Relevant bibliographies by topics / Omics data analysi

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  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Omics data analysi'

Author: Grafiati
Published: 9 March 2023
Last updated: 10 March 2023

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Journal articles on the topic "Omics data analysi"

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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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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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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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Madrid-Márquez, Laura, Cristina Rubio-Escudero, Beatriz Pontes, Antonio González-Pérez, José C. Riquelme, and Maria E. Sáez. "MOMIC: A Multi-Omics Pipeline for Data Analysis, Integration and Interpretation." Applied Sciences 12, no. 8 (April 14, 2022): 3987. http://dx.doi.org/10.3390/app12083987.

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Background and Objectives: The burst of high-throughput omics technologies has given rise to a new era in systems biology, offering an unprecedented scenario for deriving meaningful biological knowledge through the integration of different layers of information. Methods: We have developed a new software tool, MOMIC, that guides the user through the application of different analysis on a wide range of omic data, from the independent single-omics analysis to the combination of heterogeneous data at different molecular levels. Results: The proposed pipeline is developed as a collection of Jupyter notebooks, easily editable, reproducible and well documented. It can be modified to accommodate new analysis workflows and data types. It is accessible via momic.us.es, and as a docker project available at github that can be locally installed. Conclusions: MOMIC offers a complete analysis environment for analysing and integrating multi-omics data in a single, easy-to-use platform.
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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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Yang, Xiaoxi, Yuqi Wen, Xinyu Song, Song He, and Xiaochen Bo. "Exploring the classification of cancer cell lines from multiple omic views." PeerJ 8 (August 18, 2020): e9440. http://dx.doi.org/10.7717/peerj.9440.

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Background Cancer classification is of great importance to understanding its pathogenesis, making diagnosis and developing treatment. The accumulation of extensive omics data of abundant cancer cell line provide basis for large scale classification of cancer with low cost. However, the reliability of cell lines as in vitro models of cancer has been controversial. Methods In this study, we explore the classification on pan-cancer cell line with single and integrated multiple omics data from the Cancer Cell Line Encyclopedia (CCLE) database. The representative omics data of cancer, mRNA data, miRNA data, copy number variation data, DNA methylation data and reverse-phase protein array data were taken into the analysis. TumorMap web tool was used to illustrate the landscape of molecular classification.The molecular classification of patient samples was compared with cancer cell lines. Results Eighteen molecular clusters were identified using integrated multiple omics clustering. Three pan-cancer clusters were found in integrated multiple omics clustering. By comparing with single omics clustering, we found that integrated clustering could capture both shared and complementary information from each omics data. Omics contribution analysis for clustering indicated that, although all the five omics data were of value, mRNA and proteomics data were particular important. While the classifications were generally consistent, samples from cancer patients were more diverse than cancer cell lines. Conclusions The clustering analysis based on integrated omics data provides a novel multi-dimensional map of cancer cell lines that can reflect the extent to pan-cancer cell lines represent primary tumors, and an approach to evaluate the importance of omic features in cancer classification.
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Chauvel, Cécile, Alexei Novoloaca, Pierre Veyre, Frédéric Reynier, and Jérémie Becker. "Evaluation of integrative clustering methods for the analysis of multi-omics data." Briefings in Bioinformatics 21, no. 2 (February 14, 2019): 541–52. http://dx.doi.org/10.1093/bib/bbz015.

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Abstract Recent advances in sequencing, mass spectrometry and cytometry technologies have enabled researchers to collect large-scale omics data from the same set of biological samples. The joint analysis of multiple omics offers the opportunity to uncover coordinated cellular processes acting across different omic layers. In this work, we present a thorough comparison of a selection of recent integrative clustering approaches, including Bayesian (BCC and MDI) and matrix factorization approaches (iCluster, moCluster, JIVE and iNMF). Based on simulations, the methods were evaluated on their sensitivity and their ability to recover both the correct number of clusters and the simulated clustering at the common and data-specific levels. Standard non-integrative approaches were also included to quantify the added value of integrative methods. For most matrix factorization methods and one Bayesian approach (BCC), the shared and specific structures were successfully recovered with high and moderate accuracy, respectively. An opposite behavior was observed on non-integrative approaches, i.e. high performances on specific structures only. Finally, we applied the methods on the Cancer Genome Atlas breast cancer data set to check whether results based on experimental data were consistent with those obtained in the simulations.
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Alizadeh, Madeline, Natalia Sampaio Moura, Alyssa Schledwitz, Seema A. Patil, Jacques Ravel, and Jean-Pierre Raufman. "Big Data in Gastroenterology Research." International Journal of Molecular Sciences 24, no. 3 (January 27, 2023): 2458. http://dx.doi.org/10.3390/ijms24032458.

