Academic literature on the topic 'Transcriptomic data management'
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Journal articles on the topic "Transcriptomic data management":
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Ortiz, Randy, Priyanka Gera, Christopher Rivera, and Juan C. Santos. "Pincho: A Modular Approach to High Quality De Novo Transcriptomics." Genes 12, no. 7 (June 22, 2021): 953. http://dx.doi.org/10.3390/genes12070953.
Abstract:
Transcriptomic reconstructions without reference (i.e., de novo) are common for data samples derived from non-model biological systems. These assemblies involve massive parallel short read sequence reconstructions from experiments, but they usually employ ad-hoc bioinformatic workflows that exhibit limited standardization and customization. The increasing number of transcriptome assembly software continues to provide little room for standardization which is exacerbated by the lack of studies on modularity that compare the effects of assembler synergy. We developed a customizable management workflow for de novo transcriptomics that includes modular units for short read cleaning, assembly, validation, annotation, and expression analysis by connecting twenty-five individual bioinformatic tools. With our software tool, we were able to compare the assessment scores based on 129 distinct single-, bi- and tri-assembler combinations with diverse k-mer size selections. Our results demonstrate a drastic increase in the quality of transcriptome assemblies with bi- and tri- assembler combinations. We aim for our software to improve de novo transcriptome reconstructions for the ever-growing landscape of RNA-seq data derived from non-model systems. We offer guidance to ensure the most complete transcriptomic reconstructions via the inclusion of modular multi-assembly software controlled from a single master console.
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Hynst, Jakub, Karla Plevova, Lenka Radova, Vojtech Bystry, Karol Pal, and Sarka Pospisilova. "Bioinformatic pipelines for whole transcriptome sequencing data exploitation in leukemia patients with complex structural variants." PeerJ 7 (June 12, 2019): e7071. http://dx.doi.org/10.7717/peerj.7071.
Abstract:
Background Extensive genome rearrangements, known as chromothripsis, have been recently identified in several cancer types. Chromothripsis leads to complex structural variants (cSVs) causing aberrant gene expression and the formation of de novo fusion genes, which can trigger cancer development, or worsen its clinical course. The functional impact of cSVs can be studied at the RNA level using whole transcriptome sequencing (total RNA-Seq). It represents a powerful tool for discovering, profiling, and quantifying changes of gene expression in the overall genomic context. However, bioinformatic analysis of transcriptomic data, especially in cases with cSVs, is a complex and challenging task, and the development of proper bioinformatic tools for transcriptome studies is necessary. Methods We designed a bioinformatic workflow for the analysis of total RNA-Seq data consisting of two separate parts (pipelines): The first pipeline incorporates a statistical solution for differential gene expression analysis in a biologically heterogeneous sample set. We utilized results from transcriptomic arrays which were carried out in parallel to increase the precision of the analysis. The second pipeline is used for the identification of de novo fusion genes. Special attention was given to the filtering of false positives (FPs), which was achieved through consensus fusion calling with several fusion gene callers. We applied the workflow to the data obtained from ten patients with chronic lymphocytic leukemia (CLL) to describe the consequences of their cSVs in detail. The fusion genes identified by our pipeline were correlated with genomic break-points detected by genomic arrays. Results We set up a novel solution for differential gene expression analysis of individual samples and de novo fusion gene detection from total RNA-Seq data. The results of the differential gene expression analysis were concordant with results obtained by transcriptomic arrays, which demonstrates the analytical capabilities of our method. We also showed that the consensus fusion gene detection approach was able to identify true positives (TPs) efficiently. Detected coordinates of fusion gene junctions were in concordance with genomic breakpoints assessed using genomic arrays. Discussion Byapplying our methods to real clinical samples, we proved that our approach for total RNA-Seq data analysis generates results consistent with other genomic analytical techniques. The data obtained by our analyses provided clues for the study of the biological consequences of cSVs with far-reaching implications for clinical outcome and management of cancer patients. The bioinformatic workflow is also widely applicable for addressing other research questions in different contexts, for which transcriptomic data are generated.
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Krishnan, Vidya S., and Sulev Kõks. "Transcriptional Basis of Psoriasis from Large Scale Gene Expression Studies: The Importance of Moving towards a Precision Medicine Approach." International Journal of Molecular Sciences 23, no. 11 (May 30, 2022): 6130. http://dx.doi.org/10.3390/ijms23116130.
Abstract:
Transcriptome profiling techniques, such as microarrays and RNA sequencing (RNA-seq), are valuable tools for deciphering the regulatory network underlying psoriasis and have revealed large number of differentially expressed genes in lesional and non-lesional skin. Such approaches provide a more precise measurement of transcript levels and their isoforms than any other methods. Large cohort transcriptomic analyses have greatly improved our understanding of the physiological and molecular mechanisms underlying disease pathogenesis and progression. Here, we mostly review the findings of some important large scale psoriatic transcriptomic studies, and the benefits of such studies in elucidating potential therapeutic targets and biomarkers for psoriasis treatment. We also emphasised the importance of looking into the alternatively spliced RNA isoforms/transcripts in psoriasis, rather than focussing only on the gene-level annotation. The neutrophil and blood transcriptome signature in psoriasis is also briefly reviewed, as it provides the immune status information of patients and is a less invasive platform. The application of precision medicine in current management of psoriasis, by combining transcriptomic data, improves the clinical response outcome in individual patients. Drugs tailored to individual patient’s genetic profile will greatly improve patient outcome and cost savings for the healthcare system.
