin signaling, and (ii) leading to the induction of gene expression changes and secondary plant defense responses. The overall goal of this proposal was to identify mechanisms by which benzoxazinoids influence other aspects of plant growth and defense. Specifically, the following hypotheses were proposed to be tested as part of an approved BARD proposal: Benzoxazinoid breakdown products directly interfere with auxin perception Global changes in maize and barley gene expression are induced by benzoxazinoid activation. There is natural variation in the maize photomorphogenic response to benzoxazinoids. Although the initial proposal included experiments with both maize and barley, there were some technical difficulties with the proposed transgenic barley experiments and most of the experimental results were generated with maize. Summary of major findings Previous research by other labs, involving both maize and other plant species, had suggested that DIMBOA alters plant growth by interfering with auxin signaling. However, experiments conducted in both the Chamovitz and the Jander labs using Arabidopsis and maize, respectively, were unable to confirm previously published reports of exogenously added DIMBOA effects on auxin signaling. Nevertheless, analysis of bx1 and bx2 maize mutant lines, which have almost no detectable benzoxazinoids, showed altered responses to blue light signaling. Transcriptomic analysis of maize mutant lines, variation in inbred lines, and responses to exogenously added DIMBOA showed alteration in the transcription of a blue light receptor, which is required for plant growth responses. This finding provides a novel mechanistic explanation of the trade-off between growth and defense that is often observed in plants. Experiments by the Jander lab and others had demonstrated that DIMBOA not only has direct toxicity against insect pests and microbial pathogens, but also induces the formation of callose in both maize and wheat. In the current project, non-targeted metabolomic assays of wildtype maize and mutants with defects in benzoxazinoid biosynthesis were used to identify unrelated metabolites that are regulated in a benzoxazinoid-dependent manner. Further investigation identified a subset of these DIMBOA-responsive compounds as catechol, as well as its glycosylated and acetylated derivatives. Analysis of co-expression data identified indole-3-glycerol phosphate synthase (IGPS) as a possible regulator of benzoxazinoid biosynthesis in maize. In the current project, enzymatic activity of three predicted maize IGPS genes was confirmed by heterologous expression. Transposon knockout mutations confirmed the function of the maize genes in benzoxazinoid biosynthesis. Sub-cellular localization studies showed that the three maize IGPS proteins are co-localized in the plastids, together with BX1 and BX2, two previously known enzymes of the benzoxazinoid biosynthesis pathway. Implications Benzoxazinoids are among the most abundant and effective defensive metabolites in maize, wheat, and rye. Although there is considerable with-in species variation in benzoxazinoid content, very little is known about the regulation of this variation and the specific effects on plant growth and defense. The results of this research provide further insight into the complex functions of maize benzoxazinoids, which are not only toxic to pests and pathogens, but also regulate plant growth and other defense responses. Knowledge gained through the current project will make it possible to engineer benzoxazinoid biosynthesis in a more targeted manner to produce pest-tolerant crops without negative effects on growth and yield.
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Cohen, Yuval, Christopher A. Cullis und 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, Oktober 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.
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Harman, Gary E., und Ilan Chet. Enhancement of plant disease resistance and productivity through use of root symbiotic fungi. United States Department of Agriculture, Juli 2008. http://dx.doi.org/10.32747/2008.7695588.bard.
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The objectives of the project were to (a) compare effects ofT22 and T-203 on growth promotion and induced resistance of maize inbred line Mol7; (b) follow induced resistance of pathogenesis-related proteins through changes in gene expression with a root and foliar pathogen in the presence or absence of T22 or T-203 and (c) to follow changes in the proteome of Mol? over time in roots and leaves in the presence or absence of T22 or T-203. The research built changes in our concepts regarding the effects of Trichoderma on plants; we hypothesized that there would be major changes in the physiology of plants and these would be reflected in changes in the plant proteome as a consequence of root infection by Trichoderma spp. Further, Trichoderma spp. differ in their effects on plants and these changes are largely a consequence of the production of different elicitors of elicitor mixtures that are produced in the zone of communication that is established by root infection by Trichoderma spp. In this work, we demonstrated that both T22 and T-203 increase growth and induce resistance to pathogens in maize. In Israel, it was shown that a hydrophobin is critical for root colonization by Trichoderma strains, and that peptaibols and an expansin-like protein from Ttrichoderma probably act as elicitors of induced resistance in plants. Further, this fungus induces the jasmonate/ethylene pathway of disease resistance and a specific cucumber MAPK is required for transduction of the resistance signal. This is the first such gene known to be induced by fungal systems. In the USA, extensive proteomic analyses of maize demonstrated a number of proteins are differentially regulated by T. harzianum strain T22. The pattern of up-regulation strongly supports the contention that this fungus induces increases in plant disease resistance, respiratory rates and photosynthesis. These are all very consistent with the observations of effects of the fungus on plants in the greenhouse and field. In addition, the chitinolytic complex of maize was examined. The numbers of maize genes encoding these enzymes was increased about 3-fold and their locations on maize chromosomes determined by sequence identification in specific BAC libraries on the web. One of the chitinolytic enzymes was determined to be a heterodimer between a specific exochitinase and different endochitinases dependent upon tissue differences (shoot or root) and the presence or absence of T. harzianum. These heterodimers, which were discovered in this work, are very strongly antifungal, especially the one from shoots in the presence of the biocontrol fungus. Finally, RNA was isolated from plants at Cornell and sent to Israel for transcriptome assessment using Affymetrix chips (the chips became available for maize at the end of the project). The data was sent back to Cornell for bioinformatic analyses and found, in large sense, to be consistent with the proteomic data. The final assessment of this data is just now possible since the full annotation of the sequences in the maize Affy chips is just now available. This work is already being used to discover more effective strains of Trichoderma. It also is expected to elucidate how we may be able to manipulate and breed plants for greater disease resistance, enhanced growth and yield and similar goals. This will be possible since the changes in gene and protein expression that lead to better plant performance can be elucidated by following changes induced by Trichoderma strains. The work was in, some parts, collaborative but in others, most specifically transcriptome analyses, fully synergistic.