Related books on 'sequence-analysis'Return to life science book list
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Life science and biomedical books
publisher: Humana Press, published: 1996-12-01
sales rank: 5149329
Leading researchers concisely summarize their hands-on experiences and methods for successfully using the most popular sequence analysis software packages available. These experts demonstrate how to examine the data produced by modern automated sequencers, how to assess its quality, how to remove extraneous data, how to align multiple overlapping sequence fragments for either assembly into sequence contigs or comparison with similar sequences from different sources. Procedures for comparing newly derived sequences with the massive amounts of information in the sequence databases are fully covered, as are techniques for performing restriction analysis, searching for open reading frames, calculating the translation products of open reading frames, and making detailed analyses of the expressed "proteins."
publisher: Springer, published: 2000-10-31
sales rank: 6064183
Computational and Evolutionary Analysis of HIV Molecular Sequences is for all researchers interested in HIV research, even those who only have a nodding acquaintance with computational biology (or those who are familiar with some, but not all, aspects of the field). HIV research is unusual in that it brings together scientists from a wide range of disciplines: clinicians, pathologists, immunologists, epidemiologists, virologists, computational biologists, structural biologists, evolutionary biologists, statisticians and mathematicians. This book seeks to bridge the gap between these groups, in both subject matter and terminology. Focused largely on HIV genetic variation, Computational and Evolutionary Analysis of HIV Molecular Sequences covers such issues as sampling and processing sequences, population genetics, phylogenetics and drug targets.
by: Tieng K. Yap
publisher: Kluwer Academic Publishers, published: 1996-04-30
sales rank: 7416675
High Performance Computational Methods for Biological Sequence Analysis presents biological sequence analysis using an interdisciplinary approach that integrates biological, mathematical and computational concepts. These concepts are presented so that computer scientists and biomedical scientists can obtain the necessary background for developing better algorithms and applying parallel computational methods. This book will enable both groups to develop the depth of knowledge needed to work in this interdisciplinary field.
This work focuses on high performance computational approaches that are used to perform computationally intensive biological sequence analysis tasks: pairwise sequence comparison, multiple sequence alignment, and sequence similarity searching in large databases. These computational methods are becoming increasingly important to the molecular biology community allowing researchers to explore the increasingly large amounts of sequence data generated by the Human Genome Project and other related biological projects. The approaches presented by the authors are state-of-the-art and show how to reduce analysis times significantly, sometimes from days to minutes.
High Performance Computational Methods for Biological Sequence Analysis is tremendously important to biomedical science students and researchers who are interested in applying sequence analyses to their studies, and to computational science students and researchers who are interested in applying new computational approaches to biological sequence analyses.
publisher: Oxford University Press, USA, published: 1987-03-01
sales rank: 10692623
publisher: Humana Press, published: 2008-06-16
sales rank: 3249979
In this book, leading researchers in the field of Bioinformatics provide a selection of the most useful and widely applicable methods, able to be applied as is, or with minor variations, to many specific problems. Over 80 authors from around the globe contribute to the two volumes, including many leading experts in their respective subjects. They encompass topics from across the diverse field of bioinformatics through its broad scope, combining to provide an inter-disciplinary collaboration involving biologists, biochemists, physicists, mathematicians, statisticians and computer scientists.
publisher: Humana Press, published: 2009-05-06
sales rank: 4029117
Over the past two decades, expressed sequence tags (ESTs - single pass reads from randomly selected cDNAs), have proven to be a remarkably cost-effective route for the purposes of gene discovery. Gaining in popularity, millions of ESTs have now been generated for over a thousand different species. In Expressed Sequence Tags (ESTs): Generation and Analysis, leading experts in the field introduce the reader to many of the fundamental concepts underlying the generation and analysis of ESTs through readily accessible and affordable sequencing technologies. The volume focuses on various methods used to generate, process and analyze EST datasets, while also exploring the use of EST technology for other purposes such as expression profiling, analysis of alternative transcripts, and phylogenomics. Written in the highly successful Methods in Molecular Biology™ series format, chapters include brief introductions to their respective topics, lists of the necessary materials and programs, step-by-step, readily reproducible protocols, and Notes sections, which highlight tips on troubleshooting and avoiding known pitfalls.