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Sauta, Elisabetta, Matteo Zampini, Daniele Dall'Olio, Claudia Sala, Gabriele Todisco, Erica Travaglino, Luca Lanino, et al. "Combining Gene Mutation with Transcriptomic Data Improves Outcome Prediction in Myelodysplastic Syndromes." Blood 142, Supplement 1 (November 28, 2023): 1863. http://dx.doi.org/10.1182/blood-2023-186222.
Abstract:
Background and Aim. Myelodysplastic syndromes (MDS) are myeloid neoplasms characterized by peripheral blood cytopenias and risk of progression to acute myeloid leukemia (AML). Disease management is challenged by heterogeneity in clinical courses and survival probability. Recently, the genomic screening integration (by Molecular International Prognostic Scoring System, IPSS-M) into patient's assessment has resulted into a significant improvement in predicting clinical outcomes compared to the conventional prognostic score (Revised IPSS, IPSS-R). Many of the consequences of genetic and cytogenetic alterations will affect gene expression by means of transcriptional and epigenetic instability and altered microenviromental signaling. The aim of this project conducted by GenoMed4All and Synthema EU consortia is to link genomic information with transcriptomic data for possibly improving the prediction of clinical outcomes in MDS patients. Patients and Methods.Clinical, cytogenetic, genomic (somatic mutations screening of 31 target genes) and transcriptomic (bulk RNA-seq of CD34 + bone marrow cells) data were collected at diagnosis in 389 MDS patients. Transcriptomic and genomic profiles were processed and the former were normalized before Principal Component Analysis (PCA) dimensionality reduction to mine the interdependency of expression-wide perturbation and recurrent genomic alterations. The prognostic impacts of genetic, cytogenetic, transcriptomic, clinical and demographic features were assessed with a penalized Cox's proportional hazards model [Gerstung M et al, Nat Commun. 2015. 6, 5901] considering the Overall Survival (OS) as primary end point. A 5-fold cross-validating (CV) scheme was exploited to control bias in risk estimation. Model accuracy was assessed using Harrell's concordance index (C-index). An independent validation of the results on 202 patients was planned. Results.We first processed each data layer assessing data robustness, removed not informative variables and scaled quantitative ones. We considered recurrent genomic and cytogenetic lesions (present in ≥5 patients), platelets, hemoglobin and bone marrow blasts (%), age and sex as covariates. To explore the main patterns of expression changes, PCA was performed to reduce multidimensional correlated expression features (20 PCs was selected, explaining 42% of the total transcriptomic variability). To evaluate the prognostic power of each data layer we grouped all available features into five groups: gene mutations (n=15), cytogenetic alterations (n=7), expression data (n=20), blood counts (n=3) and demographic variables (n=2). Within a 5-fold CV we combined these variables in our integrative model to calculate MDS patients risk. The obtained predictive accuracy (C-index) for OS was 0.83, underlying that transcriptomic data significantly improved the current standard prognostic scoring systems. Accordingly, in our patient population, the C-index of the conventional IPSS-R score and the new IPSS-M were 0.68 and 0.76, respectively. A similar improvement by adding transcriptomic data was observed in prediction of the risk of AML evolution. Moreover, by analyzing the contribution of each feature category to the OS probability ( Figure 1), in term of explained variance, the relative impact of transcriptomic is 40%, with the remaining prognostic information distributed among genomic features (somatic gene mutations and cytogenetics lesions, 24%), demographics (20%) and clinical features (15%). An independent validation of these results on 202 patients is currently ongoing. Figure 2 shows an example of personalized survival prediction using patients from the study population. In two subjects with same clinical phenotype and mutations leading to a similar IPSS-M prognosis, the integrative model captures additional prognostic information and efficiently predicts clinical outcome. Given the complexity of our model, specific technological support is needed to combine data at individual patient level and to translate it into a personalized outcome prediction. To this aim, we created a prototype web portal based on our dataset for user-defined genomic/transcriptomic and clinical features. Conclusion. In predicting survival of MDS patients, genomic, transcriptomic and diagnostic clinical variables all have utility, with a significant contribution from the transcriptome.
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Dunn, Jemma, Vasileios P. Lenis, David A. Hilton, Rolf Warta, Christel Herold-Mende, C. Oliver Hanemann, and Matthias E. Futschik. "Integration and Comparison of Transcriptomic and Proteomic Data for Meningioma." Cancers 12, no. 11 (November 5, 2020): 3270. http://dx.doi.org/10.3390/cancers12113270.
