Statistical and Computational Methods for Microbiome Multi-Omics Data PDF Download
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Author: Himel Mallick
Publisher: Frontiers Media SA
ISBN: 2889660915
Category : Science
Languages : en
Pages : 170
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Book Description
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.
Author: Himel Mallick
Publisher: Frontiers Media SA
ISBN: 2889660915
Category : Science
Languages : en
Pages : 170
Get Book Here
Book Description
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.
Author: Joao Carlos Setubal
Publisher: Frontiers Media SA
ISBN: 2889664376
Category : Science
Languages : en
Pages : 170
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Book Description
Author: Setubal
Publisher: Frontiers Media SA
ISBN: 2832506402
Category : Science
Languages : en
Pages : 223
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Book Description
Author: Ehsan Nazemalhosseini-Mojarad
Publisher: Frontiers Media SA
ISBN: 2832530389
Category : Science
Languages : en
Pages : 433
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Book Description
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.
Author: Somnath Datta
Publisher: Springer Nature
ISBN: 3030733513
Category : Medical
Languages : en
Pages : 349
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Book Description
Microbiome research has focused on microorganisms that live within the human body and their effects on health. During the last few years, the quantification of microbiome composition in different environments has been facilitated by the advent of high throughput sequencing technologies. The statistical challenges include computational difficulties due to the high volume of data; normalization and quantification of metabolic abundances, relative taxa and bacterial genes; high-dimensionality; multivariate analysis; the inherently compositional nature of the data; and the proper utilization of complementary phylogenetic information. This has resulted in an explosion of statistical approaches aimed at tackling the unique opportunities and challenges presented by microbiome data. This book provides a comprehensive overview of the state of the art in statistical and informatics technologies for microbiome research. In addition to reviewing demonstrably successful cutting-edge methods, particular emphasis is placed on examples in R that rely on available statistical packages for microbiome data. With its wide-ranging approach, the book benefits not only trained statisticians in academia and industry involved in microbiome research, but also other scientists working in microbiomics and in related fields.
Author: Indra Mani
Publisher: Springer
ISBN: 9789819718436
Category : Science
Languages : en
Pages : 0
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Book Description
This book introduces the rapidly evolving field of multi-omics in understanding the human microbiome. The book focuses on the technology used to generate multi-omics data, including advances in next-generation sequencing and other high-throughput methods. It also covers the application of artificial intelligence and machine learning algorithms to the analysis of multi-omics data, providing readers with an overview of the powerful computational tools that are driving innovation in this field. The chapter also explores the various bioinformatics databases and tools available for the analysis of multi-omics data. The book also delves into the application of multi-omics technology to the study of microbial diversity, including metagenomics, metatranscriptomics, and metaproteomics. The book also explores the use of these techniques to identify and characterize microbial communities in different environments, from the gut and oral microbiome to the skin microbiome and beyond. Towards theend, it focuses on the use of multi-omics in the study of microbial consortia, including mycology and the viral microbiome. The book also explores the potential of multi-omics to identify genes of biotechnological importance, providing readers with an understanding of the role that this technology could play in advancing biotech research. Finally, the book concludes with a discussion of the clinical applications of multi-omics technology, including its potential to identify disease biomarkers and develop personalized medicine approaches. Overall, this book provides readers with a comprehensive overview of this exciting field, highlighting the potential for multi-omics to transform our understanding of the microbial world.
Author: Gary Hardiman
Publisher: MDPI
ISBN: 3039287443
Category : Science
Languages : en
Pages : 202
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Book Description
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.
Author: Yinglin Xia
Publisher: American Chemical Society
ISBN: 0841299161
Category : Science
Languages : en
Pages : 229
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Book Description
Compared with other research fields, both microbiome and metabolomics data are complicated and have some unique characteristics, respectively. Thus, choosing an appropriate statistical test or method is a very important step in the analysis of microbiome and metabolomics data. However, this is still a difficult task for those biomedical researchers without a statistical background and for those biostatisticians who do not have research experiences in these fields. Graduate students studying microbiome and metabolomics; statisticians, working on microbiome and metabolomics projects, either for their own research, or for their collaborative research for experimental design, grant application, and data analysis; and researchers who investigate biomedical and biochemical projects with the microbiome, metabolome, and multi-omics data analysis will benefit from reading this work.
