An Integrated Analysis Of Microbiomes And Metabolomics

Because the microbial community is dynamic, an individual’s microbiota at a given time is varied, and many factors, including age, host genetics, diet, and the local environment, significantly change the microbiota. Thus, microbiome researchers have naturally expanded their research to look for insights into the interaction of the microbiome with other “omics”. Metabolites (small molecules) are the intermediate or end products of metabolism. Metabolites have various functions. The microbial-derived metabolites play an important role in the function of the microbiome. Thus, the advancement in microbiome studies is becoming particularly critical for the integration of microbial DNA sequencing data with other omics data, especially microbiome-metabolomics integration.

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.

The book serves as an amalgamation of knowledge and principles used in the area of systems and synthetic biology, and targets inter-disciplinary research groups. The readers from diversified areas would be benefited by the valuable resources and information available in one book. Microbiome projects with efficient data handling can fuel progress in the area of microbial synthetic biology by providing a ready to use plug and play chassis. Advances in gene editing technology such as the use of tailor made synthetic transcription factors will further enhance the availability of synthetic devices to be applied in the fields of environment, agriculture and health. The different chapters of the book reviews a broad range of topics, including food microbiome in ecology, use of microbiome in personalized medicine, machine learning in biomedicine. The book also describes ways to harness and exploit the incredible amounts of genomic data. The book is not only limited to medicine but also caters to the needs of environmentalists, biochemical engineers etc. It will be of interest to advanced students and researchers in life sciences, computational biology, microbiology and other inter-disciplinary areas.

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.

This unique book addresses the bioinformatic and statistical modelling and also the analysis of microbiome data using cutting-edge QIIME 2 and R software. It covers core analysis topics in both bioinformatics and statistics, which provides a complete workflow for microbiome data analysis: from raw sequencing reads to community analysis and statistical hypothesis testing. It includes real-world data from the authors’ research and from the public domain, and discusses the implementation of QIIME 2 and R for data analysis step-by-step. The data as well as QIIME 2 and R computer programs are publicly available, allowing readers to replicate the model development and data analysis presented in each chapter so that these new methods can be readily applied in their own research. Bioinformatic and Statistical Analysis of Microbiome Data is an ideal book for advanced graduate students and researchers in the clinical, biomedical, agricultural, and environmental fields, as well as those studying bioinformatics, statistics, and big data analysis.

This unique book officially defines microbiome statistics as a specific new field of statistics and addresses the statistical analysis of correlation, association, interaction, and composition in microbiome research. It also defines the study of the microbiome as a hypothesis-driven experimental science and describes two microbiome research themes and six unique characteristics of microbiome data, as well as investigating challenges for statistical analysis of microbiome data using the standard statistical methods. This book is useful for researchers of biostatistics, ecology, and data analysts. Presents a thorough overview of statistical methods in microbiome statistics of parametric and nonparametric correlation, association, interaction, and composition adopted from classical statistics and ecology and specifically designed for microbiome research. Performs step-by-step statistical analysis of correlation, association, interaction, and composition in microbiome data. Discusses the issues of statistical analysis of microbiome data: high dimensionality, compositionality, sparsity, overdispersion, zero-inflation, and heterogeneity. Investigates statistical methods on multiple comparisons and multiple hypothesis testing and applications to microbiome data. Introduces a series of exploratory tools to visualize composition and correlation of microbial taxa by barplot, heatmap, and correlation plot. Employs the Kruskal–Wallis rank-sum test to perform model selection for further multi-omics data integration. Offers R code and the datasets from the authors’ real microbiome research and publicly available data for the analysis used. Remarks on the advantages and disadvantages of each of the methods used.

Microbiome and Metabolome in Diagnosis, Therapy, and Other Strategic Applications is the first book to simultaneously cover the microbiome and the metabolome in relevant clinical conditions. In a pioneering fashion, it addresses not only the classic intestinal environment, but also the oral, gastric, lung, skin and vaginal microbiome that is in line with the latest investigations. Nonbacterial microbiomes, such as fungi and viruses are not overlooked, and the plasma microbiome is also discussed. As plasma, brain, placenta, tumor cells, and other sterile fluids and tissues, are increasingly recognized to potentially host a microbiome, albeit a limited one, this is a timely resource. The book's editors were fortunate to have the input of renowned collaborators from nearly all continents. This is truly an international effort that brings the latest in the field to students and professionals alike. Provides comprehensive coverage on diagnosis, therapy, pharmacotherapy and disease prevention in context of the microbiome and metabolome Focuses on the proposed physiological or pathological conditions Presents an up-to-date, useful reference

Microbiome and the Eye: What’s the connection? highlights how alterations in the gut and eye microbiomes can lead to systemic immune alterations with subsequent effects on the eye. The book is divided into two sections, one highlighting how alterations in the gut microbiome impact various components of health outside the gut, with a focus on the immune system and inflammatory mediators, and the second focusing on studies on a variety of ocular diseases, including ocular surface diseases/dry eye, keratitis, uveitis, glaucoma, and retinopathy to gut dysbiosis. With its translational approach, the book is suitable for both researchers and clinicians. The book will help readers understand the mechanisms in which gut and eye microbiome composition may influence health in multiple compartments, with a focus on eye diseases. Helps researchers understand the clinical eye diseases that have been linked to gut microbiome abnormalities Helps clinicians understand the mechanisms in which gut microbiome composition may influence health in multiple compartments Provides a foundation for future studies that consider gut microbiome manipulations as a treatment for specific eye diseases