Abstract:
Meningioma are the most frequent primary intracranial tumour. Management of aggressive meningioma is complex, and development of effective biomarkers or pharmacological interventions is hampered by an incomplete knowledge of molecular landscape. Here, we present an integrated analysis of two complementary omics studies to investigate alterations in the “transcriptome–proteome” profile of high-grade (III) compared to low-grade (I) meningiomas. We identified 3598 common transcripts/proteins and revealed concordant up- and downregulation in grade III vs. grade I meningiomas. Concordantly upregulated genes included FABP7, a fatty acid binding protein and the monoamine oxidase MAOB, the latter of which we validated at the protein level and established an association with Food and Drug Administration (FDA)-approved drugs. Notably, we derived a plasma signature of 21 discordantly expressed genes showing positive changes in protein but negative in transcript levels of high-grade meningiomas, including the validated genes CST3, LAMP2, PACS1 and HTRA1, suggesting the acquisition of these proteins by tumour from plasma. Aggressive meningiomas were enriched in processes such as oxidative phosphorylation and RNA metabolism, whilst concordantly downregulated genes were related to reduced cellular adhesion. Overall, our study provides the first transcriptome–proteome characterisation of meningioma, identifying several novel and previously described transcripts/proteins with potential grade III biomarker and therapeutic significance.
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Huang, Kexin, Yun Zhang, Haoran Gong, Zhengzheng Qiao, Tiangang Wang, Weiling Zhao, Liyu Huang, and Xiaobo Zhou. "Inferring evolutionary trajectories from cross-sectional transcriptomic data to mirror lung adenocarcinoma progression." PLOS Computational Biology 19, no. 5 (May 25, 2023): e1011122. http://dx.doi.org/10.1371/journal.pcbi.1011122.
Abstract:
Lung adenocarcinoma (LUAD) is a deadly tumor with dynamic evolutionary process. Although much endeavors have been made in identifying the temporal patterns of cancer progression, it remains challenging to infer and interpret the molecular alterations associated with cancer development and progression. To this end, we developed a computational approach to infer the progression trajectory based on cross-sectional transcriptomic data. Analysis of the LUAD data using our approach revealed a linear trajectory with three different branches for malignant progression, and the results showed consistency in three independent cohorts. We used the progression model to elucidate the potential molecular events in LUAD progression. Further analysis showed that overexpression of BUB1B, BUB1 and BUB3 promoted tumor cell proliferation and metastases by disturbing the spindle assembly checkpoint (SAC) in the mitosis. Aberrant mitotic spindle checkpoint signaling appeared to be one of the key factors promoting LUAD progression. We found the inferred cancer trajectory allows to identify LUAD susceptibility genetic variations using genome-wide association analysis. This result shows the opportunity for combining analysis of candidate genetic factors with disease progression. Furthermore, the trajectory showed clear evident mutation accumulation and clonal expansion along with the LUAD progression. Understanding how tumors evolve and identifying mutated genes will help guide cancer management. We investigated the clonal architectures and identified distinct clones and subclones in different LUAD branches. Validation of the model in multiple independent data sets and correlation analysis with clinical results demonstrate that our method is effective and unbiased.
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Chen, Huapu, Zhiyuan Li, Yaorong Wang, Hai Huang, Xuewei Yang, Shuangfei Li, Wei Yang, and Guangli Li. "Comparison of Gonadal Transcriptomes Uncovers Reproduction-Related Genes with Sexually Dimorphic Expression Patterns in Diodon hystrix." Animals 11, no. 4 (April 7, 2021): 1042. http://dx.doi.org/10.3390/ani11041042.
Abstract:
Diodon hystrix is a new and emerging aquaculture species in south China. However, due to the lack of understanding of reproductive regulation, the management of breeding and reproduction under captivity remains a barrier for the commercial aquaculture of D. hystrix. More genetic information is needed to identify genes critical for gonadal development. Here, the first gonadal transcriptomes of D. hystrix were analyzed and 151.89 million clean reads were generated. All reads were assembled into 57,077 unigenes, and 24,574 could be annotated. By comparing the gonad transcriptomes, 11,487 differentially expressed genes were obtained, of which 4599 were upregulated and 6888 were downregulated in the ovaries. Using enrichment analyses, many functional pathways were found to be associated with reproduction regulation. A set of sex-biased genes putatively involved in gonad development and gametogenesis were identified and their sexually dimorphic expression patterns were characterized. The detailed transcriptomic data provide a useful resource for further research on D. hystrix reproductive manipulation.
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Lindholm-Perry, Amanda. "90 Leveraging the Potential of Molecular and Genetic Markers to Improve Feed Efficiency in Beef Cattle." Journal of Animal Science 101, Supplement_3 (November 6, 2023): 94–95. http://dx.doi.org/10.1093/jas/skad281.115.
Abstract:
Abstract Genetic and other molecular markers are powerful tools with the potential to improve livestock production traits, like feed efficiency. Feed efficiency is a complex biological trait mediated by many genes. Molecular based research, including transcriptome studies to evaluate gene expression profiles to determine the genes and pathways that contribute to feed efficiency in beef cattle have been widely performed over the last decade. Despite its widespread use, challenges remain in transcriptomic data analysis, in particular non-reproducibility of results between studies. These discrepancies exist for a number of reasons including small numbers of replicates, technical differences (e.g. sample preparation, sequencing platform), and biological differences (e.g., environmental, management, and genetic effects). Recent developments in technology and analytic tools provide an opportunity to integrate and synthesize the results of existing and future data. The potential to utilize these tools to have greater impact on feed efficiency will also be discussed. USDA is an equal opportunity provider and employer.
