Relevant bibliographies by topics / Omic data

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  2. Dissertations / Theses
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  4. Book chapters
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Academic literature on the topic 'Omic data'

Author: Grafiati
Published: 19 April 2025

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Journal articles on the topic "Omic data"

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Oromendia, Ana, Dorina Ismailgeci, Michele Ciofii, Taylor Donnelly, Linda Bojmar, John Jyazbek, Arnaub Chatterjee, David Lyden, Kenneth H. Yu, and David Paul Kelsen. "Error-free, automated data integration of exosome cargo protein data with extensive clinical data in an ongoing, multi-omic translational research study." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): e16743-e16743. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e16743.

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e16743 Background: Major advances in understanding the biology of cancer have come from genomic analysis of tumor and normal tissue. Integrating extensive patient-related data with deep analysis of omic data is crucial to informing omic data interpretation. Currently, such integrations are a highly manual, asynchronous, and costly process as well as error-prone and time-consuming. To develop new blood assays that may detect very early stage PDAC, a multi-omic investigation with deep clinical annotation is needed. Using pilot data from an on-going study, we test a new platform allowing automated error-free integration of an extensive clinical database with extensive omic data. Methods: Demographic, clinical, family pedigree and pathology data were collected on the Rave EDC platform. Exosomes were purified from 46 plasma samples from 14 controls and 24 PDAC patients and cargo proteins were quantified via SILAC. The Rave Omics platform was used to ingest and integrate clinical and omic data, run quality checks and generate integrated clinical-omic datasets. Data fidelity was validated by systematically computing differences between corresponding values in the source flies with those present in the extracted data object (integrated data). The root mean squared error (RMSE) was calculated for numeric values in each sample. Additional validation was conducted by manual inspection to ascertain data integrity. Results: We demonstrated automatic integration, without human intervention, of a subset of the clinical data and all available SILAC data into an analysis-ready data object. Data transfer was completely faithful, with 100% concordance between the source and the integrated data without loss of features. All proteins (n = 1515) and clinical variables (n = 64) were imported. Their nomenclature and corresponding sample values (n = 69690) and clinical values (n = 2432) matched exactly between datasets. In all samples, the RMSE was exactly zero, indicating no deviation between data sources. Conclusions: We demonstrated that automatic, efficient, and reliable integration of clinical-omic data is achievable during an in-flight PDAC trial. Automatic exploratory analytics supporting biomarker discovery are currently being used to uncover associations between omic and clinical features. The Rave Omics platform is disease-agnostic and we plan to expand to trials of varying size, indication, and completion status where systematic, automated integration of clinical and (multi)omic data is needed.
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Ugidos, Manuel, Sonia Tarazona, José M. Prats-Montalbán, Alberto Ferrer, and Ana Conesa. "MultiBaC: A strategy to remove batch effects between different omic data types." Statistical Methods in Medical Research 29, no. 10 (March 4, 2020): 2851–64. http://dx.doi.org/10.1177/0962280220907365.

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Diversity of omic technologies has expanded in the last years together with the number of omic data integration strategies. However, multiomic data generation is costly, and many research groups cannot afford research projects where many different omic techniques are generated, at least at the same time. As most researchers share their data in public repositories, different omic datasets of the same biological system obtained at different labs can be combined to construct a multiomic study. However, data obtained at different labs or moments in time are typically subjected to batch effects that need to be removed for successful data integration. While there are methods to correct batch effects on the same data types obtained in different studies, they cannot be applied to correct lab or batch effects across omics. This impairs multiomic meta-analysis. Fortunately, in many cases, at least one omics platform—i.e. gene expression— is repeatedly measured across labs, together with the additional omic modalities that are specific to each study. This creates an opportunity for batch analysis. We have developed MultiBaC (multiomic Multiomics Batch-effect Correction correction), a strategy to correct batch effects from multiomic datasets distributed across different labs or data acquisition events. Our strategy is based on the existence of at least one shared data type which allows data prediction across omics. We validate this approach both on simulated data and on a case where the multiomic design is fully shared by two labs, hence batch effect correction within the same omic modality using traditional methods can be compared with the MultiBaC correction across data types. Finally, we apply MultiBaC to a true multiomic data integration problem to show that we are able to improve the detection of meaningful biological effects.
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Rappoport, Nimrod, and Ron Shamir. "NEMO: cancer subtyping by integration of partial multi-omic data." Bioinformatics 35, no. 18 (January 30, 2019): 3348–56. http://dx.doi.org/10.1093/bioinformatics/btz058.

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Abstract Motivation Cancer subtypes were usually defined based on molecular characterization of single omic data. Increasingly, measurements of multiple omic profiles for the same cohort are available. Defining cancer subtypes using multi-omic data may improve our understanding of cancer, and suggest more precise treatment for patients. Results We present NEMO (NEighborhood based Multi-Omics clustering), a novel algorithm for multi-omics clustering. Importantly, NEMO can be applied to partial datasets in which some patients have data for only a subset of the omics, without performing data imputation. In extensive testing on ten cancer datasets spanning 3168 patients, NEMO achieved results comparable to the best of nine state-of-the-art multi-omics clustering algorithms on full data and showed an improvement on partial data. On some of the partial data tests, PVC, a multi-view algorithm, performed better, but it is limited to two omics and to positive partial data. Finally, we demonstrate the advantage of NEMO in detailed analysis of partial data of AML patients. NEMO is fast and much simpler than existing multi-omics clustering algorithms, and avoids iterative optimization. Availability and implementation Code for NEMO and for reproducing all NEMO results in this paper is in github: https://github.com/Shamir-Lab/NEMO. Supplementary information Supplementary data are available at Bioinformatics online.
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Canela, Núria Anela. "A pioneering multi-omics data platform sheds light on the understanding of biological systems." Project Repository Journal 20, no. 1 (July 4, 2024): 20–23. http://dx.doi.org/10.54050/prj2021863.

