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This is an introductory text and laboratory manual to be used primarily in undergraduate courses. It is also useful for graduate students and research scientists who require an introduction to the theory and methods of nanopore sequencing. The book has clear explanations of the principles of this emerging technology, together with instructional material written by experts that describes how to use a MinION nanopore instrument for sequencing in research or the classroom.At Harvard University the book serves as a textbook and lab manual for a university laboratory course designed to intensify the intellectual experience of incoming undergraduates while exploring biology as a field of concentration. Nanopore sequencing is an ideal topic as a path to encourage students about the range of courses they will take in Biology by pre-emptively addressing the complaint about having to take a course in Physics or Maths while majoring in Biology. The book addresses this complaint by concretely demonstrating the range of topics — from electricity to biochemistry, protein structure, molecular engineering, and informatics — that a student will have to master in subsequent courses if he or she is to become a scientist who truly understands what his or her biology instrument is measuring when investigating biological phenomena.
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.
This book provides insights into the current state of sorghum genomics. It particularly focuses on the tools and strategies employed in genome sequencing and analysis, public and private genomic resources and how all this information is leading to direct outcomes for plant breeders. The advent of affordable whole genome sequencing in combination with existing cereal functional genomics data has enabled the leveraging of the significant novel diversity available in sorghum, the genome of which was fully sequenced in 2009, providing an unmatched resource for the genetic improvement of sorghum and other grass species. Cultivated grain sorghum is a food and feed cereal crop adapted to hot and dry climates, and is a staple for 500 million of the world’s poorest people. Globally, sorghum is also an important source of animal feed and forage, an emerging biofuel crop and model for C4 grasses, particularly genetically complex sugarcane.
Timely and accurate pathogen diagnosis is critical for effective treatment, outbreak prevention and precise antibiotics administration of infectious diseases, which remains a challenge in clinical practice. Metagenomic next generation sequencing (mNGS) allows researchers and laboratory specialists to analyze the mixed collection of sequencing reads in human clinical samples, including sequences from bacteria, viruses, fungi and parasites besides the host. This new technology shows great potential in pathogen diagnosis. The sensitivity and faster turnaround time are higher than conventional clinical microbiology tests, especially for fastidious and atypical pathogens. However, current mNGS based pathogen detection and diagnosis are facing challenges from both technical and practical aspects. First, sequencing noise can be introduced from different steps such as samples preparation, sequencing, data analysis and reporting algorithm. Also, the interpretation of the results such as detection limits and detection rates are not straightforward to clinicians, compared with traditional culture-based technologies. Such challenges and considerations should be fully addressed before the wild application of mNGS as a pathogen detection tool. Further, these clarifications might help to properly find the best clinical application scenarios, refine the medical treatments, and improve the clinical effectiveness of mNGS in complex infectious diseases. This Research Topic is focused on the application of mNGS for infectious diseases diagnosis and treatment, including experiment process and clinical usage. Its purpose is to level up the clarification of NGS process, to support the development of efficient experiment methods, data interpretation algorithms and to report good examples of related clinical applications. We hope this Research Topic could facilitate the discovery of novel methods and help NGS specialists and clinicians to know mutual concerns and work together to improve the clinical effectiveness of mNGS.
This volume presents the proceedings of the joint conference of the European Medical and Biological Engineering Conference (EMBEC) and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics (NBC), held in Tampere, Finland, in June 2017. The proceedings present all traditional biomedical engineering areas, but also highlight new emerging fields, such as tissue engineering, bioinformatics, biosensing, neurotechnology, additive manufacturing technologies for medicine and biology, and bioimaging, to name a few. Moreover, it emphasizes the role of education, translational research, and commercialization.
​The recent advances in genomics are continuing to reshape our approach to diagnostics, prognostics and therapeutics in oncologic and other disorders. A paradigm shift in pharmacogenomics and in the diagnosis of genetic inherited diseases and infectious diseases is unfolding as the result of implementation of next generation genomic technologies. With rapidly growing knowledge and applications driving this revolution, along with significant technologic and cost changes, genomic approaches are becoming the primary methods in many laboratories and for many diseases. As a result, a plethora of clinical genomic applications have been implemented in diagnostic pathology laboratories, and the applications and demands continue to evolve rapidly. This has created a tremendous need for a comprehensive resource on genomic applications in clinical and anatomic pathology. We believe that our current textbook provides such a resource to practicing molecular pathologists, hematopathologists and other subspecialized pathologists, general pathologists, pathology and other trainees, oncologists, geneticists and a growing spectrum of other clinicians. With periodic updates and a sufficiently rapid time from submission to publication, this textbook will be the resource of choice for many professionals and teaching programs. Its focus on genomics parallels the evolution of these technologies as primary methods in the clinical lab. The rapid evolution of genomics and its applications in medicine necessitates the (frequent) updating of this publication. This text will provide a state-of-the art review of the scientific principles underlying next generation genomic technologies and the required bioinformatics approaches to analyses of the daunting amount of data generated by current and emerging genomic technologies. Implementation roadmaps for various clinical assays such as single gene, gene panels, whole exome and whole genome assays will be discussed together with issues related to reporting and the pathologist’s role in interpretation and clinical integration of genomic tests results. Genomic applications for site-specific solid tumors and hematologic neoplasms will be detailed. Genomic applications in pharmacogenomics, inherited genetic diseases and infectious diseases will also be discussed. The latest iteration of practice recommendations or guidelines in genomic testing put forth by stakeholder professional organizations such as the College of American Pathology and the Association for Molecular Pathology, will be discussed as well as regulatory issues and laboratory accreditation related to genomic testing. All chapters will be written by experts in their fields and will include the most up to date scientific and clinical information.