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Studying individual data types in isolation provides only limited and incomplete answers to complex biological questions and particularly falls short in revealing sufficient mechanistic and kinetic details. In contrast, multi-omics approaches to studying health and disease permit the generation and integration of multiple data types on a much larger scale, offering a comprehensive picture of biological and disease processes. Gastroenterology and hepatobiliary research are particularly well-suited to such analyses, given the unique position of the luminal gastrointestinal (GI) tract at the nexus between the gut (mucosa and luminal contents), brain, immune and endocrine systems, and GI microbiome. The generation of ‘big data’ from multi-omic, multi-site studies can enhance investigations into the connections between these organ systems and organisms and more broadly and accurately appraise the effects of dietary, pharmacological, and other therapeutic interventions. In this review, we describe a variety of useful omics approaches and how they can be integrated to provide a holistic depiction of the human and microbial genetic and proteomic changes underlying physiological and pathophysiological phenomena. We highlight the potential pitfalls and alternatives to help avoid the common errors in study design, execution, and analysis. We focus on the application, integration, and analysis of big data in gastroenterology and hepatobiliary research.
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Misra, Biswapriya B., Carl Langefeld, Michael Olivier, and Laura A. Cox. "Integrated omics: tools, advances and future approaches." Journal of Molecular Endocrinology 62, no. 1 (January 2019): R21—R45. http://dx.doi.org/10.1530/jme-18-0055.

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With the rapid adoption of high-throughput omic approaches to analyze biological samples such as genomics, transcriptomics, proteomics and metabolomics, each analysis can generate tera- to peta-byte sized data files on a daily basis. These data file sizes, together with differences in nomenclature among these data types, make the integration of these multi-dimensional omics data into biologically meaningful context challenging. Variously named as integrated omics, multi-omics, poly-omics, trans-omics, pan-omics or shortened to just ‘omics’, the challenges include differences in data cleaning, normalization, biomolecule identification, data dimensionality reduction, biological contextualization, statistical validation, data storage and handling, sharing and data archiving. The ultimate goal is toward the holistic realization of a ‘systems biology’ understanding of the biological question. Commonly used approaches are currently limited by the 3 i’s – integration, interpretation and insights. Post integration, these very large datasets aim to yield unprecedented views of cellular systems at exquisite resolution for transformative insights into processes, events and diseases through various computational and informatics frameworks. With the continued reduction in costs and processing time for sample analyses, and increasing types of omics datasets generated such as glycomics, lipidomics, microbiomics and phenomics, an increasing number of scientists in this interdisciplinary domain of bioinformatics face these challenges. We discuss recent approaches, existing tools and potential caveats in the integration of omics datasets for development of standardized analytical pipelines that could be adopted by the global omics research community.
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Pan, Jianqiao, Baoshan Ma, Xiaoyu Hou, Chongyang Li, Tong Xiong, Yi Gong, and Fengju Song. "The construction of transcriptional risk scores for breast cancer based on lightGBM and multiple omics data." Mathematical Biosciences and Engineering 19, no. 12 (2022): 12353–70. http://dx.doi.org/10.3934/mbe.2022576.

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<abstract> <sec><title>Background</title><p>Polygenic risk score (PRS) can evaluate the individual-level genetic risk of breast cancer. However, standalone single nucleotide polymorphisms (SNP) data used for PRS may not provide satisfactory prediction accuracy. Additionally, current PRS models based on linear regression have insufficient power to leverage non-linear effects from thousands of associated SNPs. Here, we proposed a transcriptional risk score (TRS) based on multiple omics data to estimate the risk of breast cancer.</p> </sec> <sec><title>Methods</title><p>The multiple omics data and clinical data of breast invasive carcinoma (BRCA) were collected from the cancer genome atlas (TCGA) and the gene expression omnibus (GEO). First, we developed a novel TRS model for BRCA utilizing single omic data and LightGBM algorithm. Subsequently, we built a combination model of TRS derived from each omic data to further improve the prediction accuracy. Finally, we performed association analysis and prognosis prediction to evaluate the utility of the TRS generated by our method.</p> </sec> <sec><title>Results</title><p>The proposed TRS model achieved better predictive performance than the linear models and other ML methods in single omic dataset. An independent validation dataset also verified the effectiveness of our model. Moreover, the combination of the TRS can efficiently strengthen prediction accuracy. The analysis of prevalence and the associations of the TRS with phenotypes including case-control and cancer stage indicated that the risk of breast cancer increases with the increases of TRS. The survival analysis also suggested that TRS for the cancer stage is an effective prognostic metric of breast cancer patients.</p> </sec> <sec><title>Conclusions</title><p>Our proposed TRS model expanded the current definition of PRS from standalone SNP data to multiple omics data and outperformed the linear models, which may provide a powerful tool for diagnostic and prognostic prediction of breast cancer.</p> </sec> </abstract>
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Dissertations / Theses on the topic "Omics data analysi"

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MASPERO, DAVIDE. "Computational strategies to dissect the heterogeneity of multicellular systems via multiscale modelling and omics data analysis." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2022. http://hdl.handle.net/10281/368331.

