Integration of Multisource Heterogenous Omics Information in Cancer

Integration of Multisource Heterogenous Omics Information in Cancer
Author: Victor Jin
Publisher: Frontiers Media SA
Total Pages: 154
Release: 2020-01-30
Genre:
ISBN: 2889634485

Multisource heterogenous omics data can provide unprecedented perspectives and insights into cancer studies, but also pose great analytical problems for researchers due to the vast amount of data produced. This Research Topic aims to provide a forum for sharing ideas, tools and results among researchers from various computational cancer biology fields such as genetic/epigenetic and genome-wide studies.

Multi-omic Data Integration in Oncology

Multi-omic Data Integration in Oncology
Author: Chiara Romualdi
Publisher: Frontiers Media SA
Total Pages: 187
Release: 2020-12-03
Genre: Medical
ISBN: 2889661512

This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.

Systems Analytics and Integration of Big Omics Data

Systems Analytics and Integration of Big Omics Data
Author: Gary Hardiman
Publisher: MDPI
Total Pages: 202
Release: 2020-04-15
Genre: Science
ISBN: 3039287443

A “genotype" is essentially an organism's full hereditary information which is obtained from its parents. A "phenotype" is an organism's actual observed physical and behavioral properties. These may include traits such as morphology, size, height, eye color, metabolism, etc. One of the pressing challenges in computational and systems biology is genotype-to-phenotype prediction. This is challenging given the amount of data generated by modern Omics technologies. This “Big Data” is so large and complex that traditional data processing applications are not up to the task. Challenges arise in collection, analysis, mining, sharing, transfer, visualization, archiving, and integration of these data. In this Special Issue, there is a focus on the systems-level analysis of Omics data, recent developments in gene ontology annotation, and advances in biological pathways and network biology. The integration of Omics data with clinical and biomedical data using machine learning is explored. This Special Issue covers new methodologies in the context of gene–environment interactions, tissue-specific gene expression, and how external factors or host genetics impact the microbiome.

Integration of Omics Approaches and Systems Biology for Clinical Applications

Integration of Omics Approaches and Systems Biology for Clinical Applications
Author: Antonia Vlahou
Publisher: John Wiley & Sons
Total Pages: 612
Release: 2018-01-24
Genre: Science
ISBN: 1119183960

Introduces readers to the state of the art of omics platforms and all aspects of omics approaches for clinical applications This book presents different high throughput omics platforms used to analyze tissue, plasma, and urine. The reader is introduced to state of the art analytical approaches (sample preparation and instrumentation) related to proteomics, peptidomics, transcriptomics, and metabolomics. In addition, the book highlights innovative approaches using bioinformatics, urine miRNAs, and MALDI tissue imaging in the context of clinical applications. Particular emphasis is put on integration of data generated from these different platforms in order to uncover the molecular landscape of diseases. The relevance of each approach to the clinical setting is explained and future applications for patient monitoring or treatment are discussed. Integration of omics Approaches and Systems Biology for Clinical Applications presents an overview of state of the art omics techniques. These methods are employed in order to obtain the comprehensive molecular profile of biological specimens. In addition, computational tools are used for organizing and integrating these multi-source data towards developing molecular models that reflect the pathophysiology of diseases. Investigation of chronic kidney disease (CKD) and bladder cancer are used as test cases. These represent multi-factorial, highly heterogeneous diseases, and are among the most significant health issues in developed countries with a rapidly aging population. The book presents novel insights on CKD and bladder cancer obtained by omics data integration as an example of the application of systems biology in the clinical setting. Describes a range of state of the art omics analytical platforms Covers all aspects of the systems biology approach—from sample preparation to data integration and bioinformatics analysis Contains specific examples of omics methods applied in the investigation of human diseases (Chronic Kidney Disease, Bladder Cancer) Integration of omics Approaches and Systems Biology for Clinical Applications will appeal to a wide spectrum of scientists including biologists, biotechnologists, biochemists, biophysicists, and bioinformaticians working on the different molecular platforms. It is also an excellent text for students interested in these fields.

