Computational Metagenomics: Network, Classification and Assembly
Author | : Bo Liu |
Publisher | : |
Total Pages | : |
Release | : 2012 |
Genre | : |
ISBN | : |
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Author | : Bo Liu |
Publisher | : |
Total Pages | : |
Release | : 2012 |
Genre | : |
ISBN | : |
Author | : Zhong Wang |
Publisher | : World Scientific |
Total Pages | : 210 |
Release | : 2022-04-11 |
Genre | : Science |
ISBN | : 9811242488 |
Breakthroughs in high-throughput genome sequencing and high-performance computing technologies have empowered scientists to decode many genomes including our own. Now they have a bigger ambition: to fully understand the vast diversity of microbial communities within us and around us, and to exploit their potential for the improvement of our health and environment. In this new field called metagenomics, microbial genomes are sequenced directly from the habitats without lab cultivation. Computational metagenomics, however, faces both a data challenge that deals with tens of tera-bases of sequences and an algorithmic one that deals with the complexity of thousands of species and their interactions.This interdisciplinary book is essential reading for those who are interested in beginning their own journey in computational metagenomics. It is a prism to look through various intricate computational metagenomics problems and unravel their three distinctive aspects: metagenomics, data engineering, and algorithms. Graduate students and advanced undergraduates from genomics science or computer science fields will find that the concepts explained in this book can serve as stepping stones for more advanced topics, while metagenomics practitioners and researchers from similar disciplines may use it to broaden their knowledge or identify new research targets.
Author | : David J. Beale |
Publisher | : Springer |
Total Pages | : 324 |
Release | : 2016-12-05 |
Genre | : Science |
ISBN | : 3319463268 |
This book brings together contributions from global experts who have helped to facilitate the exciting and rapid advances that are taking place in microbial metabolomics. The main application of this field is in clinical and veterinary microbiology, but there is a great potential to apply metabolomics to help to better understand complex biological systems that are dominated by multiple-species microbial populations exposed to changing growth and nutritional conditions. In particular, environmental (e.g., water, soil), food (e.g., microbial spoilage, food pathogens), and agricultural and industrial applications are seen as developing areas for microbial metabolomics. As such, the book includes contributions with clinical, environmental, and industrial perspectives.
Author | : Richard Durbin |
Publisher | : Cambridge University Press |
Total Pages | : 372 |
Release | : 1998-04-23 |
Genre | : Science |
ISBN | : 113945739X |
Probabilistic models are becoming increasingly important in analysing the huge amount of data being produced by large-scale DNA-sequencing efforts such as the Human Genome Project. For example, hidden Markov models are used for analysing biological sequences, linguistic-grammar-based probabilistic models for identifying RNA secondary structure, and probabilistic evolutionary models for inferring phylogenies of sequences from different organisms. This book gives a unified, up-to-date and self-contained account, with a Bayesian slant, of such methods, and more generally to probabilistic methods of sequence analysis. Written by an interdisciplinary team of authors, it aims to be accessible to molecular biologists, computer scientists, and mathematicians with no formal knowledge of the other fields, and at the same time present the state-of-the-art in this new and highly important field.
Author | : Francis Martin |
Publisher | : Springer Nature |
Total Pages | : 370 |
Release | : 2022-12-15 |
Genre | : Science |
ISBN | : 1071628712 |
This volume guides researchers on how to characterize, image rare, and hitherto unknown taxa and their interactions, to identify new functions and biomolecules and to understand how environmental changes condition the activity and the response of the organisms living with us and in our environment. Chapters cover different organism types (i.e., archaea, bacteria, fungi, protest, microfauna and microeukaryotes) and propose detailed protocols to produce high quality DNA, to analyse active microbial communities directly involved in complex interactions or processes through stable isotope probing, to identify and characterize of new functional genes, to image in situ interactions and to apply bioinformatics analysis tools to complex metagenomic or RNAseq sequence data. Written in the successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible protocols, and notes on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Microbial Environmental Genomics (MEG): Methods and Protocols, Second Edition aims to serve as a primary research reference for researchers in microbiology working to in the expanding field of molecular ecology and environmental genomics.
Author | : Trevor C. Charles |
Publisher | : Springer |
Total Pages | : 256 |
Release | : 2017-10-09 |
Genre | : Science |
ISBN | : 3319615106 |
In this book, the latest tools available for functional metagenomics research are described.This research enables scientists to directly access the genomes from diverse microbial genomes at one time and study these “metagenomes”. Using the modern tools of genome sequencing and cloning, researchers have now been able to harness this astounding metagenomic diversity to understand and exploit the diverse functions of microorganisms. Leading scientists from around the world demonstrate how these approaches have been applied in many different settings, including aquatic and terrestrial habitats, microbiomes, and many more environments. This is a highly informative and carefully presented book, providing microbiologists with a summary of the latest functional metagenomics literature on all specific habitats.
Author | : Jian Ma |
Publisher | : Springer Nature |
Total Pages | : 508 |
Release | : |
Genre | : |
ISBN | : 1071639897 |
Author | : Peter Markstein |
Publisher | : Imperial College Press |
Total Pages | : 355 |
Release | : 2008 |
Genre | : Mathematics |
ISBN | : 1848162642 |
This proceedings volume contains 29 papers covering many of the latest developments in the fast-growing field of bioinformatics. The contributions span a wide range of topics, including computational genomics and genetics, protein function and computational proteomics, the transcriptome, structural bioinformatics, microarray data analysis, motif identification, biological pathways and systems, and biomedical applications.The papers not only cover theoretical aspects of bioinformatics but also delve into the application of new methods, with input from computation, engineering and biology disciplines. This multidisciplinary approach to bioinformatics gives these proceedings a unique viewpoint of the field.
Author | : Robert G. Beiko |
Publisher | : |
Total Pages | : 324 |
Release | : 2018 |
Genre | : Microbiology |
ISBN | : 9781493987283 |
Author | : Bernhard Schölkopf |
Publisher | : Springer Science & Business Media |
Total Pages | : 295 |
Release | : 2013-12-11 |
Genre | : Computers |
ISBN | : 3642411363 |
This book honours the outstanding contributions of Vladimir Vapnik, a rare example of a scientist for whom the following statements hold true simultaneously: his work led to the inception of a new field of research, the theory of statistical learning and empirical inference; he has lived to see the field blossom; and he is still as active as ever. He started analyzing learning algorithms in the 1960s and he invented the first version of the generalized portrait algorithm. He later developed one of the most successful methods in machine learning, the support vector machine (SVM) – more than just an algorithm, this was a new approach to learning problems, pioneering the use of functional analysis and convex optimization in machine learning. Part I of this book contains three chapters describing and witnessing some of Vladimir Vapnik's contributions to science. In the first chapter, Léon Bottou discusses the seminal paper published in 1968 by Vapnik and Chervonenkis that lay the foundations of statistical learning theory, and the second chapter is an English-language translation of that original paper. In the third chapter, Alexey Chervonenkis presents a first-hand account of the early history of SVMs and valuable insights into the first steps in the development of the SVM in the framework of the generalised portrait method. The remaining chapters, by leading scientists in domains such as statistics, theoretical computer science, and mathematics, address substantial topics in the theory and practice of statistical learning theory, including SVMs and other kernel-based methods, boosting, PAC-Bayesian theory, online and transductive learning, loss functions, learnable function classes, notions of complexity for function classes, multitask learning, and hypothesis selection. These contributions include historical and context notes, short surveys, and comments on future research directions. This book will be of interest to researchers, engineers, and graduate students engaged with all aspects of statistical learning.