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Covers the fundamentals and techniques of multiple biological sequence alignment and analysis, and shows readers how to choose the appropriate sequence analysis tools for their tasks This book describes the traditional and modern approaches in biological sequence alignment and homology search. This book contains 11 chapters, with Chapter 1 providing basic information on biological sequences. Next, Chapter 2 contains fundamentals in pair-wise sequence alignment, while Chapters 3 and 4 examine popular existing quantitative models and practical clustering techniques that have been used in multiple sequence alignment. Chapter 5 describes, characterizes and relates many multiple sequence alignment models. Chapter 6 describes how traditionally phylogenetic trees have been constructed, and available sequence knowledge bases can be used to improve the accuracy of reconstructing phylogeny trees. Chapter 7 covers the latest methods developed to improve the run-time efficiency of multiple sequence alignment. Next, Chapter 8 covers several popular existing multiple sequence alignment server and services, and Chapter 9 examines several multiple sequence alignment techniques that have been developed to handle short sequences (reads) produced by the Next Generation Sequencing technique (NSG). Chapter 10 describes a Bioinformatics application using multiple sequence alignment of short reads or whole genomes as input. Lastly, Chapter 11 provides a review of RNA and protein secondary structure prediction using the evolution information inferred from multiple sequence alignments. - Covers the full spectrum of the field, from alignment algorithms to scoring methods, practical techniques, and alignment tools and their evaluations - Describes theories and developments of scoring functions and scoring matrices -Examines phylogeny estimation and large-scale homology search Multiple Biological Sequence Alignment: Scoring Functions, Algorithms and Applications is a reference for researchers, engineers, graduate and post-graduate students in bioinformatics, and system biology and molecular biologists. Ken Nguyen, PhD, is an associate professor at Clayton State University, GA, USA. He received his PhD, MSc and BSc degrees in computer science all from Georgia State University. His research interests are in databases, parallel and distribute computing and bioinformatics. He was a Molecular Basis of Disease fellow at Georgia State and is the recipient of the highest graduate honor at Georgia State, the William M. Suttles Graduate Fellowship. Xuan Guo, PhD, is a postdoctoral associate at Oak Ridge National Lab, USA. He received his PhD degree in computer science from Georgia State University in 2015. His research interests are in bioinformatics, machine leaning, and cloud computing. He is an editorial assistant of International Journal of Bioinformatics Research and Applications. Yi Pan, PhD, is a Regents' Professor of Computer Science and an Interim Associate Dean and Chair of Biology at Georgia State University. He received his BE and ME in computer engineering from Tsinghua University in China and his PhD in computer science from the University of Pittsburgh. Dr. Pan's research interests include parallel and distributed computing, optical networks, wireless networks and bioinformatics. He has published more than 180 journal papers with about 60 papers published in various IEEE/ACM journals. He is co-editor along with Albert Y. Zomaya of the Wiley Series in Bioinformatics.

High-throughput, next generation sequencing (NGS) technologies are capable of producing a huge amount of sequence data in a relatively short time and have revolutionized genome research in recent years. The powerful and flexible nature of NGS has made it an indispensable tool for a broad spectrum of biological sciences, and NGS technologies have transformed scientific research in many fields. Written by experts from around the world, this book explores the most recent advances in NGS instrumentation and data analysis. The book begins with a comprehensive description of current NGS platforms, their sequencing chemistries, instrument specifications, and general workflows and procedures. A separate chapter is dedicated to low-quantity, single molecule sequencing technology. Further chapters explore the application of NGS technologies in various fields, including polymorphism detection, sRNA research, rare variant detection, large variant detection, exome sequencing, plant development studies, microbial metagenomics, and studies on the human microbiome. Practical and cutting-edge, this volume will assist all scientists who wish to apply these innovative research tools.

Covers the fundamentals and techniques of multiple biological sequence alignment and analysis, and shows readers how to choose the appropriate sequence analysis tools for their tasks This book describes the traditional and modern approaches in biological sequence alignment and homology search.  This book contains 11 chapters, with Chapter 1.

Pooling designs have been widely used in various aspects of DNA sequencing. In biological applications, the well-studied mathematical problem called “group testing” shifts its focus to nonadaptive algorithms while the focus of traditional group testing is on sequential algorithms. Biological applications also bring forth new models not previously considered, such as the error-tolerant model, the complex model, and the inhibitor model. This book is the first attempt to collect all the significant research on pooling designs in one convenient place.

Genetic Databases offers a timely analysis of the underlying tensions, contradictions and limitations of the current regulatory frameworks for, and policy debates about, genetic databases. Drawing on original empirical research and theoretical debates in the fields of sociology, anthropology and legal studies, the contributors to this book challenge the prevailing orthodoxy of informed consent and explore the relationship between personal privacy and the public good. They also consider the multiple meanings attached to human tissue and the role of public consultations and commercial involvement in the creation and use of genetic databases. The authors argue that policy and regulatory frameworks produce a representation of participation that is often at odds with the experiences and understandings of those taking part. The findings present a serious challenge for public policy to provide mechanisms to safeguard the welfare of individuals participating in genetic databases.

Reports and discusses the result of a recent DNA barcoding work to help define a threshold between intra- and inter-specific genetic distances to help identify candidate species. Uses a combination of published and new 16S mitochondrial rDNA sequences from 60 frog species known to occur in French Guiana, most of which are considered to be widely distributed across the Guianan and Amazonian regions, to obtain a minimum estimate of the number of undescribed species of amphibians in this region. Describes the three methods used in the analysis and combine the IBD and distance-based analysis to evaluate threshold values for the identification of candidate species in amphibians. Source: National Library of New Zealand Te Puna Matauranga o Aotearoa, licensed by the Department of Internal Affairs for re-use under the Creative Commons Attribution 3.0 New Zealand Licence.

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.

Advances in sequencing technology have allowed scientists to study the human genome in greater depth and on a larger scale than ever before – as many as hundreds of millions of short reads in the course of a few days. But what are the best ways to deal with this flood of data? Algorithms for Next-Generation Sequencing is an invaluable tool for students and researchers in bioinformatics and computational biology, biologists seeking to process and manage the data generated by next-generation sequencing, and as a textbook or a self-study resource. In addition to offering an in-depth description of the algorithms for processing sequencing data, it also presents useful case studies describing the applications of this technology.

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