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A pioneering multi-omics data platform sheds light on the understanding of biological systems The GLOMICAVE project has developed an innovative multi-omics data analysis digital platform, relying on big data analytics and artificial intelligence and using large-scale publicly available and experimental omic datasets. The project aimed to maximise the utility of omic data at a massive level and discover new links between animal and vegetable genotype and phenotype, understanding biological systems as a whole.
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Lancaster, Samuel M., Akshay Sanghi, Si Wu, and Michael P. Snyder. "A Customizable Analysis Flow in Integrative Multi-Omics." Biomolecules 10, no. 12 (November 27, 2020): 1606. http://dx.doi.org/10.3390/biom10121606.

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The number of researchers using multi-omics is growing. Though still expensive, every year it is cheaper to perform multi-omic studies, often exponentially so. In addition to its increasing accessibility, multi-omics reveals a view of systems biology to an unprecedented depth. Thus, multi-omics can be used to answer a broad range of biological questions in finer resolution than previous methods. We used six omic measurements—four nucleic acid (i.e., genomic, epigenomic, transcriptomics, and metagenomic) and two mass spectrometry (proteomics and metabolomics) based—to highlight an analysis workflow on this type of data, which is often vast. This workflow is not exhaustive of all the omic measurements or analysis methods, but it will provide an experienced or even a novice multi-omic researcher with the tools necessary to analyze their data. This review begins with analyzing a single ome and study design, and then synthesizes best practices in data integration techniques that include machine learning. Furthermore, we delineate methods to validate findings from multi-omic integration. Ultimately, multi-omic integration offers a window into the complexity of molecular interactions and a comprehensive view of systems biology.
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Morota, Gota. "30 Mutli-omic data integration in quantitative genetics." Journal of Animal Science 97, Supplement_2 (July 2019): 15. http://dx.doi.org/10.1093/jas/skz122.027.

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Abstract The advent of high-throughput technologies has generated diverse omic data including single-nucleotide polymorphisms, copy-number variation, gene expression, methylation, and metabolites. The next major challenge is how to integrate those multi-omic data for downstream analyses to enhance our biological insights. This emerging approach is known as multi-omic data integration, which is in contrast to studying each omic data type independently. I will discuss challenging issues in developing algorithms and methods for multi-omic data integration. The particular focus will be given to the potential for combining diverse types of FAANG data and the utility of multi-omic data integration in association analysis and phenotypic prediction.
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Escriba-Montagut, Xavier, Yannick Marcon, Augusto Anguita-Ruiz, Demetris Avraam, Jose Urquiza, Andrei S. Morgan, Rebecca C. Wilson, Paul Burton, and Juan R. Gonzalez. "Federated privacy-protected meta- and mega-omics data analysis in multi-center studies with a fully open-source analytic platform." PLOS Computational Biology 20, no. 12 (December 9, 2024): e1012626. https://doi.org/10.1371/journal.pcbi.1012626.

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The importance of maintaining data privacy and complying with regulatory requirements is highlighted especially when sharing omic data between different research centers. This challenge is even more pronounced in the scenario where a multi-center effort for collaborative omics studies is necessary. OmicSHIELD is introduced as an open-source tool aimed at overcoming these challenges by enabling privacy-protected federated analysis of sensitive omic data. In order to ensure this, multiple security mechanisms have been included in the software. This innovative tool is capable of managing a wide range of omic data analyses specifically tailored to biomedical research. These include genome and epigenome wide association studies and differential gene expression analyses. OmicSHIELD is designed to support both meta- and mega-analysis, so that it offers a wide range of capabilities for different analysis designs. We present a series of use cases illustrating some examples of how the software addresses real-world analyses of omic data.
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Meunier, Lea, Guillaume Appe, Abdelkader Behdenna, Valentin Bernu, Helia Brull Corretger, Prashant Dhillon, Eleonore Fox, et al. "Abstract 6209: From data disparity to data harmony: A comprehensive pan-cancer omics data collection." Cancer Research 84, no. 6_Supplement (March 22, 2024): 6209. http://dx.doi.org/10.1158/1538-7445.am2024-6209.

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Abstract In cancer research, the exponential growth of omics datasets offers a significant opportunity for scientific advancement. However, challenges such as the lack of uniform standards, in both clinical and omic data, hinder the effective utilization of these datasets, thus impeding our understanding of cancer biology and the development of innovative therapeutic approaches.Addressing these challenges, we have created a novel collection of pan-cancer omics datasets with extensive clinical data harmonization and consistent omic data normalization.Here, we focused on patient-derived gene expression microarray datasets from the Gene Expression Omnibus database. To navigate the complexities presented by the diverse clinical descriptions inherent in these datasets, we leveraged our proprietary ontology, machine learning models, and domain expert quality control processes to homogenize the clinical data elements.Datasets were then selected based on sample composition, molecular data compatibility, and clinical data availability, then passed through a uniform preprocessing and normalization pipeline to maximize data quality. Finally, gene names were aligned on a single annotation reference, and potential batch effects were adjusted before expression data were merged together.We obtained a total of 32,825 transcriptomic sample profiles from 470 datasets, covering 13,435 genes and 45 clinical data elements, across 30 cancer types. Healthy tissue was favored over adjacent tissue, to minimize the risk of introducing biases related to cancer patient background genomic profiles into downstream analyses. We compared our collection with The Cancer Genome Atlas (TCGA), the most commonly used RNA-seq transcriptomic dataset in cancer research. It covers 30 out of the 33 TCGA cancer types, with on average 4.2 times more samples per cancer type ([0.3; 45.5], median 3.4). Despite the two data collections being based on distinct technologies, we observed a Pearson correlation of 0.69 over the 11,753 genes in common, and a 100% overlap of the differentially expressed genes between genders. This consistency highlights cross-technology reliability and complementarity.We have built and continuously enriched a comprehensive dataset collection enabling the secondary analysis of high-quality omic data. This initial work - focused on microarray datasets - allows us to streamline design, exploration and validation of various omics data-driven studies in cancer research.Our ongoing efforts involve not only the continued integration of microarray datasets but also the integration of pan-cancer RNA-seq and single-cell data. This initiative is set to expand further, encompassing a broader range of omics datasets in the future. Citation Format: Lea Meunier, Guillaume Appe, Abdelkader Behdenna, Valentin Bernu, Helia Brull Corretger, Prashant Dhillon, Eleonore Fox, Julien Haziza, Charles Lescure, Camille Marijon, Clemence Petit, Solene Weill, Akpeli Nordor. From data disparity to data harmony: A comprehensive pan-cancer omics data collection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6209.
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Quackenbush, John. "Data standards for 'omic' science." Nature Biotechnology 22, no. 5 (May 2004): 613–14. http://dx.doi.org/10.1038/nbt0504-613.