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L'eterogeneità pervade i sistemi biologici e si manifesta in differenze strutturali e funzionali osservate sia tra diversi individui di uno stesso gruppo (es. organismi o patologie), sia fra gli elementi costituenti di un singolo individuo (es. cellule). Lo studio dell’eterogeneità dei sistemi biologici e, in particolare, di quelli multicellulari è fondamentale per la comprensione meccanicistica di fenomeni fisiologici e patologici complessi (es. il cancro), così come per la definizione di strategie prognostiche, diagnostiche e terapeutiche efficaci. Questo lavoro è focalizzato sullo sviluppo e l’applicazione di metodi computazionali e modelli matematici per la caratterizzazione dell’eterogeneità di sistemi multicellulari e delle sottopopolazioni di cellule tumorali che sottendono l’evoluzione di una patologia neoplastica. Analoghe metodologie sono state sviluppate per caratterizzare efficacemente l’evoluzione e l’eterogeneità virale. La ricerca è suddivisa in due porzioni complementari, la prima finalizzata alla definizione di metodi per l’analisi e l’integrazione di dati omici generati da esperimenti di sequenziamento, la seconda alla modellazione e simulazione multiscala di sistemi multicellulari. Per quanto riguarda il primo filone, le tecnologie di next-generation sequencing permettono di generare enormi moli di dati omici, relativi per esempio al genoma o trascrittoma di un determinato individuo, attraverso esperimenti di bulk o single-cell sequencing. Una delle sfide principale in informatica è quella di definire metodi computazionali per estrarre informazione utile da tali dati, tenendo conto degli alti livelli di errori dato-specifico, dovuti principalmente a limiti tecnologici. In particolare, nell’ambito di questo lavoro, ci si è concentrati sullo sviluppo di metodi per l’analisi di dati di espressione genica e di mutazioni genomiche. In dettaglio, è stata effettuata una comparazione esaustiva dei metodi di machine-learning per il denoising e l’imputation di dati di single-cell RNA-sequencing. Inoltre, sono stati sviluppati metodi per il mapping dei profili di espressione su reti metaboliche, attraverso un framework innovativo che ha consentito di stratificare pazienti oncologici in base al loro metabolismo. Una successiva estensione del metodo ha permesso di analizzare la distribuzione dei flussi metabolici all'interno di una popolazione di cellule, via un approccio di flux balance analysis. Per quanto riguarda l’analisi dei profili mutazionali, è stato ideato e implementato il primo metodo per la ricostruzione di modelli filogenomici a partire da dati longitudinali a risoluzione single-cell, che sfrutta un framework che combina una Markov Chain Monte Carlo con una nuova funzione di likelihood pesata. Analogamente, è stato sviluppato un framework che sfrutta i profili delle mutazioni a bassa frequenza per ricostruire filogenie robuste e probabili catene di infenzione, attraverso l’analisi dei dati di sequenziamento di campioni virali. Gli stessi profili mutazionali permettono anche di deconvolvere il segnale nelle firme associati a specifici meccanismi molecolari che generano tali mutazioni, attraverso un approccio basato su non-negative matrix factorization. La ricerca condotta per quello che riguarda la simulazione computazionale ha portato allo sviluppo di un modello multiscala, in cui la simulazione della dinamica di popolazioni cellulari, rappresentata attraverso un Cellular Potts Model, è accoppiata all'ottimizzazione di un modello metabolico associato a ciascuna cellula sintetica. Co modello è possibile rappresentare ipotesi in termini matematici e osservare proprietà emergenti da tali assunti. Infine, un primo tentativo per combinare i due approcci metodologici ha condotto all'integrazione di dati di single-cell RNA-seq all'interno del modello multiscala, consentendo di formulare ipotesi data-driven sulle proprietà emergenti del sistema.
Heterogeneity pervades biological systems and manifests itself in the structural and functional differences observed both among different individuals of the same group (e.g., organisms or disease systems) and among the constituent elements of a single individual (e.g., cells). The study of the heterogeneity of biological systems and, in particular, of multicellular systems is fundamental for the mechanistic understanding of complex physiological and pathological phenomena (e.g., cancer), as well as for the definition of effective prognostic, diagnostic, and therapeutic strategies. This work focuses on developing and applying computational methods and mathematical models for characterising the heterogeneity of multicellular systems and, especially, cancer cell subpopulations underlying the evolution of neoplastic pathology. Similar methodologies have been developed to characterise viral evolution and heterogeneity effectively. The research is divided into two complementary portions, the first aimed at defining methods for the analysis and integration of omics data generated by sequencing experiments, the second at modelling and multiscale simulation of multicellular systems. Regarding the first strand, next-generation sequencing technologies allow us to generate vast amounts of omics data, for example, related to the genome or transcriptome of a given individual, through bulk or single-cell sequencing experiments. One of the main challenges in computer science is to define computational methods to extract useful information from such data, taking into account the high levels of data-specific errors, mainly due to technological limitations. In particular, in the context of this work, we focused on developing methods for the analysis of gene expression and genomic mutation data. In detail, an exhaustive comparison of machine-learning methods for denoising and imputation of single-cell RNA-sequencing data has been performed. Moreover, methods for mapping expression profiles onto metabolic networks have been developed through an innovative framework that has allowed one to stratify cancer patients according to their metabolism. A subsequent extension of the method allowed us to analyse the distribution of metabolic fluxes within a population of cells via a flux balance analysis approach. Regarding the analysis of mutational profiles, the first method for reconstructing phylogenomic models from longitudinal data at single-cell resolution has been designed and implemented, exploiting a framework that combines a Markov Chain Monte Carlo with a novel weighted likelihood function. Similarly, a framework that exploits low-frequency mutation profiles to reconstruct robust phylogenies and likely chains of infection has been developed by analysing sequencing data from viral samples. The same mutational profiles also allow us to deconvolve the signal in the signatures associated with specific molecular mechanisms that generate such mutations through an approach based on non-negative matrix factorisation. The research conducted with regard to the computational simulation has led to the development of a multiscale model, in which the simulation of cell population dynamics, represented through a Cellular Potts Model, is coupled to the optimisation of a metabolic model associated with each synthetic cell. Using this model, it is possible to represent assumptions in mathematical terms and observe properties emerging from these assumptions. Finally, we present a first attempt to combine the two methodological approaches which led to the integration of single-cell RNA-seq data within the multiscale model, allowing data-driven hypotheses to be formulated on the emerging properties of the system.
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Wang, Zhi. "Module-Based Analysis for "Omics" Data." Thesis, North Carolina State University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3690212.

