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 ...
Author: Mourad Elloumi
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.
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 ...
Author: Wing-Kin Sung
Publisher: CRC Press
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.
The algorithms I present address three such opportunities that exist today with next-generation sequencing.
Author: Andreas Sundquist
Publisher: Proquest, UMI Dissertation Publishing
Thirty years ago, Sanger first introduced the gel electrophoresis method for sequencing DNA. Since then, technology has improved to the point where we are adding over 10 billion bases to GenBank each year at a cost of less than 0.1 cents per base. Amazingly, we are now entering an era of even more dramatic sequencing growth thanks to next-generation technologies that will completely dwarf all previous efforts. Although the cost and speed of sequencing will improve by orders of magnitude, the characteristic short read length of such technologies creates new challenges in effectively using the data. In this dissertation, I describe three significant algorithmic contributions I have made for next-generation sequencing: (1) whole-genome short-read sequencing and assembly, (2) bacterial flora-typing using targeted short-read sequencing, and (3) ancestry inference using dense SNP arrays. First, I present SHRAP (SHort Read Assembly Protocol), a sequencing protocol and assembly methodology that uses short-read technologies to decipher complex, mammalian-sized genomes. Our sequencing protocol is based on a variation of hierarchical clone-based sequencing that is optimized for high-throughput implementation using current technologies. We assemble the genome through a series of algorithms that first determines clone ordering in-silico, then performs error correction, and finally assembles localized sets of reads in three stages of hierarchically larger regions. By benchmarking our method on large simulations of the human genome, I demonstrate that it is possible to perform fast and truly inexpensive de novo sequencing of mammalian genomes. Next, I describe the genomic study of microbial communities using next-generation sequencing. I present a methodology for phylogenetic classification based on short, 16S rDNA gene sequence reads and apply the technique to reads obtained via high-throughput Pyrosequencing. I then examine our ability to classify reads at different levels in the phylogeny and discuss limitations of the technique and the effects of read-length and targeting specific 16S variable regions using simulation. Finally, I present HAPAA (HMM-based Analysis of Polymorphisms in Admixed Ancestries), a methodology for inferring the ancestry of chromosomal blocks using dense SNP arrays. I describe how our method improves upon previous techniques by modeling the long-range patterns of haplotypic variation seen in populations due to linkage disequilibrium. Finally, to study the effect of genetic divergence between populations on ancestry inference methods, I will present a testing methodology we devised that constructs synthetic populations and tests on individuals with varied genetic histories. As DNA sequencing technology evolves, it will continue to open up opportunities for new computational approaches for understanding our genetics. The algorithms I present address three such opportunities that exist today with next-generation sequencing.
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.
Author: Ali Masoudi-Nejad
Publisher: Springer Science & Business Media
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.
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 ...
Author: Ion Mandoiu
Publisher: John Wiley & Sons
Aiming to foster future collaborations between researchers in algorithms, bioinformatics, and molecular biology, this book serves as an up-to-date survey of the most important recent developments and computational challenges in various application areas of next-generation sequencing technologies. Offering helpful insight from renowned experts, the book covers topics such as NGS error correction, road mapping, variant detection and genotyping, characterization of structural variants with NGS, genome-assisted transcriptome reconstruction, small RNA analysis, and much more.
This journey to conquer cancer is more optimistic now with the unfolding of the cancer genome. This book focuses on the application of various NGS in the frontier cancer genome research.
Author: Wei Wu
Publisher: Springer Science & Business Media
This volume provides an interdisciplinary perspective of applying Next Generation Sequencing (NGS) technology to cancer research. It aims to systematically introduce the concept of NGS, a variety of NGS platforms and their practical implications in cancer biology.This unique and comprehensive text will integrate the unprecedented NGS technology into various cancer research projects as opposed to most books which offer a detailed description of the technology. This volume will present true experimental results with concrete data processing pipelines, discuss the bottleneck of each platform for real project in cancer research. In additional, single cancer cell sequencing as the proof of concept will be introduced in this book, along with cutting-edge information provided will help the intended audience to develop a comprehensive understanding of the NGS technology and practical whole genome sequencing data analysis and rapidly translate into their own research, specifically in the field of cancer biology.