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Boekel, Jorrit, John M. Chilton, Ira R. Cooke, Peter L. Horvatovich, Pratik D. Jagtap, Lukas Käll, Janne Lehtiö, Pieter Lukasse, Perry D. Moerland, and Timothy J. Griffin. "Multi-omic data analysis using Galaxy." Nature Biotechnology 33, no. 2 (February 2015): 137–39. http://dx.doi.org/10.1038/nbt.3134.

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Dissertations / Theses on the topic "Omic data"

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Guan, Xiaowei. "Bioinformatics Approaches to Heterogeneous Omic Data Integration." Case Western Reserve University School of Graduate Studies / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=case1340302883.

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Xiao, Hui. "Network-based approaches for multi-omic data integration." Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/289716.

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The advent of advanced high-throughput biological technologies provides opportunities to measure the whole genome at different molecular levels in biological systems, which produces different types of omic data such as genome, epigenome, transcriptome, translatome, proteome, metabolome and interactome. Biological systems are highly dynamic and complex mechanisms which involve not only the within-level functionality but also the between-level regulation. In order to uncover the complexity of biological systems, it is desirable to integrate multi-omic data to transform the multiple level data into biological knowledge about the underlying mechanisms. Due to the heterogeneity and high-dimension of multi-omic data, it is necessary to develop effective and efficient methods for multi-omic data integration. This thesis aims to develop efficient approaches for multi-omic data integration using machine learning methods and network theory. We assume that a biological system can be represented by a network with nodes denoting molecules and edges indicating functional links between molecules, in which multi-omic data can be integrated as attributes of nodes and edges. We propose four network-based approaches for multi-omic data integration using machine learning methods. Firstly, we propose an approach for gene module detection by integrating multi-condition transcriptome data and interactome data using network overlapping module detection method. We apply the approach to study the transcriptome data of human pre-implantation embryos across multiple development stages, and identify several stage-specific dynamic functional modules and genes which provide interesting biological insights. We evaluate the reproducibility of the modules by comparing with some other widely used methods and show that the intra-module genes are significantly overlapped between the different methods. Secondly, we propose an approach for gene module detection by integrating transcriptome, translatome, and interactome data using multilayer network. We apply the approach to study the ribosome profiling data of mTOR perturbed human prostate cancer cells and mine several translation efficiency regulated modules associated with mTOR perturbation. We develop an R package, TERM, for implementation of the proposed approach which offers a useful tool for the research field. Next, we propose an approach for feature selection by integrating transcriptome and interactome data using network-constrained regression. We develop a more efficient network-constrained regression method eGBL. We evaluate its performance in term of variable selection and prediction, and show that eGBL outperforms the other related regression methods. With application on the transcriptome data of human blastocysts, we select several interested genes associated with time-lapse parameters. Finally, we propose an approach for classification by integrating epigenome and transcriptome data using neural networks. We introduce a superlayer neural network (SNN) model which learns DNA methylation and gene expression data parallelly in superlayers but with cross-connections allowing crosstalks between them. We evaluate its performance on human breast cancer classification. The SNN provides superior performances and outperforms several other common machine learning methods. The approaches proposed in this thesis offer effective and efficient solutions for integration of heterogeneous high-dimensional datasets, which can be easily applied to other datasets presenting the similar structures. They are therefore applicable to many fields including but not limited to Bioinformatics and Computer Science.
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Zuo, Yiming. "Differential Network Analysis based on Omic Data for Cancer Biomarker Discovery." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/78217.

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Recent advances in high-throughput technique enables the generation of a large amount of omic data such as genomics, transcriptomics, proteomics, metabolomics, glycomics etc. Typically, differential expression analysis (e.g., student's t-test, ANOVA) is performed to identify biomolecules (e.g., genes, proteins, metabolites, glycans) with significant changes on individual level between biologically disparate groups (disease cases vs. healthy controls) for cancer biomarker discovery. However, differential expression analysis on independent studies for the same clinical types of patients often led to different sets of significant biomolecules and had only few in common. This may be attributed to the fact that biomolecules are members of strongly intertwined biological pathways and highly interactive with each other. Without considering these interactions, differential expression analysis could lead to biased results. Network-based methods provide a natural framework to study the interactions between biomolecules. Commonly used data-driven network models include relevance network, Bayesian network and Gaussian graphical models. In addition to data-driven network models, there are many publicly available databases such as STRING, KEGG, Reactome, and ConsensusPathDB, where one can extract various types of interactions to build knowledge-driven networks. While both data- and knowledge-driven networks have their pros and cons, an appropriate approach to incorporate the prior biological knowledge from publicly available databases into data-driven network model is desirable for more robust and biologically relevant network reconstruction. Recently, there has been a growing interest in differential network analysis, where the connection in the network represents a statistically significant change in the pairwise interaction between two biomolecules in different groups. From the rewiring interactions shown in differential networks, biomolecules that have strongly altered connectivity between distinct biological groups can be identified. These biomolecules might play an important role in the disease under study. In fact, differential expression and differential network analyses investigate omic data from two complementary perspectives: the former focuses on the change in individual biomolecule level between different groups while the latter concentrates on the change in pairwise biomolecules level. Therefore, an approach that can integrate differential expression and differential network analyses is likely to discover more reliable and powerful biomarkers. To achieve these goals, we start by proposing a novel data-driven network model (i.e., LOPC) to reconstruct sparse biological networks. The sparse networks only contains direct interactions between biomolecules which can help researchers to focus on the more informative connections. Then we propose a novel method (i.e., dwgLASSO) to incorporate prior biological knowledge into data-driven network model to build biologically relevant networks. Differential network analysis is applied based on the networks constructed for biologically disparate groups to identify cancer biomarker candidates. Finally, we propose a novel network-based approach (i.e., INDEED) to integrate differential expression and differential network analyses to identify more reliable and powerful cancer biomarker candidates. INDEED is further expanded as INDEED-M to utilize omic data at different levels of human biological system (e.g., transcriptomics, proteomics, metabolomics), which we believe is promising to increase our understanding of cancer. Matlab and R packages for the proposed methods are developed and available at Github (https://github.com/Hurricaner1989) to share with the research community.
Ph. D.
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Tsai, Tsung-Heng. "Bayesian Alignment Model for Analysis of LC-MS-based Omic Data." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/64151.