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This thesis focuses on methodologies and applications of module-based analysis (MBA) in omics studies to investigate the relationships of phenotypes and biomarkers, e.g., SNPs, genes, and metabolites. As an alternative to traditional single–biomarker approaches, MBA may increase the detectability and reproducibility of results because biomarkers tend to have moderate individual effects but significant aggregate effect; it may improve the interpretability of findings and facilitate the construction of follow-up biological hypotheses because MBA assesses biomarker effects in a functional context, e.g., pathways and biological processes. Finally, for exploratory “omics” studies, which usually begin with a full scan of a long list of candidate biomarkers, MBA provides a natural way to reduce the total number of tests, and hence relax the multiple-testing burdens and improve power.

The first MBA project focuses on genetic association analysis that assesses the main and interaction effects for sets of genetic (G) and environmental (E) factors rather than for individual factors. We develop a kernel machine regression approach to evaluate the complete effect profile (i.e., the G, E, and G-by-E interaction effects separately or in combination) and construct a kernel function for the Gene-Environmental (GE) interaction directly from the genetic kernel and the environmental kernel. We use simulation studies and real data applications to show improved performance of the Kernel Machine (KM) regression method over the commonly adapted PC regression methods across a wide range of scenarios. The largest gain in power occurs when the underlying effect structure is involved complex GE interactions, suggesting that the proposed method could be a useful and powerful tool for performing exploratory or confirmatory analyses in GxE-GWAS.

In the second MBA project, we extend the kernel machine framework developed in the first project to model biomarkers with network structure. Network summarizes the functional interplay among biological units; incorporating network information can more precisely model the biological effects, enhance the ability to detect true signals, and facilitate our understanding of the underlying biological mechanisms. In the work, we develop two kernel functions to capture different network structure information. Through simulations and metabolomics study, we show that the proposed network-based methods can have markedly improved power over the approaches ignoring network information.

Metabolites are the end products of cellular processes and reflect the ultimate responses of biology system to genetic variations or environment exposures. Because of the unique properties of metabolites, pharmcometabolomics aims to understand the underlying signatures that contribute to individual variations in drug responses and identify biomarkers that can be helpful to response predictions. To facilitate mining pharmcometabolomic data, we establish an MBA pipeline that has great practical value in detection and interpretation of signatures, which may potentially indicate a functional basis for the drug response. We illustrate the utilities of the pipeline by investigating two scientific questions in aspirin study: (1) which metabolites changes can be attributed to aspirin intake, and (2) what are the metabolic signatures that can be helpful in predicting aspirin resistance. Results show that the MBA pipeline enables us to identify metabolic signatures that are not found in preliminary single-metabolites analysis.

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Zheng, Ning. "Mediation modeling and analysis forhigh-throughput omics data." Thesis, Uppsala universitet, Statistiska institutionen, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-256318.

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There is a strong need for powerful unified statistical methods for discovering underlying genetic architecture of complex traits with the assistance of omics information. In this paper, two methods aiming to detect novel association between the human genome and complex traits using intermediate omics data are developed based on statistical mediation modeling. We demonstrate theoretically that given proper mediators, the proposed statistical mediation models have better power than genome-wide association studies (GWAS) to detect associations missed in standard GWAS that ignore the mediators. For each ofthe modeling methods in this paper, an empirical example is given, where the association between a SNP and BMI missed by standard GWAS can be discovered by mediation analysis.
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Campanella, Gianluca. "Statistical analysis of '-omics' data : developments and applications." Thesis, Imperial College London, 2015. http://hdl.handle.net/10044/1/32109.