Author: Gibraan Rahman
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Next-generation sequencing (NGS) has effected an explosion of research into the relationship between genetic information and a variety of biological conditions. One of the most exciting areas of study is how the trillions of microbial species that we share this Earth with affect our health. However, the process of extracting useful biological insights from this breadth of data is far from trivial. There are numerous statistical and computational considerations in addition to the already complex and messy biological problems. In this thesis, I describe my work on developing and implementing software to tackle the complex world of statistical microbiome analysis. In the first part of this thesis, we review the applications and challenges of performing dimensionality reduction on microbiome data comprising thousands of microbial taxa. When dealing with this high dimensionality, it is imperative to be able to get an overview of the community structure in a lower dimensional space that can be both visualized and interpreted. We review the statistical considerations for dimensionality reduction and the existing tools and algorithms that can and cannot address them. This includes discussions about sparsity, compositionality, and phylogenetic signal. We also make recommendations about tools and algorithms to consider for different use-cases. In the second part of this thesis, we present a new software, Evident, designed to assist researchers with statistical analysis of microbiome effect sizes and power analysis. Effect sizes of statistical tests are not widely reported in microbiome datasets, limiting the interpretability of community differences such as alpha and beta diversity. As more large microbiome studies are produced, researchers have the opportunity to mine existing datasets to get a sense of the effect size for different biological conditions. These, in turn, can be used to perform power analysis prior to designing an experiment, allowing researchers to better allocate resources. We show how Evident is scalable to dozens of datasets and provides easy calculation and exploration of effect sizes and power analysis from existing data. In the third part of this thesis, we describe a novel investigation into the joint microbiome and metabolome axis in colorectal cancer. In most cases of sporadic colorectal cancers (CRC), tumorigenesis is a multistep process driven by genomic alterations in concert with dietary influences. In addition, mounting evidence has implicated the gut microbiome as an effector in the development and progression of CRC. While large meta-analyses have provided mechanistic insight into disease progression in CRC patients, study heterogeneity has limited causal associations. To address this limitation, multi-omics studies on genetically controlled cohorts of mice were performed to distinguish genetic and dietary influences. Diet was identified as the major driver of microbial and metabolomic differences, with reductions in alpha diversity and widespread changes in cecal metabolites seen in HFD-fed mice. Similarly, the levels of non-classic amino acid conjugated forms of the bile acid cholic acid (AA-CAs) increased with HFD. We show that these AA-CAs signal through the nuclear receptor FXR and membrane receptor TGR5 to functionally impact intestinal stem cell growth. In addition, the poor intestinal permeability of these AA-CAs supports their localization in the gut. Moreover, two cryptic microbial strains, Ileibacterium valens and Ruminococcus gnavus, were shown to have the capacity to synthesize these AA-CAs. This multi-omics dataset from CRC mouse models supports diet-induced shifts in the microbiome and metabolome in disease progression with potential utility in directing future diagnostic and therapeutic developments. In the fourth chapter, we demonstrate a new framework for performing differential abundance analysis using customized statistical modeling. As we learn more and more about the relationship between the microbiome and biological conditions, experimental protocols are becoming more and more complex. For example, meta-analyses, interventions, longitudinal studies, etc. are being used to better understand the dynamic nature of the microbiome. However, statistical methods to analyze these relationships are lacking--especially in the field of differential abundance. Finding biomarkers associated with conditions of interest must be performed with statistical care when dealing with these kinds of experimental designs. We present BIRDMAn, a software package integrating probabilistic programming with Stan to build custom models for analyzing microbiome data. We show that, on both simulated and real datasets, BIRDMAn is able to extract novel biological signals that are missed by existing methods. These chapters, taken together, advance our knowledge of statistical analysis of microbiome data and provide tools and references for researchers looking to perform analysis on their own data.
Author: Francisco A. Gómez Vela
Publisher: MDPI
ISBN: 3039437712
Category : Medical
Languages : en
Pages : 222
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Book Description
In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality.