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Liquid chromatography coupled with mass spectrometry (LC-MS) has been widely used in various omic studies for biomarker discovery. Appropriate LC-MS data preprocessing steps are needed to detect true differences between biological groups. Retention time alignment is one of the most important yet challenging preprocessing steps, in order to ensure that ion intensity measurements among multiple LC-MS runs are comparable. In this dissertation, we propose a Bayesian alignment model (BAM) for analysis of LC-MS data. BAM uses Markov chain Monte Carlo (MCMC) methods to draw inference on the model parameters and provides estimates of the retention time variability along with uncertainty measures, enabling a natural framework to integrate information of various sources. From methodology development to practical application, we investigate the alignment problem through three research topics: 1) development of single-profile Bayesian alignment model, 2) development of multi-profile Bayesian alignment model, and 3) application to biomarker discovery research. Chapter 2 introduces the profile-based Bayesian alignment using a single chromatogram, e.g., base peak chromatogram from each LC-MS run. The single-profile alignment model improves on existing MCMC-based alignment methods through 1) the implementation of an efficient MCMC sampler using a block Metropolis-Hastings algorithm, and 2) an adaptive mechanism for knot specification using stochastic search variable selection (SSVS). Chapter 3 extends the model to integrate complementary information that better captures the variability in chromatographic separation. We use Gaussian process regression on the internal standards to derive a prior distribution for the mapping functions. In addition, a clustering approach is proposed to identify multiple representative chromatograms for each LC-MS run. With the Gaussian process prior, these chromatograms are simultaneously considered in the profile-based alignment, which greatly improves the model estimation and facilitates the subsequent peak matching process. Chapter 4 demonstrates the applicability of the proposed Bayesian alignment model to biomarker discovery research. We integrate the proposed Bayesian alignment model into a rigorous preprocessing pipeline for LC-MS data analysis. Through the developed analysis pipeline, candidate biomarkers for hepatocellular carcinoma (HCC) are identified and confirmed on a complementary platform.
Ph. D.
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Ruffalo, Matthew M. "Algorithms for Constructing Features for Integrated Analysis of Disparate Omic Data." Case Western Reserve University School of Graduate Studies / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=case1449238712.

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Elhezzani, Najla Saad R. "New statistical methodologies for improved analysis of genomic and omic data." Thesis, King's College London (University of London), 2018. https://kclpure.kcl.ac.uk/portal/en/theses/new-statistical-methodologies-for-improved-analysis-of-genomic-and-omic-data(eb8d95f4-e926-4c54-984f-94d86306525a).html.

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We develop statistical tools for analyzing different types of phenotypic data in genome-wide settings. When the phenotype of interest is a binary case-control status, most genome-wide association studies (GWASs) use randomly selected samples from the population (hereafter bases) as the control set. This approach is successful when the trait of interest is very rare; otherwise, a loss in the statistical power to detect disease-associated variants is expected. To address this, we propose a joint analysis of the three types of samples; cases, bases and controls. This is done by modeling the bases as a mixture of multinomial logistic functions of cases and controls, according to disease prevalence. In a typical GWAS, where thousands of single-nucleotide polymorphisms (SNPs) are available for testing, score-based test statistics are ideal in this case. Other tests of associations such as Wald’s and likelihood ratio tests are known to be asymptotically equivalent to the score test, however their performance under small sample sizes can vary significantly. In order to allow the test comparison to be performed under the proposed case-base-control (CBC) design, we provide an estimation procedure using the maximum likelihood (ML) method along with the expectation-maximization (EM) algorithm. Simulations show that combining the three samples can increase the power to detect disease-associated variants, though a very large base sample set can compensate for lack of controls. In the second part of the thesis, we consider a joint analysis of both genome-wide SNPs as well as multiple phenotypes, with a focus on the challenges they present in the estimation of SNP heritability. The current standard for performing this task is fit-ting a variance component model, despite its tendency to produce boundary estimates when small sample sizes are used. We propose a Bayesian covariance component model (BCCM) that takes into account genetic correlation among phenotypes and genetic correlation among individuals. The use of Bayesian methods allows us to circumvent some issues related to small sample sizes, mainly overfitting and boundary estimates. Using gene expression pathways, we demonstrate a significant improvement in SNP heritability estimates over univariate and ML-based methods, thus explaining why recent progress in eQTL identification has been limited. I published this work as an article in the European Journal of Human genetics. In the third part of the thesis, we study the prospects of using the proposed BCCM for phenotype prediction. Results from real data show consistency in accuracy between ML based methods and the proposed Bayesian method, when effect sizes are estimated using their posterior mode. It is also noted that an initial imputation step relatively increases the predictive accuracy.
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Elsheikh, Samar Salah Mohamedahmed. "Integration of multi-omic data and neuroimaging characteristics in studying brain related diseases." Doctoral thesis, Faculty of Health Sciences, 2020. http://hdl.handle.net/11427/32609.