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In recent years, increasingly efficient molecular biology techniques created new opportunities to harness large-scale repositories of biological material collected in epidemiological studies; however, methods to manipulate and analyse the wealth of information thus generated have lagged behind. The introductory chapter of this thesis presents the multifaceted field of 'computational epidemiology' from the perspectives of molecular biology, measurement theory, and statistical modelling. Focusing on measurement of DNA methylation levels, the author also reviews the state of the art, proposes novel pre-processing methods and evaluation frameworks, and provides recommendations for genome-wide studies of DNA methylation levels using Illumina Infinium® HumanMethylation450 BeadChips. The remaining chapters, in the form of three self-contained scientific articles, cover applications on the following topics: (i) DNA methylation differences associated with internal migration patterns within Italy; (ii) associations of DNA methylation profiles with adiposity measures, targeted gene expression, biomarkers of lipid and glucose metabolism, and risk of developing three obesity-associated diseases; (iii) associations of a dietary score with blood pressure, and with urinary metabolites as characterised by NMR spectroscopy. The thesis is concluded with general remarks and the presentation of some open problems that offer potential for future research.
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Budimir, Iva <1992&gt. "Stochastic Modeling and Correlation Analysis of Omics Data." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amsdottorato.unibo.it/9792/1/Budimir_Iva_tesi.pdf.

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We studied the properties of three different types of omics data: protein domains in bacteria, gene length in metazoan genomes and methylation in humans. Gene elongation and protein domain diversification are some of the most important mechanisms in the evolution of functional complexity. For this reason, the investigation of the dynamic processes that led to their current configuration can highlight the important aspects of genome and proteome evolution and consequently of the evolution of living organisms. The potential of methylation to regulate the expression of genes is usually attributed to the groups of close CpG sites. We performed the correlation analysis to investigate the collaborative structure of all CpGs on chromosome 21. The long-tailed distributions of gene length and protein domain occurrences were successfully described by the stochastic evolutionary model and fitted with the Poisson Log-Normal distribution. This approach included both demographic and environmental stochasticity and the Gompertzian density regulation. The parameters of the fitted distributions were compared at the evolutionary scale. This allowed us to define a novel protein-domain-based phylogenetic method for bacteria which performed well at the intraspecies level. In the context of gene length distribution, we derived a new generalized population dynamics model for diverse subcommunities which allowed us to jointly model both coding and non-coding genomic sequences. A possible application of this approach is a method for differentiation between protein-coding genes and pseudogenes based on their length. General properties of the methylation correlation structure were firstly analyzed for the large data set of healthy controls and later compared to the Down syndrome (DS) data set. The CpGs demonstrated strong group behaviour even across the large genomic distances. Detected differences in DS were surprisingly small, possibly caused by the small sample size of DS which reduced the power of statistical analysis.
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Kim, Jieun. "Computational tools for the integrative analysis of muti-omics data to decipher trans-omics networks." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/28524.

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Regulatory networks define the phenotype, morphology, and function of cells. These networks are built from the basic building blocks of the cell—DNA, RNA, and proteins—and cut across the respective omics layers—genome, transcriptome, and proteome. The resulting omics networks depict a near infinite possibility of nodes and edges that intricately connect the ‘omes’. With the rapid advancement in the technologies that generate omics data in bulk samples and now at single-cell resolution, the field of life sciences is now met with the challenge to connect these omes to generate trans-omics networks. To this end, this thesis addressed some of the pressing challenges in trans-omics network reconstruction and the integrative analysis of omics data at both bulk and single-cell resolution: 1) the lack of an integrated pipeline for processing and downstream analysis of lesser studied omics layers; 2) the need for an integrative framework to reconstruct transcriptional networks and discover novel regulators of transcriptional regulation; and 3) development of tools for the reconstruction of single-cell multi-modal TRNs. I envision the work of my thesis to contribute towards the integrative study of bulk and single-cell trans-omics analysis, which I believe will become essential and standard-place in molecular biological studies as the comprehensiveness and accuracy of omics data measurements and databases for connecting different omics improves.
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Ding, Hao. "Visualization and Integrative analysis of cancer multi-omics data." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1467843712.

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Castleberry, Alissa. "Integrated Analysis of Multi-Omics Data Using Sparse Canonical Correlation Analysis." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu15544898045976.

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Tellaroli, Paola. "Three topics in omics research." Doctoral thesis, Università degli studi di Padova, 2015. http://hdl.handle.net/11577/3423912.