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Bao, Riyue, Lei Huang, Jorge Andrade, Wei Tan, Warren A. Kibbe, Hongmei Jiang, and Gang Feng. "Review of Current Methods, Applications, and Data Management for the Bioinformatics Analysis of Whole Exome Sequencing." Cancer Informatics 13s2 (January 2014): CIN.S13779. http://dx.doi.org/10.4137/cin.s13779.
Abstract:
The advent of next-generation sequencing technologies has greatly promoted advances in the study of human diseases at the genomic, transcriptomic, and epigenetic levels. Exome sequencing, where the coding region of the genome is captured and sequenced at a deep level, has proven to be a cost-effective method to detect disease-causing variants and discover gene targets. In this review, we outline the general framework of whole exome sequence data analysis. We focus on established bioinformatics tools and applications that support five analytical steps: raw data quality assessment, preprocessing, alignment, post-processing, and variant analysis (detection, annotation, and prioritization). We evaluate the performance of open-source alignment programs and variant calling tools using simulated and benchmark datasets, and highlight the challenges posed by the lack of concordance among variant detection tools. Based on these results, we recommend adopting multiple tools and resources to reduce false positives and increase the sensitivity of variant calling. In addition, we briefly discuss the current status and solutions for big data management, analysis, and summarization in the field of bioinformatics.
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Franses, Joseph W., Michael J. Raabe, Amaya Pankaj, Bidish Patel, Avril Coley, Irun Bhan, Martin Aryee, and David T. Ting. "Abstract PO016: Spatial transcriptomic profiling to characterize the tumor-vascular interactome of hepatocellular carcinoma." Clinical Cancer Research 28, no. 17_Supplement (September 1, 2022): PO016. http://dx.doi.org/10.1158/1557-3265.liverca22-po016.
Abstract:
Abstract BACKGROUND: The hepatocellular carcinoma (HCC) tumor microenvironment (TME) is composed of a complex ecosystem dominated by cancer cells and the endothelial cells that line tumor blood vessels. Although many genomic drivers have been identified and at least three transcriptional subsets have been proposed, these efforts have not yet led to novel therapies or otherwise significantly impacted management options. Single cell transcriptional profiling has generated deep insights into the multiple heterogeneous cell types within tissues, but the spatial context of these data is lost during single cell processing. Spatial transcriptomic approaches aim to bridge the gap between dissociative single cell technologies and in situ histopathological characterization.METHODS: To gain insight into potential in situ cancer-endothelial crosstalk interactions, we utilized the Nanostring GeoMx spatial transcriptomics platform with the Cancer Transcriptome Atlas ~1800 gene oligonucleotide probe panel to generate tumor (Arginase+) and blood vessel (CD31+) areas of interest (AOI) gene expression profiles from formalin-fixed, paraffin-embedded archival tissue specimens obtained from HCC resection specimens. Oligonucleotides released from each microscopic AOI were then captured, processed by DNA sequencing, and analyzed using custom computational pipelines.RESULTS: Using the 119 ROI containing data from both tumor and vessels that passed quality control filters, we performed unbiased hierarchical clustering of both the tumor and vessel areas of interest (AOI) within each ROI using the most highly variable genes for each AOI set and identified at least 3 clusters within each AOI type (tumor and vessel). Based on gene ontology analysis of the tumor AOIs, the two subsets were distinguished by unique immune and inflammatory-related genes. Analogous ontology-based characterization of the vessel AOIs demonstrated two groups: 1) an interferon-activated, inflamed progenitor, and immune checkpoint-associated cluster; and 2) a TGF-beta and oxidative stress-associated cluster. Notably, both vessel clusters also contained significant numbers of leukocyte genes, concordant with the intimate relationship of the vasculature and immune system. Canonical correlation analysis (CCA) utilizing both the most variable genes within each AOI set showed significant correlated gene sets within tumor AOIs and vessel AOIs, implying biologically significant interactions in multiple signaling pathways.CONCLUSIONS: Spatial transcriptomic profiling enables an understanding of cell-cell interactions in situ that can uncover biologically distinct tumor and blood vessel niches within the HCC microenvironment. Subsequent efforts will be focused on functionally assessing the spatially linked cancer and endothelial cell phenotypes with the goals of developing improved prognostic and predictive biomarkers and generating novel drug targets. Citation Format: Joseph W Franses, Michael J Raabe, Amaya Pankaj, Bidish Patel, Avril Coley, Irun Bhan, Martin Aryee, David T Ting. Spatial transcriptomic profiling to characterize the tumor-vascular interactome of hepatocellular carcinoma [abstract]. In: Proceedings of the AACR Special Conference: Advances in the Pathogenesis and Molecular Therapies of Liver Cancer; 2022 May 5-8; Boston, MA. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(17_Suppl):Abstract nr PO016.
Dissertations / Theses on the topic "Transcriptomic data management":
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Bouvier, Matteo. "Identification et contrôle de réseaux de régulation de gènes." Electronic Thesis or Diss., Lyon, École normale supérieure, 2023. http://www.theses.fr/2023ENSL0117.