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Approaches to the identification of genetic variants associated with complex brain diseases have evolved in recent decades. This evolution was supported by advancements in medical imaging and genotyping technologies that result in rich data production in the field of imaging genetics and radiogenomics. Studies in these fields have taken different designs and directions from genomewide associations to studying the complex interplay between genetics and structural connectivity of a wide range of brain-related diseases. Nevertheless, such combinations of heterogeneous, high dimensional and inter-related data has introduced new challenges which cannot be handled with traditional statistical methods. In this thesis, we proposed analysis pipelines and methodologies to study the causal relationship between neuroimaging features, including tumour characteristics and connectomics, genetics and clinical factors in brain-related diseases. In doing so, we adopted two longitudinal study designs and modelled the association between Alzheimer's disease progression and genetic factors, utilising local and global brain connectivity networks. In addition to that, we performed a multi-stage radiogenomic analysis in glioblastoma using non-parametric statistical methods. To address some limitations in the methods, we adopted the Structural Equation Model and developed a mathematical model to examine the inter-correlation between neuroimaging and multi-omic characteristics of brain-related diseases. Our findings have successfully identified risk genes that were previously reported in the literature of Alzheimer's and glioblastoma diseases, and discovered potential risk variants which associate with disease progression. More specifically, we found some loci in the genes CDH18, ANTXR2 and IGF1, located in Chromosomes 5, 4 and 12, to have effect on the brain connectivity over time in Alzheimer's disease. We also found that the expression of APP, HFE, PLAU and BLMH have significant effects on the structural connectivity of local areas in the brain, these are the left Heschl gyrus, right anterior cingulate gyrus, left fusiform gyrus and left Heschl gyrus, respectively. These potential association patterns could be useful for early disease diagnosis, treatment and neurodegeneration prediction. More importantly, we identified gaps in the imaging genetics methodologies, we proposed a mathematical model accounting for these limitations and evaluated the model which produced promising results. Our proposed flexible model, BiGen, addresses the gaps in the existing tools by combining neuroimaging, genetics, environmental, and phenotype information to a single complex analysis, accounting for the heterogeneity, inter-correlation, and non-linearity of the variables. Moreover, BiGen adopts an important assumption which is hardly met in the literature of imaging genetics, and that is, all the four variables are assumed to be latent constructs, that means they can not be observed directly from the data, and are measured through observed indicators. This is an important assumption in both neuroimaging, behavioural and genetic studies, and it is one of the reasons why BiGen is flexible and can easily be extended to include more indicators and latent constructs in the context of brain-related diseases.
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Ehrenberger, Tobias. "Cancer systems biology : functional insights and therapeutic strategies for medulloblastoma from omic data integration." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/123062.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biological Engineering, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 151-167).
Medulloblastoma (MB) is a chiefly pediatric cancer of the cerebellum that has been studied extensively using genomic, epigenomic, and transcriptomic data. It comprises at least four molecularly distinct subgroups: WNT, SHH, Group 3, and Group 4. Despite the detailed characterization of MB, many disease-driving events remain to be elucidated and therapeutic targets to be nominated. In this thesis, we describe three studies that contribute to a better understanding of this devastating disease: First, we describe a study that aims to fully describe the genomic landscape in the largest medulloblastoma cohort to date, using 491 sequenced MB tumors and 1,256 epigenetically analyzed cases. This work describes subgroup-specific driver alterations including previously unappreciated actionable targets; and, based on epigenetic data, identifies further heterogeneity within Group 3 and Group 4 tumors. Second, we focus on the proteomes and phospho-proteomes of 45 medulloblastoma samples.
We identified distinct pathways associated with two subsets of SHH tumors that showed robustly distinct proteomes, but similar transcriptomes, and found post-translational modifications of MYC that are associated with poor outcomes in Group 3 tumors. We also found kinases associated with subtypes and showed that inhibiting PRKDC sensitizes MYC-driven cells to radiation. This study shows that proteomics enables a more comprehensive, functional readout, providing a foundation for future therapeutic strategies. Third, we characterize the metabolomic space of MB on largely the same 45 tumors as used in the proteome-focused study. Here, we present preliminary insights from derived from integrative network and other analyses. We find that MB consensus subgroups are preserved in metabolic space, and that certain classes of metabolites are elevated in MYC-activated MB.
We also show that, similar to other cancers, a previously described gain-of-function mutation in IDH1 may cause elevated 2-hydroxyglutarate levels in MB. The work described in this thesis significantly enhances previous knowledge of medulloblastoma and its subgroups, and provides insights that may aid in the development of medulloblastoma therapies in the near future.
by Tobias Ehrenberger.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Department of Biological Engineering
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Curti, Nico. "Implementazione e benchmarking dell'algoritmo QDANet PRO per l'analisi di big data genomici." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/12018/.

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Dato il recente avvento delle tecnologie NGS, in grado di sequenziare interi genomi umani in tempi e costi ridotti, la capacità di estrarre informazioni dai dati ha un ruolo fondamentale per lo sviluppo della ricerca. Attualmente i problemi computazionali connessi a tali analisi rientrano nel topic dei Big Data, con databases contenenti svariati tipi di dati sperimentali di dimensione sempre più ampia. Questo lavoro di tesi si occupa dell'implementazione e del benchmarking dell'algoritmo QDANet PRO, sviluppato dal gruppo di Biofisica dell'Università di Bologna: il metodo consente l'elaborazione di dati ad alta dimensionalità per l'estrazione di una Signature a bassa dimensionalità di features con un'elevata performance di classificazione, mediante una pipeline d'analisi che comprende algoritmi di dimensionality reduction. Il metodo è generalizzabile anche all'analisi di dati non biologici, ma caratterizzati comunque da un elevato volume e complessità, fattori tipici dei Big Data. L'algoritmo QDANet PRO, valutando la performance di tutte le possibili coppie di features, ne stima il potere discriminante utilizzando un Naive Bayes Quadratic Classifier per poi determinarne il ranking. Una volta selezionata una soglia di performance, viene costruito un network delle features, da cui vengono determinate le componenti connesse. Ogni sottografo viene analizzato separatamente e ridotto mediante metodi basati sulla teoria dei networks fino all'estrapolazione della Signature finale. Il metodo, già precedentemente testato su alcuni datasets disponibili al gruppo di ricerca con riscontri positivi, è stato messo a confronto con i risultati ottenuti su databases omici disponibili in letteratura, i quali costituiscono un riferimento nel settore, e con algoritmi già esistenti che svolgono simili compiti. Per la riduzione dei tempi computazionali l'algoritmo è stato implementato in linguaggio C++ su HPC, con la parallelizzazione mediante librerie OpenMP delle parti più critiche.
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Arsenteva, Polina. "Statistical modeling and analysis of radio-induced adverse effects based on in vitro and in vivo data." Electronic Thesis or Diss., Bourgogne Franche-Comté, 2023. http://www.theses.fr/2023UBFCK074.