Cutting-edge and easy to use, Expressed Sequence Tags (ESTs): Generation and Analysis serves as an ideal reference for scientists continuing the vital investigation of genes and the genome.
publisher: CRC Press, published: 2004-06-25
sales rank: 7088717
Digital image sequences (including digital video) are increasingly common and important components in technical applications ranging from medical imaging and multimedia communications to autonomous vehicle navigation. The immense popularity of DVD video and the introduction of digital television make digital video ubiquitous in the consumer domain.
Digital Image Sequence Processing, Compression, and Analysis provides an overview of the current state of the field, as analyzed by leading researchers. An invaluable resource for planning and conducting research in this area, the book conveys a unified view of potential directions for further industrial development. It offers an in-depth treatment of the latest perspectives on processing, compression, and analysis of digital image sequences.
Research involving digital image sequences remains extremely active. The advent of economical sequence acquisition, storage, and display devices, together with the availability of computing power, opens new areas of opportunity. This volume delivers the background necessary to understand the strengths and weaknesses of current techniques and the directions that consumer and technical applications may take over the coming decade.
publisher: Cambridge University Press, published: 2009-04-20
sales rank: 203908
The Phylogenetic Handbook is a broad, hands on guide to theory and practice of nucleotide and protein phylogenetic analysis. This second edition includes six new chapters, covering topics such as Bayesian inference, tree topology testing and the impact of recombination on phylogenies, as well as a detailed section on molecular adaptation. The book has a stronger focus on hypothesis testing than the previous edition, with more extensive discussions on recombination analysis, detecting molecular adaptation and genealogy-based population genetics. Many chapters include elaborate practical sections, which have been updated to introduce the reader to the most recent versions of sequence analysis and phylogeny software, including BLAST, FastA, Clustal, T-coffee, Muscle, DAMBE, Tree-puzzle, Phylip, MEGA, PAUP*, IQPNNI, CONSEL, ModelTest, Prottest, PAML, HYPHY, MrBayes, BEAST, LAMARC, SplitsTree, and RDP. Many analysis tools are described by their original authors, resulting in clear explanations that constitute an ideal teaching guide for advanced-level undergraduate and graduate students.
by: Larry S. Shapiro
publisher: Cambridge University Press, published: 2005-09-15
sales rank: 6231702
Computer vision is a rapidly growing field which aims to make computers 'see' as effectively as humans. In this book Dr Shapiro presents a new computer vision framework for interpreting time-varying imagery. This is an important task, since movement reveals valuable information about the environment. The fully-automated system operates on long, monocular image sequences containing multiple, independently-moving objects, and demonstrates the practical feasibility of recovering scene structure and motion in a bottom-up fashion. Real and synthetic examples are given throughout, with particular emphasis on image coding applications. Novel theory is derived in the context of the affine camera, a generalisation of the familiar scaled orthographic model. Analysis proceeds by tracking 'corner features' through successive frames and grouping the resulting trajectories into rigid objects using new clustering and outlier rejection techniques. The three-dimensional motion parameters are then computed via 'affine epipolar geometry', and 'affine structure' is used to generate alternative views of the object and fill in partial views. The use of all available features (over multiple frames) and the incorporation of statistical noise properties substantially improves existing algorithms, giving greater reliability and reduced noise sensitivity.
by: Stephen Winters-Hilt
publisher: Lulu.com, published: 2011-05-02
sales rank: 3165008
This is intended to be a simple and accessible book on machine learning methods and their application in computational genomics and nanopore transduction detection. This book has arisen from eight years of teaching one-semester courses on various machine-learning, cheminformatics, and bioinformatics topics. The book begins with a description of ad hoc signal acquisition methods and how to orient on signal processing problems with the standard tools from information theory and signal analysis. A general stochastic sequential analysis (SSA) signal processing architecture is then described that implements Hidden Markov Model (HMM) methods. Methods are then shown for classification and clustering using generalized Support Vector Machines, for use with the SSA Protocol, or independent of that approach. Optimization metaheuristics are used for tuning over algorithmic parameters throughout. Hardware implementations and short code examples of the various methods are also described.