Abstract:
L’identification précise de Réseaux de Régulation de Gènes (RRG) est encore aujourd’hui une question de grande importance pour la biologie des systèmes, puisqu’elle permettrait d’expliquer finement les phénomènes de prise de décision cellulaire. Des travaux menés précédemment dans notre équipe ont permis d'établir un algorithme itératif de reconstruction de RRGs. Cet algorithme a la particularité de ne pas proposer un ensemble de réseaux simulables au lieu d’un seul réseau candidat. Cette thèse propose une stratégie de sélection parmi un ensemble de RRGs par conception d’expériences de perturbation. Dans un premier temps, nous avons proposé une solution informatique pour le stockage et la manipulation des très grands jeux de données produits par la simulation des RRGs. Cette solution prend la forme de deux librairies Python pour optimiser l’empreinte mémoire de grandes matrices. Ensuite, nous avons défini une stratégie de sélection de réseaux par conception d'expériences. Une analyse topologique des réseaux candidats permet de choisir un petit nombre de perturbations qui seront ensuite simulées sur les RRGs afin de retenir la perturbation la plus discriminante. Enfin, nous avons élaboré un algorithme de contrôle de RRGs permettant de prédire la séquence de stimuli à appliquer pour le mener à un état cellulaire voulu et dont une preuve de concept a été faitePrecise inference of Gene Regulatory Networks (GRNs) remains to this day a challenging task in the systems biology field but would allow us to explain the processes of cellular decision-making. Previous work in our team has led to the proposal of an iterative GRN inference algorithm that does not produce a single GRN but rather an ensemble of executable candidate networks. This thesis proposes a strategy for GRN selection from an ensemble that relies on design of experiments. First, we introduce two Python libraries for the storage and manipulation of the very large datasets generated by the simulation of our GRNs. These libraries control the memory footprint of large and dense matrices. Then, we propose a design of experiment strategy for selecting networks. A small number of promising perturbations is selected by topological analysis of the GRNs. Perturbations are simulated and the most discriminative is chosen. Finally, we developed an algorithm for controlling GRNs by determining the sequence of stimuli to apply to reach a desired cell state. A proof of concept is presented
Book chapters on the topic "Transcriptomic data management":
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Ijaz, Muhammad, and Muhammad Muddassir Ali. "Next-generation Sequencing in Veterinary Medicine Technologies to Improve Diagnosis, Control, and Management of Livestock Diseases." In Recent Trends In Livestock Innovative Technologies, 170–87. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815165074123070015.
Abstract:
Next-generation sequencing has changed the study of genetics. NGS technology is predicted to be significant in veterinary care and animal husbandry. With the development of modern techniques, genomes can now be sequenced considerably more quickly and accurately. In the current review, we detail the many sequencing techniques that are accessible and also go over a few biological topics where using next-generation sequencing might lead to whole new directions in veterinary research. Large volumes of genomic, transcriptomic, and proteomic data may now be analysed by researchers thanks to the advent of high throughput molecular technologies and accompanying bioinformatics. The volume of DNA sequence information that can be generated using Next Generation Sequencing (NGS) technology is a glaring illustration of this stage. The identification and quantification of proteins in a given sample have also been made easier by recent advancements in high-precision mass spectrometry and protein and peptide separation efficiency. The way biological and evolutionary processes are investigated at the molecular level is beginning to change due to these technological advancements, which are also being utilised to research infectious illnesses in animals. To better understand how next-generation sequencing functions and how it might be applied to veterinary medicine for the sake of disease management and control, this chapter focuses on presenting existing and projected insights.
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van Dam, Pieter-Jan, and Steven Van Laere. "Molecular profiling in cancer research and personalized medicine." In Oxford Textbook of Cancer Biology, edited by Francesco Pezzella, Mahvash Tavassoli, and David J. Kerr, 347–62. Oxford University Press, 2019. http://dx.doi.org/10.1093/med/9780198779452.003.0024.
Abstract:
Recent efforts by worldwide consortia such as The Cancer Genome Atlas and the International Cancer Genome Consortium have greatly accelerated our knowledge of human cancer biology. Nowadays, complete sets of human tumours that have been characterized at the genomic, epigenomic, transcriptomic, or proteomic level are available to the research community. The generation of these data was made possible thanks to the application of high-throughput molecular profiling techniques such as microarrays and next-generation sequencing. The primary conclusion from current profiling experiments is that human cancer is a complex disease characterized by extreme molecular heterogeneity, both between and within the classical, tissue-defined cancer types. This molecular variety necessitates a paradigm shift in patient management, away from generalized therapy schemes and towards more personalized treatments. This chapter provides an overview of how molecular cancer profiling can assist in facilitating this transition. First, the state-of-the-art of molecular breast cancer profiling is reviewed to provide a general background. Then, the most pertinent high-throughput molecular profiling techniques along with various data mining techniques (i.e. unsupervised clustering, statistical learning) are discussed. Finally, the challenges and perspectives with respect to molecular cancer profiling, also from the perspective of personalized medicine, are summarized.