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Dans ce travail nous abordons le problème des effets indésirables induits par la radiothérapie sur les tissus sains. L'objectif est de proposer un cadre mathématique pour comparer les effets de différentes modalités d'irradiation, afin de pouvoir éventuellement choisir les traitements qui produisent le moins d'effets indésirables pour l’utilisation potentielle en clinique. Les effets secondaires sont étudiés dans le cadre de deux types de données : en termes de réponse omique in vitro des cellules endothéliales humaines, et en termes d'effets indésirables observés sur des souris dans le cadre d'expérimentations in vivo. Dans le cadre in vitro, nous rencontrons le problème de l'extraction d'informations clés à partir de données temporelles complexes qui ne peuvent pas être traitées avec les méthodes disponibles dans la littérature. Nous modélisons le fold change radio-induit, l'objet qui code la différence d'effet de deux conditions expérimentales, d’une manière qui permet de prendre en compte les incertitudes des mesures ainsi que les corrélations entre les entités observées. Nous construisons une distance, avec une généralisation ultérieure à une mesure de dissimilarité, permettant de comparer les fold changes en termes de toutes leurs propriétés statistiques importantes. Enfin, nous proposons un algorithme computationnellement efficace effectuant le clustering joint avec l'alignement temporel des fold changes. Les caractéristiques clés extraites de ces dernières sont visualisées à l'aide de deux types de représentations de réseau, dans le but de faciliter l'interprétation biologique. Dans le cadre in vivo, l’enjeu statistique est d’établir un lien prédictif entre des variables qui, en raison des spécificités du design expérimental, ne pourront jamais être observées sur les mêmes animaux. Dans le contexte de ne pas avoir accès aux lois jointes, nous exploitons les informations supplémentaires sur les groupes observés pour déduire le modèle de régression linéaire. Nous proposons deux estimateurs des paramètres de régression, l'un basé sur la méthode des moments et l'autre basé sur le transport optimal, ainsi que des estimateurs des intervalles de confiance basés sur le bootstrap stratifié
In this work we address the problem of adverse effects induced by radiotherapy on healthy tissues. The goal is to propose a mathematical framework to compare the effects of different irradiation modalities, to be able to ultimately choose those treatments that produce the minimal amounts of adverse effects for potential use in the clinical setting. The adverse effects are studied in the context of two types of data: in terms of the in vitro omic response of human endothelial cells, and in terms of the adverse effects observed on mice in the framework of in vivo experiments. In the in vitro setting, we encounter the problem of extracting key information from complex temporal data that cannot be treated with the methods available in literature. We model the radio-induced fold change, the object that encodes the difference in the effect of two experimental conditions, in the way that allows to take into account the uncertainties of measurements as well as the correlations between the observed entities. We construct a distance, with a further generalization to a dissimilarity measure, allowing to compare the fold changes in terms of all the important statistical properties. Finally, we propose a computationally efficient algorithm performing clustering jointly with temporal alignment of the fold changes. The key features extracted through the latter are visualized using two types of network representations, for the purpose of facilitating biological interpretation. In the in vivo setting, the statistical challenge is to establish a predictive link between variables that, due to the specificities of the experimental design, can never be observed on the same animals. In the context of not having access to joint distributions, we leverage the additional information on the observed groups to infer the linear regression model. We propose two estimators of the regression parameters, one based on the method of moments and the other based on optimal transport, as well as the estimators for the confidence intervals based on the stratified bootstrap procedure
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Books on the topic "Omic data"

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Azuaje, Francisco. Bioinformatics and biomarker discovery: "omic" data analysis for personalised medicine. Hoboken, NJ: John Wiley & Sons, 2010.

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Azuaje, Francisco. Bioinformatics and biomarker discovery: "omic" data analysis for personalised medicine. Hoboken, NJ: John Wiley & Sons, 2010.

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Azuaje, Francisco. Bioinformatics and biomarker discovery: "omic" data analysis for personalised medicine. Hoboken, NJ: John Wiley & Sons, 2010.

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Mayer, Bernd, ed. Bioinformatics for Omics Data. Totowa, NJ: Humana Press, 2011. http://dx.doi.org/10.1007/978-1-61779-027-0.

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Ning, Kang, ed. Methodologies of Multi-Omics Data Integration and Data Mining. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8210-1.

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Alkhateeb, Abedalrhman, and Luis Rueda, eds. Machine Learning Methods for Multi-Omics Data Integration. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-36502-7.

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Tieri, Paolo, Christine Nardini, and Jennifer Elizabeth Dent, eds. Multi-omic Data Integration. Frontiers Media SA, 2015. http://dx.doi.org/10.3389/978-2-88919-648-7.

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Romualdi, Chiara, Enrica Calura, Davide Risso, Sampsa Hautaniemi, and Francesca Finotello, eds. Multi-omic Data Integration in Oncology. Frontiers Media SA, 2020. http://dx.doi.org/10.3389/978-2-88966-151-0.

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Data Analysis for Omic Sciences: Methods and Applications. Elsevier, 2018. http://dx.doi.org/10.1016/s0166-526x(18)x0004-x.