The book provides a comprehensive understanding of cutting-edge research and applications at the intersection of genomics and advanced AI techniques and serves as an essential resource for researchers, bioinformaticians, and practitioners looking to leverage genomics data for AI-driven insights and innovations. The book encompasses a wide range of topics, starting with an introduction to genomics data and its unique characteristics. Each chapter unfolds a unique facet, delving into the collaborative potential and challenges that arise from advanced technologies. It explores image analysis techniques specifically tailored for genomic data. It also delves into deep learning showcasing the power of convolutional neural networks (CNN) and recurrent neural networks (RNN) in genomic image analysis and sequence analysis. Readers will gain practical knowledge on how to apply deep learning techniques to unlock patterns and relationships in genomics data. Transfer learning, a popular technique in AI, is explored in the context of genomics, demonstrating how knowledge from pre-trained models can be effectively transferred to genomic datasets, leading to improved performance and efficiency. Also covered is the domain adaptation techniques specifically tailored for genomics data. The book explores how genomics principles can inspire the design of AI algorithms, including genetic algorithms, evolutionary computing, and genetic programming. Additional chapters delve into the interpretation of genomic data using AI and ML models, including techniques for feature importance and visualization, as well as explainable AI methods that aid in understanding the inner workings of the models. The applications of genomics in AI span various domains, and the book explores AI-driven drug discovery and personalized medicine, genomic data analysis for disease diagnosis and prognosis, and the advancement of AI-enabled genomic research. Lastly, the book addresses the ethical considerations in integrating genomics with AI, computer vision, and machine learning. Audience The book will appeal to biomedical and computer/data scientists and researchers working in genomics and bioinformatics seeking to leverage AI, computer vision, and machine learning for enhanced analysis and discovery; healthcare professionals advancing personalized medicine and patient care; industry leaders and decision-makers in biotechnology, pharmaceuticals, and healthcare industries seeking strategic insights into the integration of genomics and advanced technologies.
The binding of proteins to DNA and the manipulation of DNA by proteins are crucial aspects of the biological role of DNA in the living cell. This book provides a comprehensive and lucid discussion of the molecular interactions involved.
Next generation sequencing (NGS) has surpassed the traditional Sanger sequencing method to become the main choice for large-scale, genome-wide sequencing studies with ultra-high-throughput production and a huge reduction in costs. The NGS technologies have had enormous impact on the studies of structural and functional genomics in all the life sciences. In this book, Next Generation Sequencing Advances, Applications and Challenges, the sixteen chapters written by experts cover various aspects of NGS including genomics, transcriptomics and methylomics, the sequencing platforms, and the bioinformatics challenges in processing and analysing huge amounts of sequencing data. Following an overview of the evolution of NGS in the brave new world of omics, the book examines the advances and challenges of NGS applications in basic and applied research on microorganisms, agricultural plants and humans. This book is of value to all who are interested in DNA sequencing and bioinformatics across all fields of the life sciences.
The domestication of grapes dates back five thousand years ago and has spread to nearly all continents. In recent years, grape acreage has increased dramatically in new regions, including the United States of America, Chile, Asia (China and India), and Turkey. A major limiting factor to the sustained production of premium grapes and wines is infections by viruses. The advent of powerful molecular and metagenomics technologies, such as molecular cloning and next generation sequencing, allowed the discovery of new viruses from grapes. To date, grapevine is susceptible to 64 viruses that belong to highly diverse taxonomic groups. The most damaging diseases include: (1) infectious degeneration; (2) leafroll disease complex; and (3) rugose wood complex. Recently, two new disease syndromes have been recognized: Syrah decline and red blotch. Losses due to fanleaf degeneration are estimated at $1 billion annually in France alone. Other diseases including leafroll, rugose wood, Syrah de cline and red blotch can result in total crop loss several years post-infection. This situation is further exacerbated by mixed infections with multiple viruses and other biotic as well as adverse abiotic environmental conditions, such as drought and winter damage, causing even greater destruction. The book builds upon the last handbook (written over twenty years ago) on the part of diagnostics and extensively expands its scope by inclusion of molecular biology aspects of select viruses that are widespread and economically most important. This includes most current information on the biology, transmission, genome replication, transcription, subcellular localization, as well as virus-host interactions. It also touches on several novel areas of scientific inquiry. It also contains suggested directions for future research in the field of grapevine virology.