Multi-omic Data Integration

Multi-omic Data Integration
Author: Paolo Tieri
Publisher: Frontiers Media SA
Total Pages: 137
Release: 2015-09-17
Genre: Science (General)
ISBN: 2889196488

Stable, predictive biomarkers and interpretable disease signatures are seen as a significant step towards personalized medicine. In this perspective, integration of multi-omic data coming from genomics, transcriptomics, glycomics, proteomics, metabolomics is a powerful strategy to reconstruct and analyse complex multi-dimensional interactions, enabling deeper mechanistic and medical insight. At the same time, there is a rising concern that much of such different omic data –although often publicly and freely available- lie in databases and repositories underutilised or not used at all. Issues coming from lack of standardisation and shared biological identities are also well-known. From these considerations, a novel, pressing request arises from the life sciences to design methodologies and approaches that allow for these data to be interpreted as a whole, i.e. as intertwined molecular signatures containing genes, proteins, mRNAs and miRNAs, able to capture inter-layers connections and complexity. Papers discuss data integration approaches and methods of several types and extents, their application in understanding the pathogenesis of specific diseases or in identifying candidate biomarkers to exploit the full benefit of multi-omic datasets and their intrinsic information content. Topics of interest include, but are not limited to: • Methods for the integration of layered data, including, but not limited to, genomics, transcriptomics, glycomics, proteomics, metabolomics; • Application of multi-omic data integration approaches for diagnostic biomarker discovery in any field of the life sciences; • Innovative approaches for the analysis and the visualization of multi-omic datasets; • Methods and applications for systematic measurements from single/undivided samples (comprising genomic, transcriptomic, proteomic, metabolomic measurements, among others); • Multi-scale approaches for integrated dynamic modelling and simulation; • Implementation of applications, computational resources and repositories devoted to data integration including, but not limited to, data warehousing, database federation, semantic integration, service-oriented and/or wiki integration; • Issues related to the definition and implementation of standards, shared identities and semantics, with particular focus on the integration problem. Research papers, reviews and short communications on all topics related to the above issues were welcomed.

Computational Methods for Multi-Omics Data Analysis in Cancer Precision Medicine

Computational Methods for Multi-Omics Data Analysis in Cancer Precision Medicine
Author: Ehsan Nazemalhosseini-Mojarad
Publisher: Frontiers Media SA
Total Pages: 433
Release: 2023-08-02
Genre: Science
ISBN: 2832530389

Cancer is a complex and heterogeneous disease often caused by different alterations. The development of human cancer is due to the accumulation of genetic and epigenetic modifications that could affect the structure and function of the genome. High-throughput methods (e.g., microarray and next-generation sequencing) can investigate a tumor at multiple levels: i) DNA with genome-wide association studies (GWAS), ii) epigenetic modifications such as DNA methylation, histone changes and microRNAs (miRNAs) iii) mRNA. The availability of public datasets from different multi-omics data has been growing rapidly and could facilitate better knowledge of the biological processes of cancer. Computational approaches are essential for the analysis of big data and the identification of potential biomarkers for early and differential diagnosis, and prognosis.

Integration of Online Omics-data Resources for Cancer Research

Integration of Online Omics-data Resources for Cancer Research
Author: Tonmoy Das
Publisher:
Total Pages:
Release: 2020
Genre:
ISBN:

Abstract: The manifestations of cancerous phenotypes necessitate alterations at different levelsof information-flow from genome to proteome. The molecular alterations at differentinformation processing levels serve as the basis for the cancer phenotype to emerge. Tounderstand the underlying mechanisms that drive the acquisition of cancer hallmarks it isrequired to interrogate cancer cells using multiple levels of information flow representedby different omics - such as genomics, epigenomics, transcriptomics, and proteomics.The advantage of multi-omics data integration comes with a trade-off in the form of anadded layer of complexity originating from inherently diverse types of omics-datasetsthat may pose a challenge to integrate the omics-data in a biologically meaningfulmanner. The plethora of cancer-specific online omics-data resources, if able to beintegrated efficiently and systematically, may facilitate the generation of new biologicalinsights for cancer research. In this review, we provide a comprehensive overviewof the online single- and multi-omics resources that are dedicated to cancer. Wecatalog various online omics-data resources such as The Cancer Genome Atlas (TCGA)along with various TCGA-associated data portals and tools for multi-omics analysisand visualization, the International Cancer Genome Consortium (ICGC), Catalogueof Somatic Mutations in Cancer (COSMIC), The Pathology Atlas, Gene ExpressionOmnibus (GEO), and PRoteomics IDEntifications (PRIDE). By comparing the strengthsand limitations of the respective online resources, we aim to highlight the currentbiological and technological challenges and possible strategies to overcome thesechallenges. We outline the available schemes for the integration of the multi-omicsdimensions for stratifying cancer patients and biomarker prediction based on theintegrated molecular-signatures of cancer. Finally, we propose the multi-omics drivensystems-biology approaches to realize the potential of precision onco-medicine as the uture of cancer research. We believe this systematic review will encourage scientistsand clinicians worldwide to utilize the online resources to explore and integrate theavailable omics datasets that may provide a window of opportunity to generate newbiological insights and contribute to the advancement of the field of cancer research

Methodologies of Multi-Omics Data Integration and Data Mining

Methodologies of Multi-Omics Data Integration and Data Mining
Author: Kang Ning
Publisher: Springer Nature
Total Pages: 173
Release: 2023-01-15
Genre: Medical
ISBN: 9811982104

This book features multi-omics big-data integration and data-mining techniques. In the omics age, paramount of multi-omics data from various sources is the new challenge we are facing, but it also provides clues for several biomedical or clinical applications. This book focuses on data integration and data mining methods for multi-omics research, which explains in detail and with supportive examples the “What”, “Why” and “How” of the topic. The contents are organized into eight chapters, out of which one is for the introduction, followed by four chapters dedicated for omics integration techniques focusing on several omics data resources and data-mining methods, and three chapters dedicated for applications of multi-omics analyses with application being demonstrated by several data mining methods. This book is an attempt to bridge the gap between the biomedical multi-omics big data and the data-mining techniques for the best practice of contemporary bioinformatics and the in-depth insights for the biomedical questions. It would be of interests for the researchers and practitioners who want to conduct the multi-omics studies in cancer, inflammation disease, and microbiome researches.

Integrative Multi-omics Analysis to Understand Cancer and Anticancer Therapy

Integrative Multi-omics Analysis to Understand Cancer and Anticancer Therapy
Author: Michelle Ting Dow
Publisher:
Total Pages: 197
Release: 2020
Genre:
ISBN:

Precision cancer medicine promises better treatments to a disease as complex and heterogenous as cancer. Many anti-cancer therapies are beneficial to only a subset of patients due to the variability in patient genetic and tumor heterogeneity. Thus, we need better frameworks for understanding underlying genomic and transcriptomic patterns influencing differential patient outcomes, yet our understanding of how genetic alterations connect to treatment in in vivo and in vitro models remains understudied. To address this gap, I utilized human patient data from The Cancer Genome Atlas (TCGA), hepatocellular carcinoma (HCC) models, prostate cancer (PCa) models, and chronic myelogenous leukemia (CML) cell lines. Through the integration of multi-omic data, I identified parallel features of human and model organism data that could reveal disease specific characteristics. Additionally, I characterized the landscape of acquired resistance for a panel of chemotherapeutic treatments and revealed potential alleles and genes that mediate the process. The analyses I conducted expose the role of genetic information and suggest future applications for development of precision medicine.