Genotyping by Sequencing for Crop Improvement

In recent years, significant advancements have been made in the management of nutritional deficiency using genome engineering—enriching the nutritional properties of agricultural and horticultural crop plants such as wheat, rice, potatoes, grapes, and bananas. To meet the demands of the rapidly growing world population, researchers are developing a range of new genome engineering tools and strategies, from increasing the nutraceuticals in cereals and fruits, to decreasing the anti-nutrients in crop plants to improve the bioavailability of minerals and vitamins. Genome Engineering for Crop Improvement provides an up-to-date view of the use of genome editing for crop bio-fortification, improved bioavailability of minerals and nutrients, and enhanced hypo-allergenicity and hypo-immunogenicity. This volume examines a diversity of important topics including mineral and nutrient localization, metabolic engineering of carotenoids and flavonoids, genome engineering of zero calorie potatoes and allergen-free grains, engineering for stress resistance in crop plants, and more. Helping readers deepen their knowledge of the application of genome engineering in crop improvement, this book: Presents genetic engineering methods for developing edible oil crops, mineral translocation in grains, increased flavonoids in tomatoes, and cereals with enriched iron bioavailability Describes current genome engineering methods and the distribution of nutritional and mineral composition in important crop plants Offers perspectives on emerging technologies and the future of genome engineering in agriculture   Genome Engineering for Crop Improvement is an essential resource for academics, scientists, researchers, agriculturalists, and students of plant molecular biology, system biology, plant biotechnology, and functional genomics.

Genomic Applications for Crop Breeding: Abiotic Stress, Quality and Yield Improvement is the second of two volumes looking at the latest advances in genomic applications to crop breeding. This volume focuses on advances improving crop resistance to abiotic stresses such as extreme heat, drought, flooding as well as advances made in quality and yield improvement. Chapters examine advances in such key crops as rice, maize, and sugarcane, among others. Genomic Applications for Crop Breeding: Abiotic Stress, Quality and Yield Improvement complements the earlier volume on biotic stressors and will be an essential purchase for those interested in crop science and food production.

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.

Praise for the Second Edition «As a comprehensive statistics reference book for quality improvement, it certainly is one of the best books available.» —Technometrics This new edition continues to provide the most current, proven statistical methods for quality control and quality improvement The use of quantitative methods offers numerous benefits in the fields of industry and business, both through identifying existing trouble spots and alerting management and technical personnel to potential problems. Statistical Methods for Quality Improvement, Third Edition guides readers through a broad range of tools and techniques that make it possible to quickly identify and resolve both current and potential trouble spots within almost any manufacturing or nonmanufacturing process. The book provides detailed coverage of the application of control charts, while also exploring critical topics such as regression, design of experiments, and Taguchi methods. In this new edition, the author continues to explain how to combine the many statistical methods explored in the book in order to optimize quality control and improvement. The book has been thoroughly revised and updated to reflect the latest research and practices in statistical methods and quality control, and new features include: Updated coverage of control charts, with newly added tools The latest research on the monitoring of linear profiles and other types of profiles Sections on generalized likelihood ratio charts and the effects of parameter estimation on the properties of CUSUM and EWMA procedures New discussions on design of experiments that include conditional effects and fraction of design space plots New material on Lean Six Sigma and Six Sigma programs and training Incorporating the latest software applications, the author has added coverage on how to use Minitab software to obtain probability limits for attribute charts. new exercises have been added throughout the book, allowing readers to put the latest statistical methods into practice. Updated references are also provided, shedding light on the current literature and providing resources for further study of the topic. Statistical Methods for Quality Improvement, Third Edition is an excellent book for courses on quality control and design of experiments at the upper-undergraduate and graduate levels. the book also serves as a valuable reference for practicing statisticians, engineers, and physical scientists interested in statistical quality improvement.

Written by leading experts from industry and academia, this first single comprehensive resource addresses recent developments in next generation DNA sequencing technology and their impact on genome research, drug discovery and health care. As such, it presents a detailed comparative analysis of commercially available platforms as well as insights into alternative, emerging sequencing techniques. In addition, the book not only covers the principles of DNA sequencing techniques but also social, ethical and commercial aspects, the concept of personalized medicine and a five-year perspective of DNA sequencing.

This book aims to extract the «molecular genes» leading to craziness! Geniuses are the ones who are «crazy enough to think they can change the world» and boldly go where no one has gone before. Where no past habit and usage are available, there is no proof of viability, as nobody has done it yet, or even imagined it, and no roadmap for guidance or market study has come up with it. The authors call upon Leonardo Da Vinci, the Renaissance genius, who as strange as it seems, shared many traits of personality with that of Steve Jobs, in terms of the ways of performing. Da Vinci helps in understanding Jobs, and hence Apple, with his unique way of designing radically novel concepts, which were actually quite crazy for his time. In order to shed light on a special creative posture, the indomitable sense of specifying undecidable objects – a hallmark of the late Steve Jobs – is what led the authors to match it with a specific design innovation theory. A real theory, backed by solid mathematical proof, exists and can account for the business virtue of a prolific ability to move into unknown crazy fields! The authors postulate that, by bringing the power of C-K theory to crack open a number of previous observations made about Apple’s methods, it is possible to identify most of the genes of this company. The authors analyze how and why an Apple way of doing business is radically different from standard business practices and why it is so successful. Genes are a measure of the entity at hand and can encourage past business education routine approaches, then become transferable across the spectrum of the socio-economic world.

Tag-based approaches were originally designed to increase the throughput of capillary sequencing, where concatemers of short sequences were first used in expression profiling. New Next Generation Sequencing methods largely extended the use of tag-based approaches as the tag lengths perfectly match with the short read length of highly parallel sequencing reactions. Tag-based approaches will maintain their important role in life and biomedical science, because longer read lengths are often not required to obtain meaningful data for many applications. Whereas genome re-sequencing and de novo sequencing will benefit from ever more powerful sequencing methods, analytical applications can be performed by tag-based approaches, where the focus shifts from 'sequencing power' to better means of data analysis and visualization for common users. Today Next Generation Sequence data require powerful bioinformatics expertise that has to be converted into easy-to-use data analysis tools. The book's intention is to give an overview on recently developed tag-based approaches along with means of their data analysis together with introductions to Next-Generation Sequencing Methods, protocols and user guides to be an entry for scientists to tag-based approaches for Next Generation Sequencing.

Reliability is one of the most important attributes for the products and processes of any company or organization. This important work provides a powerful framework of domain-independent reliability improvement and risk reducing methods which can greatly lower risk in any area of human activity. It reviews existing methods for risk reduction that can be classified as domain-independent and introduces the following new domain-independent reliability improvement and risk reduction methods: Separation Stochastic separation Introducing deliberate weaknesses Segmentation Self-reinforcement Inversion Reducing the rate of accumulation of damage Permutation Substitution Limiting the space and time exposure Comparative reliability models The domain-independent methods for reliability improvement and risk reduction do not depend on the availability of past failure data, domain-specific expertise or knowledge of the failure mechanisms underlying the failure modes. Through numerous examples and case studies, this invaluable guide shows that many of the new domain-independent methods improve reliability at no extra cost or at a low cost. Using the proven methods in this book, any company and organisation can greatly enhance the reliability of its products and operations.