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The rather generic title of this Thesis is due to the fact that several aspects of biological phenomena have been investigated. Most of this work was addressed at the investigation of the limitations of one of the essential tools for analyzing gene expression data: cluster analysis. With several hundred of clustering methods in existence, there is clearly no shortage of clustering algorithms but, at the same time, satisfactory answers to some basic questions are still to come. In particular, we present a novel algorithm for the clustering of static data and a new strategy for the clustering of short-length time-course data. Finally, we analyzed data coming from Cap Analysis Gene Expression, a relatively new technology useful for the genome-wide promoter analysis and still mostly unexplored.
Il titolo piuttosto generico di questa tesi è dovuto al fatto che sono stati indagati diversi aspetti di fenomeni biologici. La maggior parte di questo lavoro è stato rivolto alla ricerca dei limiti di uno degli strumenti essenziali per l'analisi di dati di espressione genica: l'analisi dei gruppi. Esistendo diverse centinaia di metodi di raggruppamento, chiaramente non c'è carenza di algoritmi di analisi dei gruppi, ma, allo stesso tempo, alcuni quesiti fondamentali non hanno ancora ricevuto risposte soddisfacenti. In particolare, presentiamo un nuovo algoritmo di analisi dei gruppi per dati statici ed una nuova strategia per il raggruppamento di dati temporali di breve lunghezza. Infine, abbiamo analizzato dati provenienti da una tecnologia relativamente nuova, chiamata Cap Analysis Gene Expression, utile per l'analisi dei promotori su tutto il genoma e ancora in gran parte inesplorata.
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Ayati, Marzieh. "Algorithms to Integrate Omics Data for Personalized Medicine." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1527679638507616.

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Books on the topic "Omics data analysi"

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

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Tseng, George C., Debashis Ghosh, and Xianghong Jasmine Zhou. Integrating Omics Data. Cambridge University Press, 2015.

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Integrating Omics Data. Cambridge University Press, 2015.

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Tseng, George, Debashis Ghosh, and Xianghong Jasmine Zhou. Integrating Omics Data. Cambridge University Press, 2015.

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Big Data in Omics and Imaging: Association Analysis. Taylor & Francis Group, 2017.

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Xiong, Momiao. Big Data in Omics and Imaging: Association Analysis. Taylor & Francis Group, 2017.

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Qingfeng, Chen, Wei Lan, Yi-Ping Phoebe Chen, and Wilson Wen Bin Goh, eds. Graph Embedding Methods for Multiple-Omics Data Analysis. Frontiers Media SA, 2021. http://dx.doi.org/10.3389/978-2-88971-600-5.

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Book chapters on the topic "Omics data analysi"

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Ghantasala, Saicharan, Shabarni Gupta, Vimala Ashok Mani, Vineeta Rai, Tumpa Raj Das, Panga Jaipal Reddy, and Veenita Grover Shah. "Omics: Data Processing and Analysis." In Biomarker Discovery in the Developing World: Dissecting the Pipeline for Meeting the Challenges, 19–39. New Delhi: Springer India, 2016. http://dx.doi.org/10.1007/978-81-322-2837-0_3.

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Österlund, Tobias, Marija Cvijovic, and Erik Kristiansson. "Integrative Analysis of Omics Data." In Systems Biology, 1–24. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2017. http://dx.doi.org/10.1002/9783527696130.ch1.

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Yu, Xiang-Tian, and Tao Zeng. "Integrative Analysis of Omics Big Data." In Methods in Molecular Biology, 109–35. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7717-8_7.

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Dunkler, Daniela, Fátima Sánchez-Cabo, and Georg Heinze. "Statistical Analysis Principles for Omics Data." In Methods in Molecular Biology, 113–31. Totowa, NJ: Humana Press, 2011. http://dx.doi.org/10.1007/978-1-61779-027-0_5.

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Lü, Jinhu, and Pei Wang. "Data-Driven Statistical Approaches for Omics Data Analysis." In Modeling and Analysis of Bio-molecular Networks, 429–59. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9144-0_9.

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Han, Maozhen, Na Zhang, Zhangjie Peng, Yujie Mao, Qianqian Yang, Yiyang Chen, Mengfei Ren, and Weihua Jia. "Multi-Omics Data Analysis for Inflammation Disease Research: Correlation Analysis, Causal Analysis and Network Analysis." In Methodologies of Multi-Omics Data Integration and Data Mining, 101–18. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8210-1_6.

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Chen, Yi-An, Lokesh P. Tripathi, and Kenji Mizuguchi. "Data Warehousing with TargetMine for Omics Data Analysis." In Methods in Molecular Biology, 35–64. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9442-7_3.

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Habyarimana, Ephrem, and Sofia Michailidou. "Genomics Data." In Big Data in Bioeconomy, 69–76. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_6.

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AbstractIn silico prediction of plant performance is gaining increasing breeders’ attention. Several statistical, mathematical and machine learning methodologies for analysis of phenotypic, omics and environmental data typically use individual or a few data layers. Genomic selection is one of the applications, where heterogeneous data, such as those from omics technologies, are handled, accommodating several genetic models of inheritance. There are many new high throughput Next Generation Sequencing (NGS) platforms on the market producing whole-genome data at a low cost. Hence, large-scale genomic data can be produced and analyzed enabling intercrosses and fast-paced recurrent selection. The offspring properties can be predicted instead of manually evaluated in the field . Breeders have a short time window to make decisions by the time they receive data, which is one of the major challenges in commercial breeding. To implement genomic selection routinely as part of breeding programs, data management systems and analytics capacity have therefore to be in order. The traditional relational database management systems (RDBMS), which are designed to store, manage and analyze large-scale data, offer appealing characteristics, particularly when they are upgraded with capabilities for working with binary large objects. In addition, NoSQL systems were considered effective tools for managing high-dimensional genomic data. MongoDB system, a document-based NoSQL database, was effectively used to develop web-based tools for visualizing and exploring genotypic information. The Hierarchical Data Format (HDF5), a member of the high-performance distributed file systems family, demonstrated superior performance with high-dimensional and highly structured data such as genomic sequencing data.
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Zhou, Guangyan, Shuzhao Li, and Jianguo Xia. "Network-Based Approaches for Multi-omics Integration." In Computational Methods and Data Analysis for Metabolomics, 469–87. New York, NY: Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0239-3_23.