There is a huge gap in this field for a book that gives practical guidance on these complex methods. This book does just that.
Author: Xinkun Wang
Publisher: CRC Press
Recent advances in genomics technology has dramatically reduced the cost of genome sequencing. Huge amounts of data is generated by next generation sequencing (NGS) technologies, posing substantial data analysis problems for many life scientists. There is a huge gap in this field for a book that gives practical guidance on these complex methods. This book does just that. It provides practical NGS data analysis techniques for an applied audience with guidance on software and algorithms. It includes lots of examples using real data to illustrate all the techniques. These features and more make it quite possibley the first pratical book on NGS data analysis with practical application.
This book provides over 2,000 Exam Prep questions and answers to accompany the text Next-Generation Sequencing Data Analysis Items include highly probable exam items: Data Link Layer, ReadyBoost, Service provider, Seeks, Datapath, ...
This book provides a thorough introduction to the necessary informatics methods and tools for operating NGS instruments and analyzing NGS data"--
Author: Stuart M. Brown
"Next-generation DNA sequencing (NGS) technology has revolutionized biomedical research, making complete genome sequencing an affordable and frequently used tool for a wide variety of research applications. This book provides a thorough introduction to the necessary informatics methods and tools for operating NGS instruments and analyzing NGS data"--
Table 19.1 Contingency Table of Fisher's Exact Test in VarScan2 Reference
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as follows: first, we will introduce various types of SNA detection algorithms; second, ...
Author: Somnath Datta
Next Generation Sequencing (NGS) is the latest high throughput technology to revolutionize genomic research. NGS generates massive genomic datasets that play a key role in the big data phenomenon that surrounds us today. To extract signals from high-dimensional NGS data and make valid statistical inferences and predictions, novel data analytic and statistical techniques are needed. This book contains 20 chapters written by prominent statisticians working with NGS data. The topics range from basic preprocessing and analysis with NGS data to more complex genomic applications such as copy number variation and isoform expression detection. Research statisticians who want to learn about this growing and exciting area will find this book useful. In addition, many chapters from this book could be included in graduate-level classes in statistical bioinformatics for training future biostatisticians who will be expected to deal with genomic data in basic biomedical research, genomic clinical trials and personalized medicine. About the editors: Somnath Datta is Professor and Vice Chair of Bioinformatics and Biostatistics at the University of Louisville. He is Fellow of the American Statistical Association, Fellow of the Institute of Mathematical Statistics and Elected Member of the International Statistical Institute. He has contributed to numerous research areas in Statistics, Biostatistics and Bioinformatics. Dan Nettleton is Professor and Laurence H. Baker Endowed Chair of Biological Statistics in the Department of Statistics at Iowa State University. He is Fellow of the American Statistical Association and has published research on a variety of topics in statistics, biology and bioinformatics.
The need to annotate and analyze the vast quantities of sequence data produced
by such next generation sequencing technologies as RAs poses another
challenge . Integrating these data with other existing genome annotations in
Author: Sun Kim
Publisher: Artech House Publishers
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.
This edited volume is devoted to Big Data Analysis from a Machine Learning standpoint as presented by some of the most eminent researchers in this area.
Author: Nathalie Japkowicz
This edited volume is devoted to Big Data Analysis from a Machine Learning standpoint as presented by some of the most eminent researchers in this area. It demonstrates that Big Data Analysis opens up new research problems which were either never considered before, or were only considered within a limited range. In addition to providing methodological discussions on the principles of mining Big Data and the difference between traditional statistical data analysis and newer computing frameworks, this book presents recently developed algorithms affecting such areas as business, financial forecasting, human mobility, the Internet of Things, information networks, bioinformatics, medical systems and life science. It explores, through a number of specific examples, how the study of Big Data Analysis has evolved and how it has started and will most likely continue to affect society. While the benefits brought upon by Big Data Analysis are underlined, the book also discusses some of the warnings that have been issued concerning the potential dangers of Big Data Analysis along with its pitfalls and challenges.
