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Genome Sequencing Technology and Algorithms by Sun Kim Pdf
The 2003 completion of the Human Genome Project was just one step in the evolution of DNA sequencing. This trailblazing work gives researchers unparalleled access to state-of-the-art DNA sequencing technologies, new algorithmic sequence assembly techniques, and emerging methods for both resequencing and genome analysis.
Algorithms for Next-Generation Sequencing Data by Mourad Elloumi Pdf
The 14 contributed chapters in this book survey the most recent developments in high-performance algorithms for NGS data, offering fundamental insights and technical information specifically on indexing, compression and storage; error correction; alignment; and assembly. The book will be of value to researchers, practitioners and students engaged with bioinformatics, computer science, mathematics, statistics and life sciences.
Next Generation Sequencing and Sequence Assembly by Ali Masoudi-Nejad,Zahra Narimani,Nazanin Hosseinkhan Pdf
The goal of this book is to introduce the biological and technical aspects of next generation sequencing methods, as well as algorithms to assemble these sequences into whole genomes. The book is organized into two parts; part 1 introduces NGS methods and part 2 reviews assembly algorithms and gives a good insight to these methods for readers new to the field. Gathering information, about sequencing and assembly methods together, helps both biologists and computer scientists to get a clear idea about the field. Chapters will include information about new sequencing technologies such as ChIp-seq, ChIp-chip, and De Novo sequence assembly.
Algorithms for Next-Generation Sequencing by Wing-Kin Sung Pdf
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.
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.
Next Generation Sequencing Technologies and Challenges in Sequence Assembly by Sara El-Metwally,Osama M. Ouda,Mohamed Helmy Pdf
The introduction of Next Generation Sequencing (NGS) technologies resulted in a major transformation in the way scientists extract genetic information from biological systems, revealing limitless insight about the genome, transcriptome and epigenome of any species. However, with NGS, came its own challenges that require continuous development in the sequencing technologies and bioinformatics analysis of the resultant raw data and assembly of the full length genome and transcriptome. Such developments lead to outstanding improvements of the performance and coverage of sequencing and improved quality for the assembled sequences, nevertheless, challenges such as sequencing errors, expensive processing and memory usage for assembly and sequencer specific errors remains major challenges in the field. This book aims to provide brief overviews the NGS field with special focus on the challenges facing the NGS field, including information on different experimental platforms, assembly algorithms and software tools, assembly error correction approaches and the correlated challenges.
C. Alexander Valencia,M. Ali Pervaiz,Ammar Husami,Yaping Qian,Kejian Zhang
Author : C. Alexander Valencia,M. Ali Pervaiz,Ammar Husami,Yaping Qian,Kejian Zhang Publisher : Springer Science & Business Media Page : 94 pages File Size : 51,6 Mb Release : 2013-10-16 Category : Medical ISBN : 9781461490326
Next Generation Sequencing Technologies in Medical Genetics by C. Alexander Valencia,M. Ali Pervaiz,Ammar Husami,Yaping Qian,Kejian Zhang Pdf
This book introduces readers to Next Generation Sequencing applications in medical genetics. The authors discuss the direct application of next-generation sequencing to medicine, specifically, laboratory medicine or molecular diagnostics. The first part of the book contains chapters on sanger sequencing, NGS technologies, targeted-amplification and capture, and exome sequencing. The second part of the book focuses on genetic disorders diagnoses by NGS, prenatal diagnosis, muscular dystrophies, mitochondrial disorders diagnosis, and challenges in molecular diagnosis. Recent developments and potential future trends in NGS sequencing applications are highlighted, as well.
Computational Methods for Next Generation Sequencing Data Analysis by Ion Mandoiu,Alexander Zelikovsky Pdf
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.
Genome-Scale Algorithm Design by Veli Mäkinen,Djamal Belazzougui,Fabio Cunial,Alexandru I. Tomescu Pdf
Provides an integrated picture of the latest developments in algorithmic techniques, with numerous worked examples, algorithm visualisations and exercises.