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Mühlberger, Irmgard, Julia Wilflingseder, Andreas Bernthaler, Raul Fechete, Arno Lukas, and Paul Perco. "Computational Analysis Workflows for Omics Data Interpretation." In Methods in Molecular Biology, 379–97. Totowa, NJ: Humana Press, 2011. http://dx.doi.org/10.1007/978-1-61779-027-0_17.

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Conference papers on the topic "Omics data analysi"

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Occhipinti, Annalisa, and Claudio Angione. "A Computational Model of Cancer Metabolism for Personalised Medicine." In Building Bridges in Medical Science 2021. Cambridge Medicine Journal, 2021. http://dx.doi.org/10.7244/cmj.2021.03.001.3.

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Cancer cells must rewrite their ‘‘internal code’’ to satisfy the demand for growth and proliferation. Such changes are driven by a combination of genetic (e.g., genes’ mutations) and non-genetic factors (e.g., tumour microenvironment) that result in an alteration of cellular metabolism. For this reason, understanding the metabolic and genomic changes of a cancer cell can provide useful insight on cancer progression and survival outcomes. In our work, we present a computational framework that uses patient-specific data to investigate cancer metabolism and provide personalised survival predictions and cancer development outcomes. The proposed model integrates patient-specific multi-omics data (i.e., genomic, metabolomic and clinical data) into a metabolic model of cancer to produce a list of metabolic reactions affecting cancer progression. Quantitative and predictive analysis, through survival analysis and machine learning techniques, is then performed on the list of selected reactions. Since our model performs an analysis of patient-specific data, the outcome of our pipeline provides a personalised prediction of survival outcome and cancer development based on a subset of identified multi-omics features (genomic, metabolomic and clinical data). In particular, our work aims to develop a computational pipeline for clinicians that relates the omic profile of each patient to their survival probability, based on a combination of machine learning and metabolic modelling techniques. The model provides patient-specific predictions on cancer development and survival outcomes towards the development of personalised medicine.
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Kovatch, Patricia, Anthony Costa, Zachary Giles, Eugene Fluder, Hyung Min Cho, and Svetlana Mazurkova. "Big omics data experience." In SC15: The International Conference for High Performance Computing, Networking, Storage and Analysis. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2807591.2807595.

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Klabukov, Il'ya. "ELEMENTS FOR SYSTEMS MEDICINE OF CHOLANGIOPATHIES." In XIV International Scientific Conference "System Analysis in Medicine". Far Eastern Scientific Center of Physiology and Pathology of Respiration, 2020. http://dx.doi.org/10.12737/conferencearticle_5fe01d9b506245.44352217.

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The approach to system analysis of bile duct dysfunctions based on analysis of multi-omics data of cholangiocytes is considered. There is suggested that changes in intercellular interactions in tissues of the bile duct cause phenotypic manifestations of the cholangiopathies in the changes in cholangiocyte regulation, which can be evaluated by analysis of changes in the molecular composition of the bile.
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Sunghoon Choi, Soo-yeon Park, Hoejin Kim, Oran Kwon, and Taesung Park. "Analysis for doubly repeated omics data from crossover design." In 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2016. http://dx.doi.org/10.1109/bibm.2016.7822782.

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Xing, Wei, Jon Smith, Mike Gavrielides, Steve Hindmarsh, Adam Huffman, and Hai H. Wang. "Nautilus: A Precision-Guided Open Data Architecture for Big Omics Data Analysis." In 2019 2nd International Conference on Artificial Intelligence and Big Data (ICAIBD). IEEE, 2019. http://dx.doi.org/10.1109/icaibd.2019.8836977.

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Ma, Yingning. "Cluster analysis for cancer omics data using Neural Network with data augmentation." In SPML 2022: 2022 5th International Conference on Signal Processing and Machine Learning. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3556384.3556388.

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Jain, Yashita, and Shanshan Ding. "Integrative Sufficient Dimension Reduction Methods for Multi-Omics Data Analysis." In BCB '17: 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3107411.3108225.

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Sun Kim. "Networks and models for the integrated analysis of multi omics data." In 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2016. http://dx.doi.org/10.1109/bibm.2016.7822479.