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 ...
Author: Sara El-Metwally
Publisher: Springer Science & Business
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.
This book constitutes the refereed proceedings of the First International Conference, AlCoB 2014, held in July 2014 in Tarragona, Spain. The 20 revised full papers were carefully reviewed and selected from 39 submissions.
Author: Adrian-Horia Dediu
This book constitutes the refereed proceedings of the First International Conference, AlCoB 2014, held in July 2014 in Tarragona, Spain. The 20 revised full papers were carefully reviewed and selected from 39 submissions. The scope of AlCoB includes topics of either theoretical or applied interest, namely: exact sequence analysis, approximate sequence analysis, pairwise sequence alignment, multiple sequence alignment, sequence assembly, genome rearrangement, regulatory motif finding, phylogeny reconstruction, phylogeny comparison, structure prediction, proteomics: molecular pathways, interaction networks, transcriptomics: splicing variants, isoform inference and quantification, differential analysis, next-generation sequencing: population genomics, metagenomics, metatranscriptomics, microbiome analysis, systems biology.
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Studies of human genetic variation reveal critical information about genetic and complex diseases such as cancer, diabetes and heart disease, ultimately leading towards improvements in health and quality of life.
Author: Soyeon Ahn (Ph. D.)
Studies of human genetic variation reveal critical information about genetic and complex diseases such as cancer, diabetes and heart disease, ultimately leading towards improvements in health and quality of life. Moreover, understanding genetic variations in viral population is of utmost importance to virologists and helps in search for vaccines. Next-generation sequencing technology is capable of acquiring massive amounts of data that can provide insight into the structure of diverse sets of genomic sequences. However, reconstructing heterogeneous sequences is computationally challenging due to the large dimension of the problem and limitations of the sequencing technology.This dissertation is focused on algorithms and analysis for two problems in which we seek to characterize genetic variations: (1) haplotype reconstruction for a single individual, so-called single individual haplotyping (SIH) or haplotype assembly problem, and (2) reconstruction of viral population, the so-called quasispecies reconstruction (QSR) problem. For the SIH problem, we have developed a method that relies on a probabilistic model of the data and employs the sequential Monte Carlo (SMC) algorithm to jointly determine type of variation (i.e., perform genotype calling) and assemble haplotypes. For the QSR problem, we have developed two algorithms. The first algorithm combines agglomerative hierarchical clustering and Bayesian inference to reconstruct quasispecies characterized by low diversity. The second algorithm utilizes tensor factorization framework with successive data removal to reconstruct quasispecies characterized by highly uneven frequencies of its components. Both algorithms outperform existing methods in both benchmarking tests and real data.
The articles collected in this book provides a glance into the rich emerging area of repeatome research, addressing some of its pressing challenges.
Author: Marco Pellegrini
Publisher: Frontiers Media SA
Repetitive structures in biological sequences are emerging as an active focus of research and the unifying concept of "repeatome" (the ensemble of knowledge associated with repeating structures in genomic/proteomic sequences) has been recently proposed in order to highlight several converging trends. One main trend is the ongoing discovery that genomic repetitions are linked to many biological significant events and functions. Diseases (e.g. Huntington's disease) have been causally linked with abnormal expansion of certain repeating sequences in the human genome. Deletions or multiple copy duplications of genes (Copy Number Variations) are important in the aetiology of cancer, Alzheimer, and Parkinson diseases. A second converging trend has been the emergence of many different models and algorithms for detecting non-obvious repeating patterns in strings with applications to in genomic data. Borrowing methodologies from combinatorial pattern, matching, string algorithms, data structures, data mining and machine learning these new approaches break the limitations of the current approaches and offer a new way to design better trans-disciplinary research. The articles collected in this book provides a glance into the rich emerging area of repeatome research, addressing some of its pressing challenges. We believe that these contributions are valuable resources for repeatome research and will stimulate further research from bioinformatic, statistical, and biological points of view.