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Jaumot, Joaquim, Carmen Bedia, and Romà Tauler. Data Analysis for Omic Sciences: Methods and Applications. Elsevier, 2018.

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Book chapters on the topic "Omic data"

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Saitou, Naruya. "Omic Data Collection." In Introduction to Evolutionary Genomics, 281–88. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5304-7_12.

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Mason, Christopher E., Sandra G. Porter, and Todd M. Smith. "Characterizing Multi-omic Data in Systems Biology." In Systems Analysis of Human Multigene Disorders, 15–38. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8778-4_2.

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Xu, Ying, Juan Cui, and David Puett. "Omic Data, Information Derivable and Computational Needs." In Cancer Bioinformatics, 41–63. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1381-7_2.

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Zou, Yan. "Analyzing Multi-Omic Data with Integrative Platforms." In Integrative Bioinformatics, 377–86. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6795-4_18.

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Warrenfeltz, Susanne, and Jessica C. Kissinger. "Accessing Cryptosporidium Omic and Isolate Data via CryptoDB.org." In Methods in Molecular Biology, 139–92. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9748-0_10.

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Reverter, Ferran, Esteban Vegas, and Josep M. Oller. "Kernel Conditional Embeddings for Associating Omic Data Types." In Bioinformatics and Biomedical Engineering, 501–10. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78723-7_43.

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Kalapanulak, Saowalak, Treenut Saithong, and Chinae Thammarongtham. "Networking Omic Data to Envisage Systems Biological Regulation." In Advances in Biochemical Engineering/Biotechnology, 121–41. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/10_2016_38.

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Xu, Ying, Juan Cui, and David Puett. "Elucidation of Cancer Drivers Through Comparative Omic Data Analyses." In Cancer Bioinformatics, 113–47. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1381-7_5.

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Warrenfeltz, Susanne, and Jessica C. Kissinger. "Correction to: Accessing Cryptosporidium Omic and Isolate Data via CryptoDB.org." In Methods in Molecular Biology, C1. New York, NY: Springer New York, 2020. http://dx.doi.org/10.1007/978-1-4939-9748-0_22.

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Bhattacharya, Surajit, and Heather Gordish-Dressman. "Guidelines for Bioinformatics and the Statistical Analysis of Omic Data." In Omics Approaches to Understanding Muscle Biology, 45–75. New York, NY: Springer US, 2019. http://dx.doi.org/10.1007/978-1-4939-9802-9_4.

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Conference papers on the topic "Omic data"

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Dey, Anirban, Kaushik Das Sharma, Pritha Bhattacharjee, and Amitava Chatterjee. "A Voting based Assimilation Method for the Winning Neurons in Multi-Level SOM to Cluster the Convoluted Biomarkers of a Time Varying ‘Omic Data." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725375.

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Chong, Darren, Sonit Singh, and Arcot Sowmya. "Spectrogram-Based Imagification Applying Deep Learning on Omics Data." In 2024 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 477–84. IEEE, 2024. https://doi.org/10.1109/dicta63115.2024.00076.

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Wolfgang, Seth, Skyler Ruiter, Marc Tunnell, Timothy Triche, Erin Carrier, and Zachary DeBruine. "Value-Compressed Sparse Column (VCSC): Sparse Matrix Storage for Single-cell Omics Data." In 2024 IEEE International Conference on Big Data (BigData), 4952–58. IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825091.

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Yao, Zhi-Cheng, Zi Liu, Wei-Zhong Lin, and Xuan Xiao. "Clustering of Drug Side Effects Based on Multi-Omics Data." In 2024 2nd International Conference on Computer, Vision and Intelligent Technology (ICCVIT), 1–7. IEEE, 2024. https://doi.org/10.1109/iccvit63928.2024.10872593.

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Li, Qi, Jian-Wei Su, and Wen-Hui Wu. "Clustering Single-Cell Multi-Omics Data with Graph Contrastive Learning." In 2024 International Conference on Machine Learning and Cybernetics (ICMLC), 239–44. IEEE, 2024. https://doi.org/10.1109/icmlc63072.2024.10935198.

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Shi, Tianyi, Xiucai Ye, Dong Huang, and Tetsuya Sakurai. "Selecting interpretable features for cancer subtyping on multi-omics data." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 1155–60. IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10821783.

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Mishra, Soumya Ranjan, Sachikanta Dash, Sasmita Padhy, Naween Kumar, and Yajnaseni Dash. "Integrating Multi-Omics Data for Advanced Diabetes Prediction and Understanding." In 2024 7th International Conference on Contemporary Computing and Informatics (IC3I), 1447–53. IEEE, 2024. https://doi.org/10.1109/ic3i61595.2024.10828970.

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Ciortan, Madalina, and Matthieu Defrance. "Optimization algorithm for omic data subspace clustering." In CSBio2021: The 12th International Conference on Computational Systems-Biology and Bioinformatics. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3486713.3486742.

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Zuo, Yiming, Guoqiang Yu, Chi Zhang, and Habtom W. Ressom. "A new approach for multi-omic data integration." In 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2014. http://dx.doi.org/10.1109/bibm.2014.6999157.

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Glass, Kimberly. "Using Multi-Omic Data to Model Gene Regulatory Networks." In Genetoberfest 2023. ScienceOpen, 2023. http://dx.doi.org/10.14293/gof.23.03.

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Reports on the topic "Omic data"

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Mitchell, Hugh, and Jennifer Kyle. Full Integration of Lipidomics Data into Multi-OMIC Functional Enrichment. Office of Scientific and Technical Information (OSTI), November 2019. http://dx.doi.org/10.2172/1986189.

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Huang, Lei, Meng Song, Hui Shen, Huixiao Hong, Ping Gong, Deng Hong-Wen, and Zhang Chaoyang. Deep learning methods for omics data imputation. Engineer Research and Development Center (U.S.), February 2024. http://dx.doi.org/10.21079/11681/48221.

