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The fourth edition of this classical reference book can once again be relied upon to present a cohesive and up-to-date exposition of all aspects of human and medical genetics. Human genetics has become one of the main basic sciences in medicine, and molecular genetics is increasingly becoming a major part of this field. This new edition integrates a wealth of new information - mainly describing the influence of the "molecular revolution" - including the principles of epigenetic processes which together create the phenotype of a human being. Other revisions are an improved layout, sub-division into a larger number of chapters, as well as two-colour print throughout for ease of reference, and many of the figures are now in full colour. For graduates and those already working in medical genetics.
The recent explosive growth of biological data has lead to a rapid increase in the number of molecular biology databases. Held in many different locations and often using varying interfaces and non-standard data formats, integrating and comparing data from these multiple databases can be difficult and time-consuming. This book provides an overview of the key tools currently available for large-scale comparisons of gene sequences and annotations, focusing on the databases and tools from the University of California, Santa Cruz (UCSC), Ensembl, and the National Centre for Biotechnology Information (NCBI). Written specifically for biology and bioinformatics students and researchers, it aims to give an appreciation of the methods by which the browsers and their databases are constructed, enabling readers to determine which tool is the most appropriate for their requirements. Each chapter contains a summary and exercises to aid understanding and promote effective use of these important tools.
The success of individualized medicine, advanced crops, and new and sustainable energy sources requires thoroughly annotated genomic information and the integration of this information into a coherent model. A thorough overview of this field, Genome Annotation explores automated genome analysis and annotation from its origins to the challenges of next-generation sequencing data analysis. The book initially takes you through the last 16 years since the sequencing of the first complete microbial genome. It explains how current analysis strategies were developed, including sequencing strategies, statistical models, and early annotation systems. The authors then present visualization techniques for displaying integrated results as well as state-of-the-art annotation tools, including MAGPIE, Ensembl, Bluejay, and Galaxy. They also discuss the pipelines for the analysis and annotation of complex, next-generation DNA sequencing data. Each chapter includes references and pointers to relevant tools. As very few existing genome annotation pipelines are capable of dealing with the staggering amount of DNA sequence information, new strategies must be developed to accommodate the needs of today’s genome researchers. Covering this topic in detail, Genome Annotation provides you with the foundation and tools to tackle this challenging and evolving area. Suitable for both students new to the field and professionals who deal with genomic information in their work, the book offers two genome annotation systems on an accompanying CD-ROM.
This volume explores databases containing genome-based data and genome-wide analyses. This book covers databases from all eukaryotic taxa, except plants. The chapters describe database contents and classic use-cases, which assist in accessing eukaryotic genomic data and encouraging comparative genomic research. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, step-by-step, readily reproducible computational protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and authoritative, Eukaryotic Genomic Databases: Methods and Protocols is a valuable resource for geneticists and molecular biologists who are interested in the latest eukaryotic genomics data. The chapters 'PomBase: The Scientific Resource for Fission Yeast' and 'The Ensembl Genome Browser: Strategies for Accessing Eukaryotic Genome Data' are available open access under a CC BY 4.0 license via link.springer.com.
This textbook describes recent advances in genomics and bioinformatics and provides numerous examples of genome data analysis that illustrate its relevance to real world problems and will improve the reader’s bioinformatics skills. Basic data preprocessing with normalization and filtering, primary pattern analysis, and machine learning algorithms using R and Python are demonstrated for gene-expression microarrays, genotyping microarrays, next-generation sequencing data, epigenomic data, and biological network and semantic analyses. In addition, detailed attention is devoted to integrative genomic data analysis, including multivariate data projection, gene-metabolic pathway mapping, automated biomolecular annotation, text mining of factual and literature databases, and integrated management of biomolecular databases. The textbook is primarily intended for life scientists, medical scientists, statisticians, data processing researchers, engineers, and other beginners in bioinformatics who are experiencing difficulty in approaching the field. However, it will also serve as a simple guideline for experts unfamiliar with the new, developing subfield of genomic analysis within bioinformatics.
Bioinformatics for Beginners: Genes, Genomes, Molecular Evolution, Databases and Analytical Tools provides a coherent and friendly treatment of bioinformatics for any student or scientist within biology who has not routinely performed bioinformatic analysis. The book discusses the relevant principles needed to understand the theoretical underpinnings of bioinformatic analysis and demonstrates, with examples, targeted analysis using freely available web-based software and publicly available databases. Eschewing non-essential information, the work focuses on principles and hands-on analysis, also pointing to further study options. Avoids non-essential coverage, yet fully describes the field for beginners Explains the molecular basis of evolution to place bioinformatic analysis in biological context Provides useful links to the vast resource of publicly available bioinformatic databases and analysis tools Contains over 100 figures that aid in concept discovery and illustration
This detailed book presents recent methodologies for the task of inspecting the genomic world of plants, extracting valuable information, and presenting it in a readable way. With a focus on bioinformatics tools, the volume explores phylogenetics and evolution, Omics analysis, as well as experimental procedures for trait characterization. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of vital expert implementation advice that will lead to successful results. Authoritative and practical, Plant Comparative Genomics serves as an ideal resource for researchers looking to implement comparative tools in order to explore their genomic data for their daily scientific work.
Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming, to machine learning and statistics, to the latest genomic data analysis techniques. The text provides accessible information and explanations, always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary, it requires different starting points for people with different backgrounds. For example, a biologist might skip sections on basic genome biology and start with R programming, whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics, supervised and unsupervised learning techniques that are important in data modeling, and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis, such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics, such as heatmaps, meta-gene plots, and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets, such as RNA-seq, ChIP-seq, and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology, Max Delbrück Center, Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015.
Bioinformatics derives knowledge from computer analysis of biological data. In particular, genomic and transcriptomic datasets are processed, analysed and, whenever possible, associated with experimental results from various sources, to draw structural, organizational, and functional information relevant to biology. Research in bioinformatics includes method development for storage, retrieval, and analysis of the data. Bioinformatics in Aquaculture provides the most up to date reviews of next generation sequencing technologies, their applications in aquaculture, and principles and methodologies for the analysis of genomic and transcriptomic large datasets using bioinformatic methods, algorithm, and databases. The book is unique in providing guidance for the best software packages suitable for various analysis, providing detailed examples of using bioinformatic software and command lines in the context of real world experiments. This book is a vital tool for all those working in genomics, molecular biology, biochemistry and genetics related to aquaculture, and computational and biological sciences.
Essential Bioinformatics is a concise yet comprehensive textbook of bioinformatics, which provides a broad introduction to the entire field. Written specifically for a life science audience, the basics of bioinformatics are explained, followed by discussions of the state-of-the-art computational tools available to solve biological research problems. All key areas of bioinformatics are covered including biological databases, sequence alignment, genes and promoter prediction, molecular phylogenetics, structural bioinformatics, genomics and proteomics. The book emphasizes how computational methods work and compares the strengths and weaknesses of different methods. This balanced yet easily accessible text will be invaluable to students who do not have sophisticated computational backgrounds. Technical details of computational algorithms are explained with a minimum use of mathematical formulae; graphical illustrations are used in their place to aid understanding. The effective synthesis of existing literature as well as in-depth and up-to-date coverage of all key topics in bioinformatics make this an ideal textbook for all bioinformatics courses taken by life science students and for researchers wishing to develop their knowledge of bioinformatics to facilitate their own research.