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Kumar, Hithesh, Vivek Chandramohan, Smrithy M. Simon, Rahul Yadav, and Shashi Kumar. "Big Data Analysis Techniques for Visualization of Genomics in Medicinal Plants." In Advances in Data Mining and Database Management, 749–81. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3142-5.ch026.
Abstract:
In this chapter, the complete overview and application of Big Data analysis in the field of health care industries, Clinical Informatics, Personalized Medicine and Bioinformatics is provided. The major tools and databases used for the Big Data analysis are discussed in this chapter. The development of sequencing machines has led to the fast and effective ways of generating DNA, RNA, Whole Genome data, Transcriptomics data, etc. available in our hands in just a matter of hours. The complete Next Generation Sequencing (NGS) huge data analysis work flow for the medicinal plants are discussed in the chapter. This chapter serves as an introduction to the big data analysis in Next Generation Sequencing and concludes with a summary of the topics of the remaining chapters of this book.
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Nurain, Ismaila O. "Phytoinformatics in Disease Management." In Therapeutic Use of Plant Secondary Metabolites, 343–64. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/9789815050622122010017.
Abstract:
The profound importance of medicinal plants as therapeutic agents as wellas their economic values has captured the attention of researchers around the world.However, it has been recognized that standardization of medicinal plant research isrequired for its incorporation into modern medicine and to maintain the healthydevelopment of the traditional medicine industry. Due to this fact, several extensiveresearch efforts have been added to the existing approaches to upgrade the sectorthrough standardization and authentication of medicinal plant and plant products aswell as bioengineering of metabolic pathways. This chapter has divulged informationabout the application of computational omics approaches to medicinal plant researchand its relevance in disease management. Omics studies such as genomics,transcriptomics, proteomics, metabolomics as well as multi-omics data integration wereaccounted for their application in a medicinal plant. Some bioinformatics programs,tools, and web databases were explained and their application in the phytoinformaticsanalysis of medicinal plant was discussed. This chapter concluded with the importanceof storing, integrating, and management of biological and medicinal plant data to makethem available as information used in disease management. It is, therefore, hoped thatthis chapter will enlighten medicinal plant researchers more on the availability ofcomputational tools to use in standardizing traditional medicine and authenticate themethodologies by making them reproducible and applicable to disease management.
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Swargam, Sandeep, and Indu Kumari. "An Introduction to the Integration of Systems Biology and OMICS data for Animal Scientists." In Systems Biology, Bioinformatics and Livestock Science, 1–16. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815165616123010006.
Abstract:
Systems biology integrates the data of all the omics studies and provides the avenues to understand the biology of an organism at higher levels like at tissue, organ or organism level. In the last decade, studies of genomics, transcriptomics, proteomics and metabolomics have been carried out. Only a limited amount of this big data has been analyzed, which is mainly focused on the genotype (single nucleotide polymorphism) level like minor allele frequency, copy number variation and structural variants. The analysis in transcriptomics is limited to differentially expressed genes and their ontology. Proteomics is focused on virulent factors, proteins involved in the disease progression and immunomodulation. However, in the case of livestock animals, there is a need to develop pipelines for the analysis of the omics data. With the integration of omics data into systems biology studies, there is a need to develop algorithms to carry out gene interaction and protein interaction studies and to build interaction networks. The pathway analysis of a system requires the well-defined interacting hub and edges of the protein system of an organism. Developing AI-ML models for drug discovery is required to target the pathogens of livestock animals. In the present era, the research is moving towards single-cell sequencing of the cells and tissues to explore the genetic heterogeneity in the micro-environment of the tissue and spatial biology of the tissue. This chapter will introduce the reader to different aspects of omics technology and its role in systems biology for better livestock management.
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Papadopoulou, Paraskevi, Anastasia Misseyanni, and Christina Marouli. "Current Environmental Health Challenges." In Handbook of Research on Emerging Developments and Environmental Impacts of Ecological Chemistry, 1–37. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1241-8.ch001.
Abstract:
This is the first of two overview chapters of important contemporary environmental health challenges. The exciting developments in the environmental health fields are approached in an interdisciplinary manner covering cutting-edge scientific developments and research. In the first chapter, environmental exposures to a variety of toxins, diseases, and stressors that challenge the individual and affect public health are examined. The handling, storage, big data management related to medical and health-informatics are discussed. Issues such as single gene polymorphisms, gene expression, transcriptomics, epigenetics, metabolomics, exposure to carcinogens, endocrine disruptors, heavy metals, physical hazards, airborne particulates, quality of food and water, toxin metabolism, bioinformatics, and exposome analysis are considered. Important recommendations and solutions are provided emphasizing the collaboration between researchers/scientists and the community.
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Verma, Renu, Parameswar Sahu, Aarti Rana, Sandeep Swargam, and Indu Kumari. "Single Cell RNA-Sequencing and Its Application in Livestock Animals." In Systems Biology, Bioinformatics and Livestock Science, 226–42. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815165616123010015.