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One common problem in omics data analysis is missing values, which can arise due to various reasons, such as poor tissue quality and insufficient sample volumes. Instead of discarding missing values and related data, imputation approaches offer an alternative means of handling missing data. However, the imputation of missing omics data is a non-trivial task. Difficulties mainly come from high dimensionality, non-linear or nonmonotonic relationships within features, technical variations introduced by sampling methods, sample heterogeneity, and the non-random missingness mechanism. Several advanced imputation methods, including deep learning-based methods, have been proposed to address these challenges. Due to its capability of modeling complex patterns and relationships in large and high-dimensional datasets, many researchers have adopted deep learning models to impute missing omics data. This review provides a comprehensive overview of the currently available deep learning-based methods for omics imputation from the perspective of deep generative model architectures such as autoencoder, variational autoencoder, generative adversarial networks, and Transformer, with an emphasis on multi-omics data imputation. In addition, this review also discusses the opportunities that deep learning brings and the challenges that it might face in this field.
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Iudicone, Daniele, and Marina Montresor. Omics community protocols. EuroSea, 2023. http://dx.doi.org/10.3289/eurosea_d3.19.

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The aim of the WP3 “Network Integration and Improvements” is to coordinate and enhance key aspects of integration of European observing technology (and related data flows) for its use in the context of international ocean monitoring activities. One of the dimensions of the integrations is the constitution of thematic networks, that is, networks whose aim is to address specific observational challenges and thus to favor innovation, innovation that will ultimately support the Blue economy. In this context, the specific aim of Task 3.8 is to accelerate the adoption of molecular methods such as genomic, transcriptomic (and related “omics”) approaches, currently used as monitoring tools in human health, to the assessment of the state and change of marine ecosystems. It was designed to favor the increase the capacity to evaluate biological diversity and the organismal metabolic states in different environmental conditions by the development of “augmented observatories”, utilizing state-of-art methodologies in genomic-enabled research at multidisciplinary observatories at well-established marine LTERs, with main focus on a mature oceanographic observatory in Naples, NEREA. In addition, an effort is dedicated to connecting existing observatories that intend to augment their observations with molecular tools. Molecular approaches come with many different options for the protocols (size fractioning, sample collection and storage, sequencing etc). One main challenge in systematically implementing those approaches is thus their standardization across observatories. Based on a survey of existing methods and on a 3-year experience in collecting, sequencing and analyzing molecular data, this deliverable is thus dedicated to present the SOPs implemented and tested at NEREA. The SOPs consider a size fractioning of the biological material to avoid biases toward more abundant, smaller organisms such as bacteria. They cover both the highly stable DNA and the less stable RNA and they are essentially an evolution of the ones developed for the highly successful Tara Oceans Expedition and recently updated for the Expedition Mission Microbiomes, an All-Atlantic expedition organised and executed by the EU AtlantECO project. Importantly, they have only slight variations with respect the ones adopted by the network of genomic observatories EMOBON. Discussions are ongoing with EMOBON to perfectly align the protocols. The SOPs are being disseminated via the main national and international networks. (EuroSea Deliverable, D3.19)
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Sanderson, William. 'Omics and Big Data in Harmful Algal Bloom Research. Office of Scientific and Technical Information (OSTI), August 2024. http://dx.doi.org/10.2172/2438485.

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Hafen, Ryan, Lisa Bramer, Lee Ann McCue, Rachel Richardson, and Chris Ebsch. MODE: The Multi-Omics Data Exploration Platform Phase I Final Technical Report. Office of Scientific and Technical Information (OSTI), December 2019. http://dx.doi.org/10.2172/1630300.

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Wheeler, Travis. Machine learning approaches for integrating multi-omics data to expand microbiome annotation. Office of Scientific and Technical Information (OSTI), April 2024. http://dx.doi.org/10.2172/2331432.

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Engel, Jasper, and Hilko van der Voet. G-TwYST harmonisation of statistical methods for use of omics data in food safety assessment. Wageningen: Biometris, Wageningen University & Research, 2018. http://dx.doi.org/10.18174/455159.

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Wrinn, Michael. Platform for efficient large-scale storage and analysis of multi-omics data in plant and microbial systems. Final Technical Report. Office of Scientific and Technical Information (OSTI), September 2020. http://dx.doi.org/10.2172/1659436.

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Solis-Lemus, Claudia. Harnessing the power of big omics data: Novel statistical tools to study the role of microbial communities in fundamental biological processes. Office of Scientific and Technical Information (OSTI), January 2024. http://dx.doi.org/10.2172/2274956.

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Holmes, Rebecca, Keeley Blackie, Ilya Ivlev, and Erick H. Turner. Enhancing Systematic Review Methods by Incorporating Unpublished Drug Trials. Agency for Healthcare Research and Quality (AHRQ), January 2025. https://doi.org/10.23970/ahrqepcwhitepaperenhancing.

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Systematic reviews often omit unpublished data due to time constraints, unclear data integration methodologies, and uncertainties about the value of including these data. This can lead to inflated efficacy estimates and underestimated harms or burdens, as unpublished studies often document negative efficacy outcomes and/or harms. This brief methods project demonstrated the integration of U.S. Food and Drug Administration (FDA) drug approval packages (i.e., FDA reviews) into three systematic reviews focusing on psychoactive drugs—paroxetine (Paxil®) for adults with post-traumatic stress disorder (PTSD), escitalopram for major depressive disorder (MDD) in adolescents, and aripiprazole for bipolar disorder in adults. We searched the FDA reviews and matched identified trials with those in the Agency for Healthcare Research and Quality’s Effective Health Care Program systematic reviews. Then, we conducted meta-analyses combining unpublished and published studies. Our analysis identified important discrepancies in effect sizes, with meta-analyses of both published and unpublished data for paroxetine showing smaller effect sizes than those using only published trials. Our findings suggest a potential overestimation of paroxetine efficacy in published literature. Incorporating FDA reviews could enhance the accuracy of effect estimates in systematic reviews. However, using unpublished data requires careful consideration due to the intensive resources required. Future research should expand to other therapeutic areas and include more sources of unpublished data to increase the robustness of systematic reviews.
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