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Fernandez-Banet, Julio, Anthony Esposito, Scott Coffin, Sabine Schefzick, Ying Ding, Keith Ching, Istvan Horvath, Peter Roberts, Paul Rejto, and Zhengyan Kan. "Abstract 4874: OASIS: A centralized portal for cancer omics data analysis." In Proceedings: AACR 106th Annual Meeting 2015; April 18-22, 2015; Philadelphia, PA. American Association for Cancer Research, 2015. http://dx.doi.org/10.1158/1538-7445.am2015-4874.

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Min, Eun Jeong, Changgee Chang, and Qi Long. "Generalized Bayesian Factor Analysis for Integrative Clustering with Applications to Multi-Omics Data." In 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2018. http://dx.doi.org/10.1109/dsaa.2018.00021.

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Reports on the topic "Omics data analysi"

1

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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Villamizar-Villegas, Mauricio, and Yasin Kursat Onder. Uncovering Time-Specific Heterogeneity in Regression Discontinuity Designs. Banco de la República de Colombia, November 2020. http://dx.doi.org/10.32468/be.1141.

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The literature that employs Regression Discontinuity Designs (RDD) typically stacks data across time periods and cutoff values. While practical, this procedure omits useful time heterogeneity. In this paper we decompose the RDD treatment effect into its weighted time-value parts. This analysis adds richness to the RDD estimand, where each time-specific component can be different and informative in a manner that is not expressed by the single cutoff or pooled regressions. To illustrate our methodology, we present two empirical examples: one using repeated cross-sectional data and another using time-series. Overall, we show a significant heterogeneity in both cutoff and time-specific effects. From a policy standpoint, this heterogeneity can pick up key differences in treatment across economically relevant episodes. Finally, we propose a new estimator that uses all observations from the original design and which captures the incremental effect of policy given a state variable. We show that this estimator is generally more precise compared to those that exclude observations exposed to other cutoffs or time periods. Our proposed framework is simple and easily replicable and can be applied to any RDD application that carries an explicitly traceable time dimension.
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Fait, Aaron, Grant Cramer, and Avichai Perl. Towards improved grape nutrition and defense: The regulation of stilbene metabolism under drought. United States Department of Agriculture, May 2014. http://dx.doi.org/10.32747/2014.7594398.bard.

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The goals of the present research proposal were to elucidate the physiological and molecular basis of the regulation of stilbene metabolism in grape, against the background of (i) grape metabolic network behavior in response to drought and of (ii) varietal diversity. The specific objectives included the study of the physiology of the response of different grape cultivars to continuous WD; the characterization of the differences and commonalities of gene network topology associated with WD in berry skin across varieties; the study of the metabolic response of developing berries to continuous WD with specific attention to the stilbene compounds; the integration analysis of the omics data generated; the study of isolated drought-associated stress factors on the regulation of stilbene biosynthesis in plantaand in vitro. Background to the topic Grape quality has a complex relationship with water input. Regulated water deficit (WD) is known to improve wine grapes by reducing the vine growth (without affecting fruit yield) and boosting sugar content (Keller et al. 2008). On the other hand, irregular rainfall during the summer can lead to drought-associated damage of fruit developmental process and alter fruit metabolism (Downey et al., 2006; Tarara et al., 2008; Chalmers et al., 792). In areas undergoing desertification, WD is associated with high temperatures. This WD/high temperature synergism can limit the areas of grape cultivation and can damage yields and fruit quality. Grapes and wine are the major source of stilbenes in human nutrition, and multiple stilbene-derived compounds, including isomers, polymers and glycosylated forms, have also been characterized in grapes (Jeandet et al., 2002; Halls and Yu, 2008). Heterologous expression of stilbenesynthase (STS) in a variety of plants has led to an enhanced resistance to pathogens, but in others the association has not been proven (Kobayashi et al., 2000; Soleas et al., 1995). Tomato transgenic plants harboring a grape STS had increased levels of resveratrol, ascorbate, and glutathione at the expense of the anthocyanin pathways (Giovinazzo et al. 2005), further emphasizing the intermingled relation among secondary metabolic pathways. Stilbenes are are induced in green and fleshy parts of the berries by biotic and abiotic elicitors (Chong et al., 2009). As is the case for other classes of secondary metabolites, the biosynthesis of stilbenes is not very well understood, but it is known to be under tight spatial and temporal control, which limits the availability of these compounds from plant sources. Only very few studies have attempted to analyze the effects of different environmental components on stilbene accumulation (Jeandet et al., 1995; Martinez-Ortega et al., 2000). Targeted analyses have generally shown higher levels of resveratrol in the grape skin (induced), in seeded varieties, in varieties of wine grapes, and in dark-skinned varieties (Gatto et al., 2008; summarized by Bavaresco et al., 2009). Yet, the effect of the grape variety and the rootstock on stilbene metabolism has not yet been thoroughly investigated (Bavaresco et al., 2009). The study identified a link between vine hydraulic behavior and physiology of stress with the leaf metabolism, which the PIs believe can eventually lead to the modifications identified in the developing berries that interested the polyphenol metabolism and its regulation during development and under stress. Implications are discussed below.
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