Abstract:
Single cell RNA sequencing (ScRNAseq) is in its infancy. There are limited studies in which this technique has been implemented to solve the scientific problem. ScRNAseq involves well facilitated labs and high end computing facilities. The ScRNAseq studies were mainly carried out in the clinical and biomedical areas. These studies are carried out in cancer research, which involves the role of immune genes or immunotherapy for cancer treatment. The human cell atlas programme is going on and atlases for different human cells are being released as it is completed. However, in the case of livestock animals, it has just started. In India, there are few ScRNAseq studies that have focused on the different developmental stages of buffalo. The experimental and bioinformatics analysis ScRNAseq involves various steps. Among this, the alignment of reads to reference genome/transcriptome is important. There is a need to develop a standardized reference genome/transcriptome for each type of cell present in different domestic/commercial livestock. Once we have all the valuable information from ScRNAseq, then this data can be integrated with system biology approaches to understand the cellular processes at a larger scale. This integration of interdisciplinary sciences will enhance the production, quality and health of the livestock animals and may help for sustainable management of livestock.
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Patil, Bheemshetty S., Pallavi S. Kanthe, Prachi P. Parvatikar, and Aravind V. Patil. "Genomics to Systems Biology in Livestock Management: its Applications and Future Perspective." In Systems Biology, Bioinformatics and Livestock Science, 260–78. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815165616123010017.
Abstract:
The recurrent and comprehensive study of biological systems as a single entity in response to stimuli is known as systems biology. The introduction of high-throughput technology for studying an animal's DNA, proteome, and metabolome was a blow to reductionism in livestock science. It is based on ideas formalized in models derived from global functional genomics investigations of the genome, transcriptome, proteome, metabolome, and other complex biological systems. The mapping of entire sets of genes, transcripts, proteins, and metabolites from a variety of organisms has driven the creation of novel '-omic' technologies for gathering and analyzing vast amounts of data. This widely defined systems approach is being used to address a wide range of issues and organizational scales, along with several elements of livestock research. It is well established that the tools that relate genetic variations to their cellular activities, pathways, and other biological roles will become even more essential in the future. For each animal genomics research issue, a vision, current state of the art, research needed to progress the field, expected outputs, and partnerships are required. Modern computational tools capable of finding functional implications and biologically meaningful networks complement the ever-increasing ability to generate massive molecular, microbial, and metabolite data sets. The intricate inter-tissue responses to physiological status and nutrition can now be seen at the same time. The knowledge acquired from the application of functional analysis of systems biology data sets to livestock management in order to improve productivity, quality, and yield.
Conference papers on the topic "Transcriptomic data management":
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BenHamadou1, Alexandra Leitao, Zenaba Khatir, Noora Al-Shamary, Hassan Hassan, Zainab Hizan, Aisha Al-Ashwal, Mark Chatting, et al. "Pearl Oyster: From National Icon To Guardian of Qatar's Marine Environment." In Qatar University Annual Research Forum & Exhibition. Qatar University Press, 2020. http://dx.doi.org/10.29117/quarfe.2020.0051.
Abstract:
The NPRP9-394-1-090 project “Pearl Oyster: from national icon to guardian of Qatar's marine environment” had as main aim to develop and apply an integrated suite of chemical and biological methods as early warning tools to assess the “health” of Qatar’s marine environment. The central theme consisted in an investigative monitoring program around the use of the pearl oyster, Pictada imbricata radiata, as a sentinel or guardian species. We have characterized the main environmental contaminants of concern at a selected number of sites around the Qatari coast (UmmBab, Al Khor, Al Wakra and Simaisma), during 2 years, in summer and winter. Potential ecological effects of contaminants (targeted and untargeted) were investigated at different biological organization levels (gene, chromosome, cell, individual, population), through a multidisciplinary approach, using classical and genotoxicological endpoints, integrative histopathology and transcriptomic responses to the different environmental stresses. To our knowledge, this is the first time an integrated approach connecting all these disciplines has been applied in the Qatari marine environment. We present here the main results, of this 3 years project, obtained in all different disciplinary approaches. The results of this project will leave a legacy of resources for future Qatari researchers, including an open access transcriptome data base and the first description of common pathologies observed in the pearl oyster P. i. radiata. Moreover, they will also represent a sound science-based baseline data essential for conservation and management planning, by integration of the data from all the different disciplines applied in the project to assess the potential ecological effects of contaminants at different biological levels.
Reports on the topic "Transcriptomic data management":
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Bloch, G., and H. S. Woodard. regulation of size related division of labor in a key pollinator and its impact on crop pollination efficacy. Israel: United States-Israel Binational Agricultural Research and Development Fund, 2021. http://dx.doi.org/10.32747/2021.8134168.bard.
