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| Author | : Joao Carlos Setubal |
| Publisher | : Frontiers Media SA |
| Total Pages | : 170 |
| Release | : 2021-02-02 |
| Genre | : Science |
| ISBN | : 2889664376 |
| Author | : Setubal |
| Publisher | : Frontiers Media SA |
| Total Pages | : 223 |
| Release | : 2023-01-04 |
| Genre | : Science |
| ISBN | : 2832506402 |
| Author | : Himel Mallick |
| Publisher | : Frontiers Media SA |
| Total Pages | : 170 |
| Release | : 2020-11-19 |
| Genre | : Science |
| ISBN | : 2889660915 |
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 | : Somnath Datta |
| Publisher | : Springer Nature |
| Total Pages | : 349 |
| Release | : 2021-10-27 |
| Genre | : Medical |
| ISBN | : 3030733513 |
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 | : David J. Balding |
| Publisher | : John Wiley & Sons |
| Total Pages | : 1828 |
| Release | : 2019-07-09 |
| Genre | : Science |
| ISBN | : 1119429250 |
A timely update of a highly popular handbook on statistical genomics This new, two-volume edition of a classic text provides a thorough introduction to statistical genomics, a vital resource for advanced graduate students, early-career researchers and new entrants to the field. It introduces new and updated information on developments that have occurred since the 3rd edition. Widely regarded as the reference work in the field, it features new chapters focusing on statistical aspects of data generated by new sequencing technologies, including sequence-based functional assays. It expands on previous coverage of the many processes between genotype and phenotype, including gene expression and epigenetics, as well as metabolomics. It also examines population genetics and evolutionary models and inference, with new chapters on the multi-species coalescent, admixture and ancient DNA, as well as genetic association studies including causal analyses and variant interpretation. The Handbook of Statistical Genomics focuses on explaining the main ideas, analysis methods and algorithms, citing key recent and historic literature for further details and references. It also includes a glossary of terms, acronyms and abbreviations, and features extensive cross-referencing between chapters, tying the different areas together. With heavy use of up-to-date examples and references to web-based resources, this continues to be a must-have reference in a vital area of research. Provides much-needed, timely coverage of new developments in this expanding area of study Numerous, brand new chapters, for example covering bacterial genomics, microbiome and metagenomics Detailed coverage of application areas, with chapters on plant breeding, conservation and forensic genetics Extensive coverage of human genetic epidemiology, including ethical aspects Edited by one of the leading experts in the field along with rising stars as his co-editors Chapter authors are world-renowned experts in the field, and newly emerging leaders. The Handbook of Statistical Genomics is an excellent introductory text for advanced graduate students and early-career researchers involved in statistical genetics.
| Author | : Robert G. Beiko |
| Publisher | : |
| Total Pages | : 324 |
| Release | : 2018 |
| Genre | : Microbiology |
| ISBN | : 9781493987283 |
| Author | : Ion Mandoiu |
| Publisher | : John Wiley & Sons |
| Total Pages | : 464 |
| Release | : 2016-09-12 |
| Genre | : Computers |
| ISBN | : 1119272165 |
Introduces readers to core algorithmic techniques for next-generation sequencing (NGS) data analysis and discusses a wide range of computational techniques and applications This book provides an in-depth survey of some of the recent developments in NGS and discusses mathematical and computational challenges in various application areas of NGS technologies. The 18 chapters featured in this book have been authored by bioinformatics experts and represent the latest work in leading labs actively contributing to the fast-growing field of NGS. The book is divided into four parts: Part I focuses on computing and experimental infrastructure for NGS analysis, including chapters on cloud computing, modular pipelines for metabolic pathway reconstruction, pooling strategies for massive viral sequencing, and high-fidelity sequencing protocols. Part II concentrates on analysis of DNA sequencing data, covering the classic scaffolding problem, detection of genomic variants, including insertions and deletions, and analysis of DNA methylation sequencing data. Part III is devoted to analysis of RNA-seq data. This part discusses algorithms and compares software tools for transcriptome assembly along with methods for detection of alternative splicing and tools for transcriptome quantification and differential expression analysis. Part IV explores computational tools for NGS applications in microbiomics, including a discussion on error correction of NGS reads from viral populations, methods for viral quasispecies reconstruction, and a survey of state-of-the-art methods and future trends in microbiome analysis. Computational Methods for Next Generation Sequencing Data Analysis: Reviews computational techniques such as new combinatorial optimization methods, data structures, high performance computing, machine learning, and inference algorithms Discusses the mathematical and computational challenges in NGS technologies Covers NGS error correction, de novo genome transcriptome assembly, variant detection from NGS reads, and more This text is a reference for biomedical professionals interested in expanding their knowledge of computational techniques for NGS data analysis. The book is also useful for graduate and post-graduate students in bioinformatics.
| Author | : Wontack Han |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2022 |
| Genre | : Bioinformatics |
| ISBN | : |
Microbial organisms play key roles in the human hosts' health and diseases. Recent advancements in genome sequencing have resulted in a large collection of sequencing data of microbial species and have expanded the research of microbiome from the characterization of microbiomes' community associated with different environments/hosts to the applications related with human health and diseases. Computational methods have been developed to identify microbial markers from microbiome datasets derived from cohorts of patients with different diseases. Predictive models based on these markers (features) have been built for discriminating host phenotypes such as disease vs healthy and cancer immunotherapy responder vs non-responder. In this dissertation, I developed computational methods for comparative analysis of metagenomes from raw sequencing data and developed Machine Learning (ML) approaches to build predictive models for host phenotype prediction based on identified microbial markers. First, I implemented the subtractive assembly method(called CoSA) for comparative metagenomics that directly detects differential reads between two groups of metagenomes, from which microbial marker genes could be assembled and characterized. Secondly, I reported the curation of a repository of microbial marker genes and predictive models built from these markers for microbiome-based prediction of host phenotype, and a computational pipeline(named Mi2P) for using the repository. Lastly, I exploited locality sensitive hashing(LSH) as clustering algorithm to group billions of k-mers having similar abundance profiles across multiple samples into k-mers co-abundance groups (kCAGs) to improve the characterization of differential microbial markers. The overall goal of my research is to develop fast and efficient approaches for identifying microbial marker genes, and make them available for building predictive models for microbiome-based host phenotype predictions.
| Author | : Francisco A. Gómez Vela |
| Publisher | : MDPI |
| Total Pages | : 222 |
| Release | : 2021-02-05 |
| Genre | : Medical |
| ISBN | : 3039437712 |
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.
| Author | : Yinglin Xia |
| Publisher | : Springer |
| Total Pages | : 505 |
| Release | : 2018-10-06 |
| Genre | : Computers |
| ISBN | : 9811315345 |
This unique book addresses the statistical modelling and analysis of microbiome data using cutting-edge R software. It includes real-world data from the authors’ research and from the public domain, and discusses the implementation of R for data analysis step by step. The data 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. The book also discusses recent developments in statistical modelling and data analysis in microbiome research, as well as the latest advances in next-generation sequencing and big data in methodological development and applications. This timely book will greatly benefit all readers involved in microbiome, ecology and microarray data analyses, as well as other fields of research.