New High Throughput Technologies for DNA Sequencing and Genomics by Keith R. Mitchelson Pdf
Since the independent invention of DNA sequencing by Sanger and by Gilbert 30 years ago, it has grown from a small scale technique capable of reading several kilobase-pair of sequence per day into today's multibillion dollar industry. This growth has spurred the development of new sequencing technologies that do not involve either electrophoresis or Sanger sequencing chemistries. Sequencing by Synthesis (SBS) involves multiple parallel micro-sequencing addition events occurring on a surface, where data from each round is detected by imaging. New High Throughput Technologies for DNA Sequencing and Genomics is the second volume in the Perspectives in Bioanalysis series, which looks at the electroanalytical chemistry of nucleic acids and proteins, development of electrochemical sensors and their application in biomedicine and in the new fields of genomics and proteomics. The authors have expertly formatted the information for a wide variety of readers, including new developments that will inspire students and young scientists to create new tools for science and medicine in the 21st century. Reviews of complementary developments in Sanger and SBS sequencing chemistries, capillary electrophoresis and microdevice integration, MS sequencing and applications set the framework for the book. * 'Hot Topic' with DNA sequencing continuing as a major research activity in many areas of life science and medicine. * Bringing together new developments in DNA sequencing technology * Reviewing issues relevant to the new applications used
Genomic Sequence Analysis for Exon Prediction Using Adaptive Signal Processing Algorithms by Md. Zia Ur Rahman,Srinivasareddy Putluri Pdf
This book addresses the issue of improving the accuracy in exon prediction in DNA sequences using various adaptive techniques based on different performance measures that are crucial in disease diagnosis and therapy. First, the authors present an overview of genomics engineering, structure of DNA sequence and its building blocks, genetic information flow in a cell, gene prediction along with its significance, and various types of gene prediction methods, followed by a review of literature starting with the biological background of genomic sequence analysis. Next, they cover various theoretical considerations of adaptive filtering techniques used for DNA analysis, with an introduction to adaptive filtering, properties of adaptive algorithms, and the need for development of adaptive exon predictors (AEPs) and structure of AEP used for DNA analysis. Then, they extend the approach of least mean squares (LMS) algorithm and its sign-based realizations with normalization factor for DNA analysis. They also present the normalized logarithmic-based realizations of least mean logarithmic squares (LMLS) and least logarithmic absolute difference (LLAD) adaptive algorithms that include normalized LMLS (NLMLS) algorithm, normalized LLAD (NLLAD) algorithm, and their signed variants. This book ends with an overview of the goals achieved and highlights the primary achievements using all proposed techniques. This book is intended to provide rigorous use of adaptive signal processing algorithms for genetic engineering, biomedical engineering, and bioinformatics and is useful for undergraduate and postgraduate students. This will also serve as a practical guide for Ph.D. students and researchers and will provide a number of research directions for further work. Features Presents an overview of genomics engineering, structure of DNA sequence and its building blocks, genetic information flow in a cell, gene prediction along with its significance, and various types of gene prediction methods Covers various theoretical considerations of adaptive filtering techniques used for DNA analysis, introduction to adaptive filtering, properties of adaptive algorithms, need for development of adaptive exon predictors (AEPs), and structure of AEP used for DNA analysis Extends the approach of LMS algorithm and its sign-based realizations with normalization factor for DNA analysis Presents the normalized logarithmic-based realizations of LMLS and LLAD adaptive algorithms that include normalized LMLS (NLMLS) algorithm, normalized LLAD (NLLAD) algorithm, and their signed variants Provides an overview of the goals achieved and highlights the primary achievements using all proposed techniques Dr. Md. Zia Ur Rahman is a professor in the Department of Electronics and Communication Engineering at Koneru Lakshmaiah Educational Foundation (K. L. University), Guntur, India. His current research interests include adaptive signal processing, biomedical signal processing, genetic engineering, medical imaging, array signal processing, medical telemetry, and nanophotonics. Dr. Srinivasareddy Putluri is currently a Software Engineer at Tata Consultancy Services Ltd., Hyderabad. He received his Ph.D. degree (Genomic Signal Processing using Adaptive Signal Processing algorithms) from the Department of Electronics and Communication Engineering at Koneru Lakshmaiah Educational Foundation (K. L. University), Guntur, India. His research interests include genomic signal processing and adaptive signal processing. He has published 15 research papers in various journals and proceedings. He is currently a reviewer of publishers like the IEEE Access and IGI.
Next-Generation Sequencing Data Analysis by Xinkun Wang Pdf
A Practical Guide to the Highly Dynamic Area of Massively Parallel SequencingThe development of genome and transcriptome sequencing technologies has led to a paradigm shift in life science research and disease diagnosis and prevention. Scientists are now able to see how human diseases and phenotypic changes are connected to DNA mutation, polymorphi
Computational Genomics with R by Altuna Akalin Pdf
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.