Abstract:
Despite the rapid increase in reliance on bumble bees for food production and security, there are many critical knowledge gaps in our understanding of bumble bee biology that limit their colony production, commercial management, and pollination services. Our project focuses on the social, endocrine, and molecular processes regulating body size in the two bumble bee species most important to agriculture: Bombus terrestris in Israel, and B. impatiens in the USA. Variation in body size underline both caste (queen/worker) differentiation and division of labor among workers (foragers are typically larger than nest bees), two hallmarks of insect sociality which are also crucial for the commercial rearing and crop pollination services of bumble bees. Our project has generated several fundamental new insights into the biology of bumble bees, which can be integrated into science-based management strategies for commercial pollination. Using transcriptomic and behavioral approaches we show that in spite of high flexibility, task performance (brood care or foraging) in bumble bee colonies is associated with physiological variation and differential brain gene expression and RNA editing patterns. We further showed that interactions between the brood, the queen, and the workers determine the developmental program of the larva. We identified two important periods. The first is a critical period during the first few days after hatching. Larvae fed by queens during this period develop over less days, are not likely to develop into gynes, and commonly reach a smaller ultimate body size compared to workers reared mostly or solely by workers. The facial exocrine (mandibular and hypopharangeal) glands are involved in this queen effect on larva development. The second period is important for determining the ultimate body size which is positively regulated by the number of tending workers. The presence of the queen during this stage has little, if at all, influence. We further show that stressors such as agrochemicals that interfere with foraging or brood care specific processes can compromise bumble bee colony development and their pollination performance. We also developed new technology (an RFID system) for automated collection of foraging trip data, for future deployment in agroecosystems. In spite of many similarities, our findings suggest important differences between the Eurasian model species (B. terrestris) and the North American model species (B. impatiens) that impact how management strategies translate across the two species. For example, there is a similar influence of the queen on offspring body size in both species, but this effect does not appear to be mediated by development time in B. impatiens as it is in B. terrestris. Taken together, our collaboration highlights the power of comparative work, to show that considerable differences that exist between these two key pollinator species, and in the organization of young bumble bee nests (wherein queens provide the majority of care and then transition away from brood care) relative to later stages of nest development.
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Crowley, David E., Dror Minz, and Yitzhak Hadar. Shaping Plant Beneficial Rhizosphere Communities. United States Department of Agriculture, July 2013. http://dx.doi.org/10.32747/2013.7594387.bard.
Abstract:
PGPR bacteria include taxonomically diverse bacterial species that function for improving plant mineral nutrition, stress tolerance, and disease suppression. A number of PGPR are being developed and commercialized as soil and seed inoculants, but to date, their interactions with resident bacterial populations are still poorly understood, and-almost nothing is known about the effects of soil management practices on their population size and activities. To this end, the original objectives of this research project were: 1) To examine microbial community interactions with plant-growth-promoting rhizobacteria (PGPR) and their plant hosts. 2) To explore the factors that affect PGPR population size and activity on plant root surfaces. In our original proposal, we initially prqposed the use oflow-resolution methods mainly involving the use of PCR-DGGE and PLFA profiles of community structure. However, early in the project we recognized that the methods for studying soil microbial communities were undergoing an exponential leap forward to much more high resolution methods using high-throughput sequencing. The application of these methods for studies on rhizosphere ecology thus became a central theme in these research project. Other related research by the US team focused on identifying PGPR bacterial strains and examining their effective population si~es that are required to enhance plant growth and on developing a simulation model that examines the process of root colonization. As summarized in the following report, we characterized the rhizosphere microbiome of four host plant species to determine the impact of the host (host signature effect) on resident versus active communities. Results of our studies showed a distinct plant host specific signature among wheat, maize, tomato and cucumber, based on the following three parameters: (I) each plant promoted the activity of a unique suite of soil bacterial populations; (2) significant variations were observed in the number and the degree of dominance of active populations; and (3)the level of contribution of active (rRNA-based) populations to the resident (DNA-based) community profiles. In the rhizoplane of all four plants a significant reduction of diversity was observed, relative to the bulk soil. Moreover, an increase in DNA-RNA correspondence indicated higher representation of active bacterial populations in the residing rhizoplane community. This research demonstrates that the host plant determines the bacterial community composition in its immediate vicinity, especially with respect to the active populations. Based on the studies from the US team, we suggest that the effective population size PGPR should be maintained at approximately 105 cells per gram of rhizosphere soil in the zone of elongation to obtain plant growth promotion effects, but emphasize that it is critical to also consider differences in the activity based on DNA-RNA correspondence. The results ofthis research provide fundamental new insight into the composition ofthe bacterial communities associated with plant roots, and the factors that affect their abundance and activity on root surfaces. Virtually all PGPR are multifunctional and may be expected to have diverse levels of activity with respect to production of plant growth hormones (regulation of root growth and architecture), suppression of stress ethylene (increased tolerance to drought and salinity), production of siderophores and antibiotics (disease suppression), and solubilization of phosphorus. The application of transcriptome methods pioneered in our research will ultimately lead to better understanding of how management practices such as use of compost and soil inoculants can be used to improve plant yields, stress tolerance, and disease resistance. As we look to the future, the use of metagenomic techniques combined with quantitative methods including microarrays, and quantitative peR methods that target specific genes should allow us to better classify, monitor, and manage the plant rhizosphere to improve crop yields in agricultural ecosystems. In addition, expression of several genes in rhizospheres of both cucumber and whet roots were identified, including mostly housekeeping genes. Denitrification, chemotaxis and motility genes were preferentially expressed in wheat while in cucumber roots bacterial genes involved in catalase, a large set of polysaccharide degradation and assimilatory sulfate reduction genes were preferentially expressed.